




"""simple docstring"""

# This model implementation is heavily based on:

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,
    StableDiffusionControlNetInpaintPipeline,
    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 (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class 			lowerCamelCase__    (  lowerCamelCase_				,  lowerCamelCase_				,  lowerCamelCase_				,  unittest.TestCase			):
			a__ :      Dict								=     StableDiffusionControlNetInpaintPipeline
			a__ :      List[Any]								=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a__ :      List[Any]								=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			a__ :      List[Any]								=     frozenset({"""control_image"""}			)  # skip `image` and `mask` for now, only test for control_image
			a__ :      Optional[int]								=     TEXT_TO_IMAGE_IMAGE_PARAMS


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					torch.manual_seed(0							)
					snake_case		:						int								=				UNetaDConditionModel(
					    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				, cross_attention_dim=32				, )
					torch.manual_seed(0							)
					snake_case		:						Union[str, Any]								=				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							)
					snake_case		:						Optional[Any]								=				DDIMScheduler(
					    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule="scaled_linear"				, clip_sample=SCREAMING_SNAKE_CASE				, set_alpha_to_one=SCREAMING_SNAKE_CASE				, )
					torch.manual_seed(0							)
					snake_case		:						Optional[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							)
					snake_case		:						Optional[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				, )
					snake_case		:						int								=				CLIPTextModel(SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

					snake_case		:						List[Any]								=				{
					    "unet": unet,
					    "controlnet": controlnet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
							snake_case		:						Optional[int]								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
					else:
							snake_case		:						str								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)

					snake_case		:						Dict								=				2
					snake_case		:						List[Any]								=				randn_tensor(
					    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=SCREAMING_SNAKE_CASE				, device=torch.device(SCREAMING_SNAKE_CASE							)				, )
					snake_case		:						Optional[int]								=				floats_tensor((1, 3, 32, 32)				, rng=random.Random(SCREAMING_SNAKE_CASE							)							).to(SCREAMING_SNAKE_CASE							)
					snake_case		:						str								=				init_image.cpu().permute(0				, 2				, 3				, 1							)[0]

					snake_case		:						Union[str, Any]								=				Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE							)							).convert("RGB"							).resize((64, 64)							)
					snake_case		:						Union[str, Any]								=				Image.fromarray(np.uinta(init_image + 4							)							).convert("RGB"							).resize((64, 64)							)

					snake_case		:						Tuple								=				{
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "output_type": "numpy",
					    "image": image,
					    "mask_image": mask_image,
					    "control_image": control_image,
					}

					return inputs


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					self._test_inference_batch_single_identical(expected_max_diff=2E-3							)







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Any								=     StableDiffusionControlNetInpaintPipeline
			a__ :      Optional[int]								=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a__ :      int								=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			a__ :      List[str]								=     frozenset([]			)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					torch.manual_seed(0							)
					snake_case		:						Optional[int]								=				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							)
					snake_case		:						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							)
					snake_case		:						Optional[int]								=				DDIMScheduler(
					    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule="scaled_linear"				, clip_sample=SCREAMING_SNAKE_CASE				, set_alpha_to_one=SCREAMING_SNAKE_CASE				, )
					torch.manual_seed(0							)
					snake_case		:						Dict								=				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							)
					snake_case		:						int								=				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				, )
					snake_case		:						Any								=				CLIPTextModel(SCREAMING_SNAKE_CASE							)
					snake_case		:						str								=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

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







class 			lowerCamelCase__    (  lowerCamelCase_				,  lowerCamelCase_				,  unittest.TestCase			):
			a__ :      str								=     StableDiffusionControlNetInpaintPipeline
			a__ :      List[Any]								=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a__ :      Any								=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					torch.manual_seed(0							)
					snake_case		:						Any								=				UNetaDConditionModel(
					    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				, cross_attention_dim=32				, )
					torch.manual_seed(0							)

					def init_weights(SCREAMING_SNAKE_CASE							):
							if isinstance(SCREAMING_SNAKE_CASE				, torch.nn.Convad							):
									torch.nn.init.normal(m.weight							)
									m.bias.data.fill_(1.0							)

					snake_case		:						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(SCREAMING_SNAKE_CASE							)

					torch.manual_seed(0							)
					snake_case		:						Any								=				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(SCREAMING_SNAKE_CASE							)

					torch.manual_seed(0							)
					snake_case		:						str								=				DDIMScheduler(
					    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule="scaled_linear"				, clip_sample=SCREAMING_SNAKE_CASE				, set_alpha_to_one=SCREAMING_SNAKE_CASE				, )
					torch.manual_seed(0							)
					snake_case		:						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							)
					snake_case		:						Optional[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				, )
					snake_case		:						List[Any]								=				CLIPTextModel(SCREAMING_SNAKE_CASE							)
					snake_case		:						int								=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

					snake_case		:						Tuple								=				MultiControlNetModel([controlneta, controlneta]							)

					snake_case		:						List[str]								=				{
					    "unet": unet,
					    "controlnet": controlnet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
							snake_case		:						int								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
					else:
							snake_case		:						List[str]								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)

					snake_case		:						Optional[Any]								=				2

					snake_case		:						Optional[Any]								=				[
					    randn_tensor(
					        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=SCREAMING_SNAKE_CASE				, device=torch.device(SCREAMING_SNAKE_CASE							)				, ),
					    randn_tensor(
					        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=SCREAMING_SNAKE_CASE				, device=torch.device(SCREAMING_SNAKE_CASE							)				, ),
					]
					snake_case		:						Optional[int]								=				floats_tensor((1, 3, 32, 32)				, rng=random.Random(SCREAMING_SNAKE_CASE							)							).to(SCREAMING_SNAKE_CASE							)
					snake_case		:						List[Any]								=				init_image.cpu().permute(0				, 2				, 3				, 1							)[0]

