"""simple docstring"""







import random



class 			_lowerCamelCase      :







				def __init__(			self     :							Union[str, Any]      ,       lowerCamelCase_     :							list[list[float]]      ,       lowerCamelCase_     :							list[int]      ,       lowerCamelCase_     :							float = 0.01      ,       lowerCamelCase_     :							int = 1_0_0_0      ,       lowerCamelCase_     :							float = -1      ,       ):

					"""simple docstring"""






					_lowercase			:     Optional[int]      					=				sample
					if len(self.sample    ) == 0:
						raise ValueError('Sample data can not be empty'    )
					_lowercase			:     Optional[int]      					=				target
					if len(self.target    ) == 0:
						raise ValueError('Target data can not be empty'    )
					if len(self.sample    ) != len(self.target    ):
						raise ValueError('Sample data and Target data do not have matching lengths'    )
					_lowercase			:     Any      					=				learning_rate
					_lowercase			:     List[str]      					=				epoch_number
					_lowercase			:     Union[str, Any]      					=				bias
					_lowercase			:     Optional[int]      					=				len(lowerCAmelCase__    )
					_lowercase			:     Optional[int]      					=				len(sample[0]    )  # number of columns in dataset
					_lowercase			:     list      					=				[]







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					for sample in self.sample:
						sample.insert(0      ,       self.bias    )

					for _ in range(self.col_sample    ):
						self.weight.append(random.random()    )

					self.weight.insert(0      ,       self.bias    )

					_lowercase			:     int      					=				0

					while True:
						_lowercase			:     int      					=				False
						for i in range(self.number_sample    ):
							_lowercase			:     Dict      					=				0
							for j in range(self.col_sample + 1    ):
								_lowercase			:     Dict      					=				u + self.weight[j] * self.sample[i][j]
							_lowercase			:     Union[str, Any]      					=				self.sign(lowerCAmelCase__    )
							if y != self.target[i]:
								for j in range(self.col_sample + 1    ):
									_lowercase			:     int      					=				(
									    self.weight[j]
									    + self.learning_rate
									    * (self.target[i] - y)
									    * self.sample[i][j]
									)
								_lowercase			:     Optional[int]      					=				True
            # print('Epoch: \n',epoch_count)
						_lowercase			:     Dict      					=				epoch_count + 1
						# if you want control the epoch or just by error
						if not has_misclassified:
							print(('\nEpoch:\n', epoch_count)    )
							print('------------------------\n'    )
							# if epoch_count > self.epoch_number or not error:
							break







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							list[float]    ):

					"""simple docstring"""






					if len(self.sample    ) == 0:
						raise ValueError('Sample data can not be empty'    )
					sample.insert(0      ,       self.bias    )
					_lowercase			:     Tuple      					=				0
					for i in range(self.col_sample + 1    ):
						_lowercase			:     Optional[Any]      					=				u + self.weight[i] * sample[i]

					_lowercase			:     Union[str, Any]      					=				self.sign(lowerCAmelCase__    )

					if y == -1:
						print(('Sample: ', sample)    )
						print('classification: P1'    )
					else:
						print(('Sample: ', sample)    )
						print('classification: P2'    )







				def 				__UpperCAmelCase       (			self     :							str      ,       lowerCamelCase_     :							float    ):

					"""simple docstring"""






					return 1 if u >= 0 else -1



SCREAMING_SNAKE_CASE						=    [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

SCREAMING_SNAKE_CASE						=    [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						SCREAMING_SNAKE_CASE						=    Perceptron(
						    sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
						)
						network.training()
						print('Finished training perceptron')
						print('Enter values to predict or q to exit')
						while True:
												SCREAMING_SNAKE_CASE						=    []
												for i in range(len(samples[0])):
																		SCREAMING_SNAKE_CASE						=    input('value: ').strip()
																		if user_input == "q":
																								break
																		SCREAMING_SNAKE_CASE						=    float(user_input)
																		sample.insert(i, observation)
												network.sort(sample)










