



"""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__	(  UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       unittest.TestCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Tuple     	=   StableDiffusionControlNetInpaintPipeline
	_SCREAMING_SNAKE_CASE  :		int     	=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_SCREAMING_SNAKE_CASE  :		str     	=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_SCREAMING_SNAKE_CASE  :		List[Any]     	=   frozenset({'''control_image'''}   )  # skip `image` and `mask` for now, only test for control_image
	_SCREAMING_SNAKE_CASE  :		str     	=   TEXT_TO_IMAGE_IMAGE_PARAMS





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  List[str]:





					"""simple docstring"""


					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    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						)
					SCREAMING_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)    ,					)
					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    DDIMScheduler(
					    beta_start=0.0_0_0_8_5    ,					beta_end=0.0_1_2    ,					beta_schedule='scaled_linear'    ,					clip_sample=lowerCAmelCase__    ,					set_alpha_to_one=lowerCAmelCase__    ,					)
					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       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						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    CLIPTextConfig(
					    bos_token_id=0    ,					eos_token_id=2    ,					hidden_size=32    ,					intermediate_size=37    ,					layer_norm_eps=1E-0_5    ,					num_attention_heads=4    ,					num_hidden_layers=5    ,					pad_token_id=1    ,					vocab_size=1_000    ,					)
					SCREAMING_SNAKE_CASE	:       str 							=    CLIPTextModel(lowerCAmelCase__						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

					SCREAMING_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							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=0						)	->  str:





					"""simple docstring"""


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

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

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

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    {
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "output_type": "numpy",
					    "image": image,
					    "mask_image": mask_image,
					    "control_image": control_image,
					}

					return inputs





	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  List[str]:





					"""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							:					Tuple						)	->  str:





					"""simple docstring"""


					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)







	def  _lowerCAmelCase    (			self							:					Tuple						)	->  int:





					"""simple docstring"""


					self._test_inference_batch_single_identical(expected_max_diff=2E-3						)




class 						lowerCAmelCase__	(  UpperCamelCase__   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   StableDiffusionControlNetInpaintPipeline
	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   frozenset([]   )





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Optional[Any]:





					"""simple docstring"""


					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    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						)
					SCREAMING_SNAKE_CASE	:       str 							=    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						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    DDIMScheduler(
					    beta_start=0.0_0_0_8_5    ,					beta_end=0.0_1_2    ,					beta_schedule='scaled_linear'    ,					clip_sample=lowerCAmelCase__    ,					set_alpha_to_one=lowerCAmelCase__    ,					)
					torch.manual_seed(0						)
					SCREAMING_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						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    CLIPTextConfig(
					    bos_token_id=0    ,					eos_token_id=2    ,					hidden_size=32    ,					intermediate_size=37    ,					layer_norm_eps=1E-0_5    ,					num_attention_heads=4    ,					num_hidden_layers=5    ,					pad_token_id=1    ,					vocab_size=1_000    ,					)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    CLIPTextModel(lowerCAmelCase__						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

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




class 						lowerCAmelCase__	(  UpperCamelCase__       ,       UpperCamelCase__       ,       unittest.TestCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Tuple     	=   StableDiffusionControlNetInpaintPipeline
	_SCREAMING_SNAKE_CASE  :		Dict     	=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_SCREAMING_SNAKE_CASE  :		int     	=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS





	def  _lowerCAmelCase    (			self							:					str						)	->  Union[str, Any]:





					"""simple docstring"""


					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       str 							=    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							:					Optional[int]						):
									if isinstance(lowerCAmelCase__    ,					torch.nn.Convad						):
													torch.nn.init.normal(m.weight						)
													m.bias.data.fill_(1.0						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    ControlNetModel(
					    block_out_channels=(32, 64)    ,					layers_per_block=2    ,					in_channels=4    ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,					cross_attention_dim=32    ,					conditioning_embedding_out_channels=(16, 32)    ,					)
					controlneta.controlnet_down_blocks.apply(lowerCAmelCase__						)

					torch.manual_seed(0						)
					SCREAMING_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)    ,					)
					controlneta.controlnet_down_blocks.apply(lowerCAmelCase__						)

