"""simple docstring"""



import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


_UpperCamelCase         =		logging.get_logger(__name__)

def    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							A__					:    List[str]														= nn.functional.normalize(lowerCAmelCase       )
							A__					:    List[str]														= nn.functional.normalize(lowerCAmelCase       )
							return torch.mm(lowerCAmelCase ,     normalized_text_embeds.t()       )



class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Union[str, Any]				    =							CLIPConfig

			_UpperCamelCase     :	List[str]				    =							['CLIPEncoderLayer']





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										super().__init__(snake_case_       )

										A__					:    Optional[int]														= CLIPVisionModel(config.vision_config       )
										A__					:    List[str]														= nn.Linear(config.vision_config.hidden_size      ,			config.projection_dim      ,			bias=snake_case_       )

										A__					:    Optional[Any]														= nn.Parameter(torch.ones(17      ,			config.projection_dim       )      ,			requires_grad=snake_case_       )
										A__					:    Dict														= nn.Parameter(torch.ones(3      ,			config.projection_dim       )      ,			requires_grad=snake_case_       )

										A__					:    str														= nn.Parameter(torch.ones(17       )      ,			requires_grad=snake_case_       )
										A__					:    Dict														= nn.Parameter(torch.ones(3       )      ,			requires_grad=snake_case_       )





			@torch.no_grad()
			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= self.vision_model(snake_case_       )[1]  # pooled_output
										A__					:    Dict														= self.visual_projection(snake_case_       )

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										A__					:    int														= cosine_distance(snake_case_      ,			self.special_care_embeds       ).cpu().float().numpy()
										A__					:    List[str]														= cosine_distance(snake_case_      ,			self.concept_embeds       ).cpu().float().numpy()

										A__					:    Optional[Any]														= []
										A__					:    List[str]														= image_embeds.shape[0]
										for i in range(snake_case_       ):
																	A__					:    str														= {"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

																	# increase this value to create a stronger `nfsw` filter
																	# at the cost of increasing the possibility of filtering benign images
																	A__					:    Optional[int]														= 0.0

																	for concept_idx in range(len(special_cos_dist[0]       )       ):
																								A__					:    int														= special_cos_dist[i][concept_idx]
																								A__					:    List[Any]														= self.special_care_embeds_weights[concept_idx].item()
																								A__					:    List[Any]														= round(concept_cos - concept_threshold + adjustment      ,			3       )
																								if result_img["special_scores"][concept_idx] > 0:
																															result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}       )
																															A__					:    Optional[Any]														= 0.01

																	for concept_idx in range(len(cos_dist[0]       )       ):
																								A__					:    Tuple														= cos_dist[i][concept_idx]
																								A__					:    int														= self.concept_embeds_weights[concept_idx].item()
																								A__					:    Any														= round(concept_cos - concept_threshold + adjustment      ,			3       )
																								if result_img["concept_scores"][concept_idx] > 0:
																															result_img["bad_concepts"].append(snake_case_       )

																	result.append(snake_case_       )

										A__					:    Union[str, Any]														= [len(res["""bad_concepts"""]       ) > 0 for res in result]

										for idx, has_nsfw_concept in enumerate(snake_case_       ):
																	if has_nsfw_concept:
																								if torch.is_tensor(snake_case_       ) or torch.is_tensor(images[0]       ):
																															A__					:    Dict														= torch.zeros_like(images[idx]       )  # black image
																								else:
																															A__					:    Dict														= np.zeros(images[idx].shape       )  # black image

										if any(snake_case_       ):
																	logger.warning(
																	    """Potential NSFW content was detected in one or more images. A black image will be returned instead."""
																	    """ Try again with a different prompt and/or seed."""       )

										return images, has_nsfw_concepts





			@torch.no_grad()
			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= self.vision_model(snake_case_       )[1]  # pooled_output
										A__					:    Dict														= self.visual_projection(snake_case_       )

										A__					:    Optional[Any]														= cosine_distance(snake_case_      ,			self.special_care_embeds       )
										A__					:    List[str]														= cosine_distance(snake_case_      ,			self.concept_embeds       )

										# increase this value to create a stronger `nsfw` filter
										# at the cost of increasing the possibility of filtering benign images
										A__					:    Union[str, Any]														= 0.0

										A__					:    Tuple														= special_cos_dist - self.special_care_embeds_weights + adjustment
										# special_scores = special_scores.round(decimals=3)
										A__					:    Union[str, Any]														= torch.any(special_scores > 0      ,			dim=1       )
										A__					:    str														= special_care * 0.01
										A__					:    List[Any]														= special_adjustment.unsqueeze(1       ).expand(-1      ,			cos_dist.shape[1]       )

										A__					:    str														= (cos_dist - self.concept_embeds_weights) + special_adjustment
										# concept_scores = concept_scores.round(decimals=3)
										A__					:    Union[str, Any]														= torch.any(concept_scores > 0      ,			dim=1       )

										A__					:    int														= 0.0  # black image

										return images, has_nsfw_concepts






"""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 	__UpperCAmelCase						(__A       ,      __A       ,      __A       ,      unittest.TestCase    ):







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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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

										A__					:    Union[str, 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      ,			snake_case_      ,			snake_case_=0       ):







										'''simple docstring'''


										if str(snake_case_       ).startswith("""mps"""       ):
																	A__					:    Tuple														= torch.manual_seed(snake_case_       )
										else:
																	A__					:    Dict														= torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )

