code: 
  
  
  
  
  
  
  """simple docstring"""
  
  from typing import List, Optional, Union
  
  import numpy as np
  
  from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
  from ...feature_extraction_utils import BatchFeature
  from ...utils import PaddingStrategy, TensorType, logging
  
  
  lowercase__      =				logging.get_logger(__name__)
  
  
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Union[str, Any]      =							["""input_values""", """attention_mask"""]
  
  
  
  
  
  		def __init__(     self							:			int		,    a_							:			int=1		,    a_							:			Optional[Any]=1_60_00		,    a_							:			str=0.0		,    a_							:			List[Any]=False		,    a_							:			Dict=True		,    **a_							:			List[str]		,    ):
  				super().__init__(feature_size=a_		,    sampling_rate=a_		,    padding_value=a_		,    **a_			)
  				lowerCAmelCase_     :							List[str]             =    return_attention_mask
  				lowerCAmelCase_     :							List[str]             =    do_normalize
  
  
  
  
  
  		@staticmethod
  		def      lowerCamelCase (     a_							:			List[np.ndarray]		,    a_							:			List[np.ndarray]		,    a_							:			float = 0.0			):
  				if attention_mask is not None:
  						lowerCAmelCase_     :							Union[str, Any]             =    np.array(a_		,    np.intaa			)
  						lowerCAmelCase_     :							List[Any]             =    []
  
  						for vector, length in zip(a_		,    attention_mask.sum(-1			)			):
  								lowerCAmelCase_     :							int             =    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7			)
  								if length < normed_slice.shape[0]:
  										lowerCAmelCase_     :							Optional[Any]             =    padding_value
  
  								normed_input_values.append(a_			)
  				else:
  						lowerCAmelCase_     :							int             =    [(x - x.mean()) / np.sqrt(x.var() + 1e-7			) for x in input_values]
  
  				return normed_input_values
  
  
  
  
  
  		def __call__(     self							:			Tuple		,    a_							:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,    a_							:			Union[bool, str, PaddingStrategy] = False		,    a_							:			Optional[int] = None		,    a_							:			bool = False		,    a_							:			Optional[int] = None		,    a_							:			Optional[bool] = None		,    a_							:			Optional[Union[str, TensorType]] = None		,    a_							:			Optional[int] = None		,    **a_							:			Dict		,    ):
  
  				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_     :							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_     :							List[Any]             =    is_batched_numpy or (
  				    isinstance(a_		,    (list, tuple)			) and (isinstance(raw_speech[0]		,    (np.ndarray, tuple, list)			))
  				)
  
  				# always return batch
  				if not is_batched:
  						lowerCAmelCase_     :							Optional[Any]             =    [raw_speech]
  
  				# convert into correct format for padding
  				lowerCAmelCase_     :							Optional[int]             =    BatchFeature({"input_values": raw_speech}			)
  
  				lowerCAmelCase_     :							str             =    self.pad(
  				    a_		,    padding=a_		,    max_length=a_		,    truncation=a_		,    pad_to_multiple_of=a_		,    return_attention_mask=a_		,    )
  
  				# convert input values to correct format
  				lowerCAmelCase_     :							Union[str, Any]             =    padded_inputs["input_values"]
  				if not isinstance(input_values[0]		,    np.ndarray			):
  						lowerCAmelCase_     :							Any             =    [np.asarray(a_		,    dtype=np.floataa			) for array in input_values]
  				elif (
  				    not isinstance(a_		,    np.ndarray			)
  				    and isinstance(input_values[0]		,    np.ndarray			)
  				    and input_values[0].dtype is np.dtype(np.floataa			)
  				):
  						lowerCAmelCase_     :							List[Any]             =    [array.astype(np.floataa			) for array in input_values]
  				elif isinstance(a_		,    np.ndarray			) and input_values.dtype is np.dtype(np.floataa			):
  						lowerCAmelCase_     :							Optional[Any]             =    input_values.astype(np.floataa			)
  
  				# convert attention_mask to correct format
  				lowerCAmelCase_     :							int             =    padded_inputs.get("attention_mask"			)
  				if attention_mask is not None:
  						lowerCAmelCase_     :							Optional[int]             =    [np.asarray(a_		,    dtype=np.intaa			) for array in attention_mask]
  