"""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		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       StableDiffusionControlNetInpaintPipeline
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				UpperCamelCase								=       frozenset({'''control_image'''}							)  # skip `image` and `mask` for now, only test for control_image
				UpperCamelCase								=       TEXT_TO_IMAGE_IMAGE_PARAMS






				def 							snake_case      (					self						:       Tuple				)      -> Any:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   Tuple      =       UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=9,					out_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),					cross_attention_dim=3_2,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[str]      =       ControlNetModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					in_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					cross_attention_dim=3_2,					conditioning_embedding_out_channels=(1_6, 3_2),					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   Optional[Any]      =       DDIMScheduler(
										    beta_start=0.00_085,					beta_end=0.012,					beta_schedule='''scaled_linear''',					clip_sample=lowerCAmelCase__,					set_alpha_to_one=lowerCAmelCase__,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   Any      =       AutoencoderKL(
										    block_out_channels=[3_2, 6_4],					in_channels=3,					out_channels=3,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],					latent_channels=4,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[str]      =       CLIPTextConfig(
										    bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1e-0_5,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
										_UpperCamelCase     :   List[str]      =       CLIPTextModel(lowerCAmelCase__				)
										_UpperCamelCase     :   Dict      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int]=0				)      -> Optional[int]:




										'''simple docstring'''



										if str(lowerCAmelCase__				).startswith('''mps'''				):
																_UpperCamelCase     :   str      =       torch.manual_seed(lowerCAmelCase__				)
										else:
																_UpperCamelCase     :   Any      =       torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[Any]      =       2
										_UpperCamelCase     :   int      =       randn_tensor(
										    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor),					generator=lowerCAmelCase__,					device=torch.device(lowerCAmelCase__				),					)
										_UpperCamelCase     :   int      =       floats_tensor((1, 3, 3_2, 3_2),					rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       init_image.cpu().permute(0,					2,					3,					1				)[0]

										_UpperCamelCase     :   Optional[int]      =       Image.fromarray(np.uinta(lowerCAmelCase__				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
										_UpperCamelCase     :   Any      =       Image.fromarray(np.uinta(init_image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)

										_UpperCamelCase     :   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 							snake_case      (					self						:       int				)      -> Union[str, 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 							snake_case      (					self						:       Dict				)      -> int:




										'''simple docstring'''



										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)






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




										'''simple docstring'''



										self._test_inference_batch_single_identical(expected_max_diff=2e-3				)




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       StableDiffusionControlNetInpaintPipeline
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				UpperCamelCase								=       frozenset([]							)






				def 							snake_case      (					self						:       List[Any]				)      -> List[Any]:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   List[Any]      =       UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=4,					out_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),					cross_attention_dim=3_2,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   Optional[int]      =       ControlNetModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					in_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					cross_attention_dim=3_2,					conditioning_embedding_out_channels=(1_6, 3_2),					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[str]      =       DDIMScheduler(
										    beta_start=0.00_085,					beta_end=0.012,					beta_schedule='''scaled_linear''',					clip_sample=lowerCAmelCase__,					set_alpha_to_one=lowerCAmelCase__,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   Optional[int]      =       AutoencoderKL(
										    block_out_channels=[3_2, 6_4],					in_channels=3,					out_channels=3,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],					latent_channels=4,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[str]      =       CLIPTextConfig(
										    bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1e-0_5,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
										_UpperCamelCase     :   int      =       CLIPTextModel(lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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




class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       StableDiffusionControlNetInpaintPipeline
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






				def 							snake_case      (					self						:       Any				)      -> Dict:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   int      =       UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=9,					out_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),					cross_attention_dim=3_2,					)
										torch.manual_seed(0				)

										def init_weights(lowerCAmelCase__						:       Optional[Any]				):
																if isinstance(lowerCAmelCase__,					torch.nn.Convad				):
																						torch.nn.init.normal(m.weight				)
																						m.bias.data.fill_(1.0				)

										_UpperCamelCase     :   List[Any]      =       ControlNetModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					in_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					cross_attention_dim=3_2,					conditioning_embedding_out_channels=(1_6, 3_2),					)
										controlneta.controlnet_down_blocks.apply(lowerCAmelCase__				)

										torch.manual_seed(0				)
										_UpperCamelCase     :   int      =       ControlNetModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					in_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					cross_attention_dim=3_2,					conditioning_embedding_out_channels=(1_6, 3_2),					)
										controlneta.controlnet_down_blocks.apply(lowerCAmelCase__				)

										torch.manual_seed(0				)
										_UpperCamelCase     :   Tuple      =       DDIMScheduler(
										    beta_start=0.00_085,					beta_end=0.012,					beta_schedule='''scaled_linear''',					clip_sample=lowerCAmelCase__,					set_alpha_to_one=lowerCAmelCase__,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   str      =       AutoencoderKL(
										    block_out_channels=[3_2, 6_4],					in_channels=3,					out_channels=3,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],					latent_channels=4,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[Any]      =       CLIPTextConfig(
										    bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1e-0_5,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
										_UpperCamelCase     :   Any      =       CLIPTextModel(lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

										_UpperCamelCase     :   str      =       MultiControlNetModel([controlneta, controlneta]				)

