


'''simple docstring'''





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							Dict							= {
			    '''sld_warmup_steps''': 15,
			    '''sld_guidance_scale''': 20,
			    '''sld_threshold''': 0.0,
			    '''sld_momentum_scale''': 0.0,
			    '''sld_mom_beta''': 0.0,
			}
			A	:							int							= {
			    '''sld_warmup_steps''': 10,
			    '''sld_guidance_scale''': 1000,
			    '''sld_threshold''': 0.0_1,
			    '''sld_momentum_scale''': 0.3,
			    '''sld_mom_beta''': 0.4,
			}
			A	:							Tuple							= {
			    '''sld_warmup_steps''': 7,
			    '''sld_guidance_scale''': 2000,
			    '''sld_threshold''': 0.0_2_5,
			    '''sld_momentum_scale''': 0.5,
			    '''sld_mom_beta''': 0.7,
			}
			A	:							List[str]							= {
			    '''sld_warmup_steps''': 0,
			    '''sld_guidance_scale''': 5000,
			    '''sld_threshold''': 1.0,
			    '''sld_momentum_scale''': 0.5,
			    '''sld_mom_beta''': 0.7,
			}


@dataclass
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							Union[List[PIL.Image.Image], np.ndarray]
			A	:							Optional[List[bool]]
			A	:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
			A	:							Optional[str]


if is_transformers_available() and is_torch_available():
					from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
					from .safety_checker import SafeStableDiffusionSafetyChecker







'''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 	a__				(				a_			,     a_			,     a_			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Optional[int]							= StableDiffusionControlNetInpaintPipeline
			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							List[Any]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A	:							Any							= frozenset({'''control_image'''}      )  # skip `image` and `mask` for now, only test for control_image
			A	:							List[Any]							= TEXT_TO_IMAGE_IMAGE_PARAMS
			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
						torch.manual_seed(0  )
						__A=  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  )
						__A=  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  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  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 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  {
						    '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       : List[str] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : int=0  )			->		str:
						if str(lowerCAmelCase_  ).startswith('mps'  ):
									__A=  torch.manual_seed(lowerCAmelCase_  )
						else:
									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )

						__A=  2
						__A=  randn_tensor(
						    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						)
						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]

						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )

						__A=  {
						    '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       : Tuple  )			->		Optional[int]:
						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       : Optional[int]  )			->		str:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )

			def 				lowerCAmelCase    (     self       : List[Any]  )			->		str:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )


class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= StableDiffusionControlNetInpaintPipeline
			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							Tuple							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A	:							int							= frozenset([]      )
			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
						torch.manual_seed(0  )
						__A=  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  )
						__A=  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  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  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 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components


class 	a__				(				a_			,     a_			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Tuple							= StableDiffusionControlNetInpaintPipeline
			A	:							int							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							Optional[int]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
						torch.manual_seed(0  )
						__A=  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(lowerCAmelCase_       : Optional[int]  ):
									if isinstance(lowerCAmelCase_ ,						torch.nn.Convad  ):
												torch.nn.init.normal(m.weight  )
												m.bias.data.fill_(1.0  )

						__A=  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  )
						__A=  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  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  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 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  MultiControlNetModel([controlneta, controlneta]  )

						__A=  {
						    '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       : Union[str, Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[Any]=0  )			->		List[Any]:
						if str(lowerCAmelCase_  ).startswith('mps'  ):
									__A=  torch.manual_seed(lowerCAmelCase_  )
						else:
									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )

						__A=  2

						__A=  [
						    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_  ) ,						),
						]
						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]

						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )

						__A=  {
						    '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]  )			->		Any:
						__A=  self.get_dummy_components()
						__A=  self.pipeline_class(**lowerCAmelCase_  )
						pipe.to(lowerCAmelCase_  )

						__A=  10.0
						__A=  4

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=0.1 ,						control_guidance_end=0.2  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=[0.1, 0.3] ,						control_guidance_end=[0.2, 0.7]  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  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       : str  )			->		Any:
						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       : Any  )			->		int:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
			def 				lowerCAmelCase    (     self       : Dict  )			->		Dict:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )

			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Optional[int]:
						__A=  self.get_dummy_components()
						__A=  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 	a__				(				unittest.TestCase      ):





			'''simple docstring'''

			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Tuple:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
						__A=  ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'  )

						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

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

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

						__A=  'pitch black hole'

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

						__A=  pipe(
						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						output_type='np' ,						num_inference_steps=3 ,						)

						__A=  output.images[0]

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

						__A=  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  )			->		Tuple:
						__A=  ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'  )

						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-v1-5' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
						__A=  DDIMScheduler.from_config(pipe.scheduler.config  )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

						__A=  torch.Generator(device='cpu'  ).manual_seed(33  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'  )
						__A=  init_image.resize((512, 512)  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'  )
						__A=  mask_image.resize((512, 512)  )

						__A=  'a handsome man with ray-ban sunglasses'

						def make_inpaint_condition(lowerCAmelCase_       : int ,						lowerCAmelCase_       : str  ):
									__A=  np.array(image.convert('RGB'  )  ).astype(np.floataa  ) / 2_55.0
									__A=  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"
									__A=  -1.0  # set as masked pixel
									__A=  np.expand_dims(lowerCAmelCase_ ,						0  ).transpose(0 ,						3 ,						1 ,						2  )
									__A=  torch.from_numpy(lowerCAmelCase_  )
									return image

						__A=  make_inpaint_condition(lowerCAmelCase_ ,						lowerCAmelCase_  )

						__A=  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' ,						)
						__A=  output.images[0]

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

						__A=  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