  				# zero-mean and unit-variance normalization
  				if self.do_normalize:
  						lowerCAmelCase_     :							Optional[Any]             =    (
  						    attention_mask
  						    if self._get_padding_strategies(a_		,    max_length=a_			) is not PaddingStrategy.DO_NOT_PAD
  						    else None
  						)
  						lowerCAmelCase_     :							Tuple             =    self.zero_mean_unit_var_norm(
  						    padded_inputs["input_values"]		,    attention_mask=a_		,    padding_value=self.padding_value			)
  
  				if return_tensors is not None:
  						lowerCAmelCase_     :							Union[str, Any]             =    padded_inputs.convert_to_tensors(a_			)
  
  				return padded_inputs
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
  
  from diffusers import (
      AutoencoderKL,
      DPMSolverMultistepScheduler,
      PNDMScheduler,
      StableDiffusionImageVariationPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  class 	__lowerCamelCase       (				A__				,							A__				,							A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Union[str, Any]      =							StableDiffusionImageVariationPipeline
  		a_			:       Tuple      =							IMAGE_VARIATION_PARAMS
  		a_			:       Optional[Any]      =							IMAGE_VARIATION_BATCH_PARAMS
  		a_			:       int      =							frozenset([]    )
  		# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  		a_			:       Optional[int]      =							frozenset([]    )
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Tuple			):
  				torch.manual_seed(0			)
  				lowerCAmelCase_     :							List[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		,    )
  				lowerCAmelCase_     :							int             =    PNDMScheduler(skip_prk_steps=a_			)
  				torch.manual_seed(0			)
  				lowerCAmelCase_     :							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			)
  				lowerCAmelCase_     :							Dict             =    CLIPVisionConfig(
  				    hidden_size=32		,    projection_dim=32		,    intermediate_size=37		,    layer_norm_eps=1e-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    image_size=32		,    patch_size=4		,    )
  				lowerCAmelCase_     :							List[Any]             =    CLIPVisionModelWithProjection(a_			)
  				lowerCAmelCase_     :							Union[str, Any]             =    CLIPImageProcessor(crop_size=32		,    size=32			)
  
  				lowerCAmelCase_     :							List[Any]             =    {
  				    "unet": unet,
  				    "scheduler": scheduler,
  				    "vae": vae,
  				    "image_encoder": image_encoder,
  				    "feature_extractor": feature_extractor,
  				    "safety_checker": None,
  				}
  				return components
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Tuple		,    a_							:			str=0			):
  				lowerCAmelCase_     :							List[Any]             =    floats_tensor((1, 3, 32, 32)		,    rng=random.Random(a_			)			)
  				lowerCAmelCase_     :							int             =    image.cpu().permute(0		,    2		,    3		,    1			)[0]
  				lowerCAmelCase_     :							List[str]             =    Image.fromarray(np.uinta(a_			)			).convert("RGB"			).resize((32, 32)			)
  				if str(a_			).startswith("mps"			):
  						lowerCAmelCase_     :							Optional[int]             =    torch.manual_seed(a_			)
  				else:
  						lowerCAmelCase_     :							Optional[Any]             =    torch.Generator(device=a_			).manual_seed(a_			)
  				lowerCAmelCase_     :							str             =    {
  				    "image": image,
  				    "generator": generator,
  				    "num_inference_steps": 2,
  				    "guidance_scale": 6.0,
  				    "output_type": "numpy",
  				}
  				return inputs
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							int             =    "cpu"  # ensure determinism for the device-dependent torch.Generator
  				lowerCAmelCase_     :							Dict             =    self.get_dummy_components()
  				lowerCAmelCase_     :							Union[str, Any]             =    StableDiffusionImageVariationPipeline(**a_			)
  				lowerCAmelCase_     :							int             =    sd_pipe.to(a_			)
  				sd_pipe.set_progress_bar_config(disable=a_			)
  