					snake_case		:						List[Any]								=				Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE							)							).convert("RGB"							).resize((64, 64)							)
					snake_case		:						str								=				Image.fromarray(np.uinta(init_image + 4							)							).convert("RGB"							).resize((64, 64)							)

					snake_case		:						str								=				{
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "output_type": "numpy",
					    "image": image,
					    "mask_image": mask_image,
					    "control_image": control_image,
					}

					return inputs


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				self.get_dummy_components()
					snake_case		:						int								=				self.pipeline_class(**SCREAMING_SNAKE_CASE							)
					pipe.to(SCREAMING_SNAKE_CASE							)

					snake_case		:						List[Any]								=				10.0
					snake_case		:						str								=				4

					snake_case		:						Tuple								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				steps
					snake_case		:						Any								=				scale
					snake_case		:						Union[str, Any]								=				pipe(**SCREAMING_SNAKE_CASE							)[0]

					snake_case		:						str								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[Any]								=				steps
					snake_case		:						Optional[Any]								=				scale
					snake_case		:						Dict								=				pipe(**SCREAMING_SNAKE_CASE				, control_guidance_start=0.1				, control_guidance_end=0.2							)[0]

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

					snake_case		:						Union[str, Any]								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
					snake_case		:						Dict								=				steps
					snake_case		:						Optional[int]								=				scale
					snake_case		:						Union[str, Any]								=				pipe(**SCREAMING_SNAKE_CASE				, 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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					self._test_inference_batch_single_identical(expected_max_diff=2E-3							)



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







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







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


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Union[str, Any]								=				ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"							)

					snake_case		:						List[Any]								=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
					    "runwayml/stable-diffusion-inpainting"				, safety_checker=SCREAMING_SNAKE_CASE				, controlnet=SCREAMING_SNAKE_CASE							)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)

					snake_case		:						Optional[int]								=				torch.Generator(device="cpu"							).manual_seed(0							)
					snake_case		:						int								=				load_image(
					    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"							).resize((512, 512)							)

					snake_case		:						int								=				load_image(
					    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
					    "/stable_diffusion_inpaint/input_bench_mask.png"							).resize((512, 512)							)

					snake_case		:						Tuple								=				"pitch black hole"

					snake_case		:						Optional[int]								=				load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"							).resize((512, 512)							)

					snake_case		:						Optional[Any]								=				pipe(
					    SCREAMING_SNAKE_CASE				, image=SCREAMING_SNAKE_CASE				, mask_image=SCREAMING_SNAKE_CASE				, control_image=SCREAMING_SNAKE_CASE				, generator=SCREAMING_SNAKE_CASE				, output_type="np"				, num_inference_steps=3				, )

					snake_case		:						Optional[Any]								=				output.images[0]

					assert image.shape == (512, 512, 3)

					snake_case		:						Union[str, Any]								=				load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"							)

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



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"							)

					snake_case		:						Tuple								=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				, safety_checker=SCREAMING_SNAKE_CASE				, controlnet=SCREAMING_SNAKE_CASE							)
					snake_case		:						Tuple								=				DDIMScheduler.from_config(pipe.scheduler.config							)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[str]								=				torch.Generator(device="cpu"							).manual_seed(33							)

					snake_case		:						Any								=				load_image(
					    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"							)
					snake_case		:						List[str]								=				init_image.resize((512, 512)							)

					snake_case		:						Optional[Any]								=				load_image(
					    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"							)
					snake_case		:						Any								=				mask_image.resize((512, 512)							)

					snake_case		:						int								=				"a handsome man with ray-ban sunglasses"

					def make_inpaint_condition(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
							snake_case		:						Optional[int]								=				np.array(image.convert("RGB"							)							).astype(np.floataa							) / 2_55.0
							snake_case		:						int								=				np.array(image_mask.convert("L"							)							).astype(np.floataa							) / 2_55.0

							assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
							snake_case		:						Union[str, Any]								=				-1.0  # set as masked pixel
							snake_case		:						List[str]								=				np.expand_dims(SCREAMING_SNAKE_CASE				, 0							).transpose(0				, 3				, 1				, 2							)
							snake_case		:						Optional[Any]								=				torch.from_numpy(SCREAMING_SNAKE_CASE							)
							return image

					snake_case		:						Any								=				make_inpaint_condition(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)

					snake_case		:						List[Any]								=				pipe(
					    SCREAMING_SNAKE_CASE				, image=SCREAMING_SNAKE_CASE				, mask_image=SCREAMING_SNAKE_CASE				, control_image=SCREAMING_SNAKE_CASE				, guidance_scale=9.0				, eta=1.0				, generator=SCREAMING_SNAKE_CASE				, num_inference_steps=20				, output_type="np"				, )
					snake_case		:						Optional[Any]								=				output.images[0]

					assert image.shape == (512, 512, 3)

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

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







"""simple docstring"""

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class 			lowerCamelCase__    (  unittest.TestCase			):


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				1
					snake_case		:						Dict								=				3
					snake_case		:						int								=				8
					snake_case		:						int								=				8

					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)

					return sample


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(image[0]				, PIL.Image.Image							):
							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					return image


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Any								=				self.dummy_sample
					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Optional[Any]								=				image_processor.postprocess(
							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Any								=				(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)

							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Tuple								=				(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 255).round()
									assert (
									    np.abs(in_np - out_np							).max() < 1E-6
									), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[str]								=				self.dummy_sample
					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
					assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Union[str, Any]								=				self.dummy_sample
					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
					list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
					assert np.abs(out_np_ad - out_np_list							).max() < 1E-6