					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    DDIMScheduler(
					    beta_start=0.0_0_0_8_5    ,					beta_end=0.0_1_2    ,					beta_schedule='scaled_linear'    ,					clip_sample=lowerCAmelCase__    ,					set_alpha_to_one=lowerCAmelCase__    ,					)
					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       Optional[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						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    CLIPTextConfig(
					    bos_token_id=0    ,					eos_token_id=2    ,					hidden_size=32    ,					intermediate_size=37    ,					layer_norm_eps=1E-0_5    ,					num_attention_heads=4    ,					num_hidden_layers=5    ,					pad_token_id=1    ,					vocab_size=1_000    ,					)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    CLIPTextModel(lowerCAmelCase__						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    MultiControlNetModel([controlneta, controlneta]						)

					SCREAMING_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





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					Dict=0						)	->  str:





					"""simple docstring"""


					if str(lowerCAmelCase__						).startswith('mps'						):
									SCREAMING_SNAKE_CASE	:       List[str] 							=    torch.manual_seed(lowerCAmelCase__						)
					else:
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.Generator(device=lowerCAmelCase__						).manual_seed(lowerCAmelCase__						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    2

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

					SCREAMING_SNAKE_CASE	:       int 							=    Image.fromarray(np.uinta(lowerCAmelCase__						)						).convert('RGB'						).resize((64, 64)						)
					SCREAMING_SNAKE_CASE	:       str 							=    Image.fromarray(np.uinta(init_image + 4						)						).convert('RGB'						).resize((64, 64)						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    {
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "output_type": "numpy",
					    "image": image,
					    "mask_image": mask_image,
					    "control_image": control_image,
					}

					return inputs





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    self.get_dummy_components()
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.pipeline_class(**lowerCAmelCase__						)
					pipe.to(lowerCAmelCase__						)

					SCREAMING_SNAKE_CASE	:       int 							=    1_0.0
					SCREAMING_SNAKE_CASE	:       int 							=    4

					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_dummy_inputs(lowerCAmelCase__						)
					SCREAMING_SNAKE_CASE	:       int 							=    steps
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    scale
					SCREAMING_SNAKE_CASE	:       Tuple 							=    pipe(**lowerCAmelCase__						)[0]

					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_dummy_inputs(lowerCAmelCase__						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    steps
					SCREAMING_SNAKE_CASE	:       int 							=    scale
					SCREAMING_SNAKE_CASE	:       int 							=    pipe(**lowerCAmelCase__    ,					control_guidance_start=0.1    ,					control_guidance_end=0.2						)[0]

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

					SCREAMING_SNAKE_CASE	:       Any 							=    self.get_dummy_inputs(lowerCAmelCase__						)
					SCREAMING_SNAKE_CASE	:       int 							=    steps
					SCREAMING_SNAKE_CASE	:       Dict 							=    scale
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    pipe(**lowerCAmelCase__    ,					control_guidance_start=0.4    ,					control_guidance_end=[0.5, 0.8]						)[0]

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





	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Any:





					"""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							:					Dict						)	->  Dict:





					"""simple docstring"""


					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  int:





					"""simple docstring"""


					self._test_inference_batch_single_identical(expected_max_diff=2E-3						)







	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


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




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


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Dict:





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					int						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
					    'runwayml/stable-diffusion-inpainting'    ,					safety_checker=lowerCAmelCase__    ,					controlnet=lowerCAmelCase__						)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=lowerCAmelCase__						)

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

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_inpaint/input_bench_mask.png'						).resize((512, 512)						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    "pitch black hole"

					SCREAMING_SNAKE_CASE	:       Tuple 							=    load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'						).resize((512, 512)						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    pipe(
					    lowerCAmelCase__    ,					image=lowerCAmelCase__    ,					mask_image=lowerCAmelCase__    ,					control_image=lowerCAmelCase__    ,					generator=lowerCAmelCase__    ,					output_type='np'    ,					num_inference_steps=3    ,					)