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

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

										A__					:    int														= {
										    """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 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Tuple				    =							StableDiffusionControlNetInpaintPipeline
			_UpperCamelCase     :	List[Any]				    =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCamelCase     :	List[Any]				    =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCamelCase     :	Tuple				    =							frozenset([]    )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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

										A__					:    Optional[int]														= {
										    """unet""": unet,
										    """controlnet""": controlnet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components





class 	__UpperCAmelCase						(__A       ,      __A       ,      unittest.TestCase    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							StableDiffusionControlNetInpaintPipeline
			_UpperCamelCase     :	int				    =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCamelCase     :	Optional[int]				    =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										torch.manual_seed(0       )
										A__					:    List[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(snake_case_       ):
																	if isinstance(snake_case_      ,			torch.nn.Convad       ):
																								torch.nn.init.normal(m.weight       )
																								m.bias.data.fill_(1.0       )

										A__					:    Dict														= 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(snake_case_       )

										torch.manual_seed(0       )
										A__					:    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(snake_case_       )

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

										A__					:    Optional[Any]														= MultiControlNetModel([controlneta, controlneta]       )

										A__					:    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      ,			snake_case_      ,			snake_case_=0       ):







										'''simple docstring'''


										if str(snake_case_       ).startswith("""mps"""       ):
																	A__					:    Optional[Any]														= torch.manual_seed(snake_case_       )
										else:
																	A__					:    Optional[Any]														= torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )

										A__					:    Any														= 2

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

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

										A__					:    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       ):







										'''simple docstring'''


										A__					:    int														= self.get_dummy_components()
										A__					:    str														= self.pipeline_class(**snake_case_       )
										pipe.to(snake_case_       )

										A__					:    Union[str, Any]														= 10.0
										A__					:    Union[str, Any]														= 4

										A__					:    List[Any]														= self.get_dummy_inputs(snake_case_       )
										A__					:    Optional[int]														= steps
										A__					:    List[Any]														= scale
										A__					:    List[Any]														= pipe(**snake_case_       )[0]

										A__					:    Union[str, Any]														= self.get_dummy_inputs(snake_case_       )
										A__					:    Dict														= steps
										A__					:    List[str]														= scale
										A__					:    str														= pipe(**snake_case_      ,			control_guidance_start=0.1      ,			control_guidance_end=0.2       )[0]

										A__					:    Union[str, Any]														= self.get_dummy_inputs(snake_case_       )
										A__					:    Union[str, Any]														= steps
										A__					:    Tuple														= scale
										A__					:    int														= pipe(**snake_case_      ,			control_guidance_start=[0.1, 0.3]      ,			control_guidance_end=[0.2, 0.7]       )[0]

										A__					:    Any														= self.get_dummy_inputs(snake_case_       )
										A__					:    Union[str, Any]														= steps
										A__					:    str														= scale
										A__					:    Optional[Any]														= pipe(**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'''


										A__					:    Any														= self.get_dummy_components()
										A__					:    int														= self.pipeline_class(**snake_case_       )
										pipe.to(snake_case_       )
										pipe.set_progress_bar_config(disable=snake_case_       )
										with tempfile.TemporaryDirectory() as tmpdir:
																	try:
																								# save_pretrained is not implemented for Multi-ControlNet
																								pipe.save_pretrained(snake_case_       )
																	except NotImplementedError:
																								pass





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







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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

										A__					:    Optional[int]														= StableDiffusionControlNetInpaintPipeline.from_pretrained(
										    """runwayml/stable-diffusion-inpainting"""      ,			safety_checker=snake_case_      ,			controlnet=snake_case_       )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=snake_case_       )

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

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

										A__					:    Optional[int]														= """pitch black hole"""

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

										A__					:    str														= pipe(
										    snake_case_      ,			image=snake_case_      ,			mask_image=snake_case_      ,			control_image=snake_case_      ,			generator=snake_case_      ,			output_type="""np"""      ,			num_inference_steps=3      ,			)

										A__					:    List[Any]														= output.images[0]

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

										A__					:    List[str]														= 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'''


										A__					:    Optional[int]														= ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""       )

										A__					:    List[str]														= StableDiffusionControlNetInpaintPipeline.from_pretrained(
										    """runwayml/stable-diffusion-v1-5"""      ,			safety_checker=snake_case_      ,			controlnet=snake_case_       )
										A__					:    str														= DDIMScheduler.from_config(pipe.scheduler.config       )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=snake_case_       )

										A__					:    List[Any]														= torch.Generator(device="""cpu"""       ).manual_seed(33       )

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

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

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

										def make_inpaint_condition(snake_case_      ,			snake_case_       ):
																	A__					:    str														= np.array(image.convert("""RGB"""       )       ).astype(np.floataa       ) / 2_55.0
																	A__					:    str														= 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__					:    Tuple														= -1.0  # set as masked pixel
																	A__					:    Any														= np.expand_dims(snake_case_      ,			0       ).transpose(0      ,			3      ,			1      ,			2       )
																	A__					:    Tuple														= torch.from_numpy(snake_case_       )
																	return image

										A__					:    int														= make_inpaint_condition(snake_case_      ,			snake_case_       )

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

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

										A__					:    Optional[int]														= 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