  				lowerCAmelCase_     :							Tuple             =    self.get_dummy_inputs(a_			)
  				lowerCAmelCase_     :							List[str]             =    sd_pipe(**a_			).images
  				lowerCAmelCase_     :							List[Any]             =    image[0, -3:, -3:, -1]
  
  				assert image.shape == (1, 64, 64, 3)
  				lowerCAmelCase_     :							List[str]             =    np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_     :							int             =    "cpu"  # ensure determinism for the device-dependent torch.Generator
  				lowerCAmelCase_     :							Optional[Any]             =    self.get_dummy_components()
  				lowerCAmelCase_     :							Tuple             =    StableDiffusionImageVariationPipeline(**a_			)
  				lowerCAmelCase_     :							List[str]             =    sd_pipe.to(a_			)
  				sd_pipe.set_progress_bar_config(disable=a_			)
  
  				lowerCAmelCase_     :							List[str]             =    self.get_dummy_inputs(a_			)
  				lowerCAmelCase_     :							Optional[Any]             =    2 * [inputs["image"]]
  				lowerCAmelCase_     :							Optional[Any]             =    sd_pipe(**a_			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    output.images
  
  				lowerCAmelCase_     :							Optional[Any]             =    image[-1, -3:, -3:, -1]
  
  				assert image.shape == (2, 64, 64, 3)
  				lowerCAmelCase_     :							int             =    np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				super().test_inference_batch_single_identical(expected_max_diff=3e-3			)
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				super().tearDown()
  				gc.collect()
  				torch.cuda.empty_cache()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			Optional[Any]		,    a_							:			List[str]="cpu"		,    a_							:			Optional[Any]=torch.floataa		,    a_							:			Optional[int]=0			):
  				lowerCAmelCase_     :							str             =    torch.Generator(device=a_			).manual_seed(a_			)
  				lowerCAmelCase_     :							str             =    load_image(
  				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  				    "/stable_diffusion_imgvar/input_image_vermeer.png"			)
  				lowerCAmelCase_     :							Optional[Any]             =    np.random.RandomState(a_			).standard_normal((1, 4, 64, 64)			)
  				lowerCAmelCase_     :							List[Any]             =    torch.from_numpy(a_			).to(device=a_		,    dtype=a_			)
  				lowerCAmelCase_     :							Any             =    {
  				    "image": init_image,
  				    "latents": latents,
  				    "generator": generator,
  				    "num_inference_steps": 3,
  				    "guidance_scale": 7.5,
  				    "output_type": "numpy",
  				}
  				return inputs
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							Tuple             =    StableDiffusionImageVariationPipeline.from_pretrained(
  				    "lambdalabs/sd-image-variations-diffusers"		,    safety_checker=a_			)
  				lowerCAmelCase_     :							int             =    sd_pipe.to(a_			)
  				sd_pipe.set_progress_bar_config(disable=a_			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    self.get_inputs(a_			)
  				lowerCAmelCase_     :							Union[str, Any]             =    sd_pipe(**a_			).images
  				lowerCAmelCase_     :							Optional[int]             =    image[0, -3:, -3:, -1].flatten()
  
  				assert image.shape == (1, 5_12, 5_12, 3)
  				lowerCAmelCase_     :							Optional[Any]             =    np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]			)
  				assert np.abs(image_slice - expected_slice			).max() < 6e-3
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict			):
  				lowerCAmelCase_     :							List[str]             =    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_     :							str             =    latents.detach().cpu().numpy()
  								assert latents.shape == (1, 4, 64, 64)
  								lowerCAmelCase_     :							Dict             =    latents[0, -3:, -3:, -1]
  								lowerCAmelCase_     :							List[Any]             =    np.array(
  								    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]			)
  
  								assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
  						elif step == 2:
  								lowerCAmelCase_     :							Dict             =    latents.detach().cpu().numpy()
  								assert latents.shape == (1, 4, 64, 64)
  								lowerCAmelCase_     :							Tuple             =    latents[0, -3:, -3:, -1]
  								lowerCAmelCase_     :							Dict             =    np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]			)
  