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






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Tuple=0				)      -> str:




										'''simple docstring'''



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

										_UpperCamelCase     :   Any      =       2

										_UpperCamelCase     :   Union[str, Any]      =       [
										    randn_tensor(
										        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor),					generator=lowerCAmelCase__,					device=torch.device(lowerCAmelCase__				),					),
										    randn_tensor(
										        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor),					generator=lowerCAmelCase__,					device=torch.device(lowerCAmelCase__				),					),
										]
										_UpperCamelCase     :   int      =       floats_tensor((1, 3, 3_2, 3_2),					rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       init_image.cpu().permute(0,					2,					3,					1				)[0]

										_UpperCamelCase     :   List[str]      =       Image.fromarray(np.uinta(lowerCAmelCase__				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
										_UpperCamelCase     :   int      =       Image.fromarray(np.uinta(init_image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)

										_UpperCamelCase     :   Dict      =       {
										    '''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 							snake_case      (					self						:       Optional[int]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       self.get_dummy_components()
										_UpperCamelCase     :   Optional[int]      =       self.pipeline_class(**lowerCAmelCase__				)
										pipe.to(lowerCAmelCase__				)

										_UpperCamelCase     :   int      =       10.0
										_UpperCamelCase     :   List[Any]      =       4

										_UpperCamelCase     :   List[str]      =       self.get_dummy_inputs(lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       steps
										_UpperCamelCase     :   Union[str, Any]      =       scale
										_UpperCamelCase     :   List[Any]      =       pipe(**lowerCAmelCase__				)[0]

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

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

										_UpperCamelCase     :   Tuple      =       self.get_dummy_inputs(lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       steps
										_UpperCamelCase     :   int      =       scale
										_UpperCamelCase     :   Optional[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 							snake_case      (					self						:       Union[str, Any]				)      -> Tuple:




										'''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 							snake_case      (					self						:       str				)      -> Union[str, Any]:




										'''simple docstring'''



										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)






				def 							snake_case      (					self						:       List[str]				)      -> Tuple:




										'''simple docstring'''



										self._test_inference_batch_single_identical(expected_max_diff=2e-3				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_dummy_components()
										_UpperCamelCase     :   Union[str, 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 					_a		( unittest.TestCase							):






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




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



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

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

										_UpperCamelCase     :   str      =       torch.Generator(device='''cpu'''				).manual_seed(0				)
										_UpperCamelCase     :   Optional[int]      =       load_image(
										    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''				).resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   List[str]      =       load_image(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
										    '''/stable_diffusion_inpaint/input_bench_mask.png'''				).resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   int      =       '''pitch black hole'''

										_UpperCamelCase     :   List[Any]      =       load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''				).resize((5_1_2, 5_1_2)				)

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

										_UpperCamelCase     :   Union[str, Any]      =       output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)

										_UpperCamelCase     :   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 							snake_case      (					self						:       List[str]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''				)

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

										_UpperCamelCase     :   Any      =       torch.Generator(device='''cpu'''				).manual_seed(3_3				)

										_UpperCamelCase     :   Union[str, Any]      =       load_image(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''				)
										_UpperCamelCase     :   Optional[int]      =       init_image.resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   List[Any]      =       load_image(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''				)
										_UpperCamelCase     :   Dict      =       mask_image.resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   Tuple      =       '''a handsome man with ray-ban sunglasses'''

										def make_inpaint_condition(lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Optional[Any]				):
																_UpperCamelCase     :   Union[str, Any]      =       np.array(image.convert('''RGB'''				)				).astype(np.floataa				) / 255.0
																_UpperCamelCase     :   Optional[int]      =       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"
																_UpperCamelCase     :   Union[str, Any]      =       -1.0  # set as masked pixel
																_UpperCamelCase     :   Union[str, Any]      =       np.expand_dims(lowerCAmelCase__,					0				).transpose(0,					3,					1,					2				)
																_UpperCamelCase     :   List[Any]      =       torch.from_numpy(lowerCAmelCase__				)
																return image

										_UpperCamelCase     :   Any      =       make_inpaint_condition(lowerCAmelCase__,					lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[int]      =       pipe(
										    lowerCAmelCase__,					image=lowerCAmelCase__,					mask_image=lowerCAmelCase__,					control_image=lowerCAmelCase__,					guidance_scale=9.0,					eta=1.0,					generator=lowerCAmelCase__,					num_inference_steps=2_0,					output_type='''np''',					)
										_UpperCamelCase     :   Union[str, Any]      =       output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)

										_UpperCamelCase     :   List[str]      =       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