					SCREAMING_SNAKE_CASE	:       Tuple 							=    output.images[0]

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

					SCREAMING_SNAKE_CASE	:       List[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							:					str						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'						)

					SCREAMING_SNAKE_CASE	:       Tuple 							=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
					    'runwayml/stable-diffusion-v1-5'    ,					safety_checker=lowerCAmelCase__    ,					controlnet=lowerCAmelCase__						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    DDIMScheduler.from_config(pipe.scheduler.config						)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=lowerCAmelCase__						)

					SCREAMING_SNAKE_CASE	:       int 							=    torch.Generator(device='cpu'						).manual_seed(33						)

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

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

					SCREAMING_SNAKE_CASE	:       str 							=    "a handsome man with ray-ban sunglasses"

					def make_inpaint_condition(_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					str						):
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    np.array(image.convert('RGB'						)						).astype(np.floataa						) / 255.0
									SCREAMING_SNAKE_CASE	:       str 							=    np.array(image_mask.convert('L'						)						).astype(np.floataa						) / 255.0

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

					SCREAMING_SNAKE_CASE	:       List[str] 							=    make_inpaint_condition(lowerCAmelCase__    ,					lowerCAmelCase__						)

					SCREAMING_SNAKE_CASE	:       int 							=    pipe(
					    lowerCAmelCase__    ,					image=lowerCAmelCase__    ,					mask_image=lowerCAmelCase__    ,					control_image=lowerCAmelCase__    ,					guidance_scale=9.0    ,					eta=1.0    ,					generator=lowerCAmelCase__    ,					num_inference_steps=20    ,					output_type='np'    ,					)
					SCREAMING_SNAKE_CASE	:       Dict 							=    output.images[0]

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

					SCREAMING_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

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=7    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=3    ,					_SCREAMING_SNAKE_CASE							:					List[str]=18    ,					_SCREAMING_SNAKE_CASE							:					str=30    ,					_SCREAMING_SNAKE_CASE							:					str=400    ,					_SCREAMING_SNAKE_CASE							:					Any=True    ,					_SCREAMING_SNAKE_CASE							:					int=None    ,					_SCREAMING_SNAKE_CASE							:					Dict=0.8_7_5    ,					_SCREAMING_SNAKE_CASE							:					List[str]=True    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=[0.5, 0.5, 0.5]    ,					_SCREAMING_SNAKE_CASE							:					str=[0.5, 0.5, 0.5]    ,					)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    size if size is not None else {'shortest_edge': 20}
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    parent
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    batch_size
					SCREAMING_SNAKE_CASE	:       Any 							=    num_channels
					SCREAMING_SNAKE_CASE	:       Any 							=    image_size
					SCREAMING_SNAKE_CASE	:       str 							=    min_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    max_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    do_resize
					SCREAMING_SNAKE_CASE	:       List[str] 							=    size
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    crop_pct
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    do_normalize
					SCREAMING_SNAKE_CASE	:       Dict 							=    image_mean
					SCREAMING_SNAKE_CASE	:       str 							=    image_std







	def  _lowerCAmelCase    (			self							:					Dict						)	->  Any:





					"""simple docstring"""


					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "crop_pct": self.crop_pct,
					}




@require_torch
@require_vision
class 						lowerCAmelCase__	(  _lowerCamelCase       ,       unittest.TestCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   ConvNextImageProcessor if is_vision_available() else None





	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ConvNextImageProcessingTester(self						)





	@property
	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Dict:





					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()





	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_resize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'size'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'crop_pct'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_normalize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_mean'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_std'						)						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 20}						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 42}						)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					pass





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					Image.Image						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       str 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       str 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE    ,					numpify=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					np.ndarray						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       int 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)







	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE    ,					torchify=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					torch.Tensor						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Any 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