  								assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
  
  				lowerCAmelCase_     :							Union[str, Any]             =    False
  
  				lowerCAmelCase_     :							str             =    StableDiffusionImageVariationPipeline.from_pretrained(
  				    "fusing/sd-image-variations-diffusers"		,    safety_checker=a_		,    torch_dtype=torch.floataa		,    )
  				pipe.to(a_			)
  				pipe.set_progress_bar_config(disable=a_			)
  				pipe.enable_attention_slicing()
  
  				lowerCAmelCase_     :							Tuple             =    self.get_inputs(a_		,    dtype=torch.floataa			)
  				pipe(**a_		,    callback=a_		,    callback_steps=1			)
  				assert callback_fn.has_been_called
  				assert number_of_steps == inputs["num_inference_steps"]
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				torch.cuda.empty_cache()
  				torch.cuda.reset_max_memory_allocated()
  				torch.cuda.reset_peak_memory_stats()
  
  				lowerCAmelCase_     :							Tuple             =    "fusing/sd-image-variations-diffusers"
  				lowerCAmelCase_     :							str             =    StableDiffusionImageVariationPipeline.from_pretrained(
  				    a_		,    safety_checker=a_		,    torch_dtype=torch.floataa			)
  				lowerCAmelCase_     :							int             =    pipe.to(a_			)
  				pipe.set_progress_bar_config(disable=a_			)
  				pipe.enable_attention_slicing(1			)
  				pipe.enable_sequential_cpu_offload()
  
  				lowerCAmelCase_     :							List[str]             =    self.get_inputs(a_		,    dtype=torch.floataa			)
  				lowerCAmelCase_     :							Any             =    pipe(**a_			)
  
  				lowerCAmelCase_     :							List[Any]             =    torch.cuda.max_memory_allocated()
  				# make sure that less than 2.6 GB is allocated
  				assert mem_bytes < 2.6 * 10**9
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Tuple			):
  				super().tearDown()
  				gc.collect()
  				torch.cuda.empty_cache()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			int		,    a_							:			int="cpu"		,    a_							:			str=torch.floataa		,    a_							:			Optional[Any]=0			):
  				lowerCAmelCase_     :							str             =    torch.Generator(device=a_			).manual_seed(a_			)
  				lowerCAmelCase_     :							int             =    load_image(
  				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  				    "/stable_diffusion_imgvar/input_image_vermeer.png"			)
  				lowerCAmelCase_     :							Optional[Any]             =    np.random.RandomState(a_			).standard_normal((1, 4, 64, 64)			)
  				lowerCAmelCase_     :							Optional[int]             =    torch.from_numpy(a_			).to(device=a_		,    dtype=a_			)
  				lowerCAmelCase_     :							Union[str, Any]             =    {
  				    "image": init_image,
  				    "latents": latents,
  				    "generator": generator,
  				    "num_inference_steps": 50,
  				    "guidance_scale": 7.5,
  				    "output_type": "numpy",
  				}
  				return inputs
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]			):
  				lowerCAmelCase_     :							Any             =    StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"			)
  				sd_pipe.to(a_			)
  				sd_pipe.set_progress_bar_config(disable=a_			)
  
  				lowerCAmelCase_     :							List[str]             =    self.get_inputs(a_			)
  				lowerCAmelCase_     :							int             =    sd_pipe(**a_			).images[0]
  
  				lowerCAmelCase_     :							Optional[Any]             =    load_numpy(
  				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  				    "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"			)
  				lowerCAmelCase_     :							List[Any]             =    np.abs(expected_image - image			).max()
  				assert max_diff < 1e-3
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							List[Any]             =    StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"			)
  				lowerCAmelCase_     :							Tuple             =    DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config			)
  				sd_pipe.to(a_			)
  				sd_pipe.set_progress_bar_config(disable=a_			)
  
  				lowerCAmelCase_     :							Optional[int]             =    self.get_inputs(a_			)
  				lowerCAmelCase_     :							Tuple             =    25
  				lowerCAmelCase_     :							int             =    sd_pipe(**a_			).images[0]
  
  				lowerCAmelCase_     :							Union[str, Any]             =    load_numpy(
  				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  				    "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"			)
  				lowerCAmelCase_     :							Optional[Any]             =    np.abs(expected_image - image			).max()
  				assert max_diff < 1e-3
  