




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,
    StableDiffusionControlNetImgaImgPipeline,
    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 (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class __lowercase(      lowercase__	,	lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						List[str]     		= StableDiffusionControlNetImgaImgPipeline
					__a						:						Optional[Any]     		= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
					__a						:						List[str]     		= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					__a						:						List[str]     		= IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}				)
					__a						:						Union[str, Any]     		= IMAGE_TO_IMAGE_IMAGE_PARAMS
					def     snake_case_				(    self     ):
								torch.manual_seed(0     )
								__lowerCamelCase					:	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     )
								__lowerCamelCase					:	Union[str, Any]	       =				ControlNetModel(
								    block_out_channels=(32, 64)						,	layers_per_block=2						,	in_channels=4						,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,	cross_attention_dim=32						,	conditioning_embedding_out_channels=(16, 32)						,	)
								torch.manual_seed(0     )
								__lowerCamelCase					:	Dict	       =				DDIMScheduler(
								    beta_start=0.00_085						,	beta_end=0.012						,	beta_schedule='scaled_linear'						,	clip_sample=__a						,	set_alpha_to_one=__a						,	)
								torch.manual_seed(0     )
								__lowerCamelCase					:	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     )
								__lowerCamelCase					:	List[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=1000						,	)
								__lowerCamelCase					:	Any	       =				CLIPTextModel(__a     )
								__lowerCamelCase					:	Optional[int]	       =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

								__lowerCamelCase					:	Optional[int]	       =				{
								    '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						,	__a						,	__a=0     ):
								if str(__a     ).startswith('mps'     ):
											__lowerCamelCase					:	List[Any]	       =				torch.manual_seed(__a     )
								else:
											__lowerCamelCase					:	Any	       =				torch.Generator(device=__a     ).manual_seed(__a     )

								__lowerCamelCase					:	int	       =				2
								__lowerCamelCase					:	Tuple	       =				randn_tensor(
								    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,	generator=__a						,	device=torch.device(__a     )						,	)
								__lowerCamelCase					:	Optional[Any]	       =				floats_tensor(control_image.shape						,	rng=random.Random(__a     )     ).to(__a     )
								__lowerCamelCase					:	List[str]	       =				image.cpu().permute(0						,	2						,	3						,	1     )[0]
								__lowerCamelCase					:	Any	       =				Image.fromarray(np.uinta(__a     )     ).convert('RGB'     ).resize((64, 64)     )
								__lowerCamelCase					:	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,
								    'control_image': control_image,
								}

								return inputs
					def     snake_case_				(    self     ):
								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     ):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )





					def     snake_case_				(    self     ):
								self._test_inference_batch_single_identical(expected_max_diff=2E-3     )



class __lowercase(      lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Tuple     		= StableDiffusionControlNetImgaImgPipeline
					__a						:						List[Any]     		= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
					__a						:						Dict     		= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					__a						:						Union[str, Any]     		= frozenset([]				)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
					def     snake_case_				(    self     ):
								torch.manual_seed(0     )
								__lowerCamelCase					:	Union[str, 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     )

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

								__lowerCamelCase					:	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(__a     )

								torch.manual_seed(0     )
								__lowerCamelCase					:	Optional[int]	       =				ControlNetModel(
								    block_out_channels=(32, 64)						,	layers_per_block=2						,	in_channels=4						,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,	cross_attention_dim=32						,	conditioning_embedding_out_channels=(16, 32)						,	)
								controlneta.controlnet_down_blocks.apply(__a     )

								torch.manual_seed(0     )
								__lowerCamelCase					:	Union[str, Any]	       =				DDIMScheduler(
								    beta_start=0.00_085						,	beta_end=0.012						,	beta_schedule='scaled_linear'						,	clip_sample=__a						,	set_alpha_to_one=__a						,	)
								torch.manual_seed(0     )
								__lowerCamelCase					:	int	       =				AutoencoderKL(
								    block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,	latent_channels=4						,	)
								torch.manual_seed(0     )
								__lowerCamelCase					:	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=1000						,	)
								__lowerCamelCase					:	int	       =				CLIPTextModel(__a     )
								__lowerCamelCase					:	Optional[Any]	       =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

								__lowerCamelCase					:	Optional[int]	       =				MultiControlNetModel([controlneta, controlneta]     )

								__lowerCamelCase					:	Any	       =				{
								    '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						,	__a						,	__a=0     ):
								if str(__a     ).startswith('mps'     ):
											__lowerCamelCase					:	Optional[int]	       =				torch.manual_seed(__a     )
								else:
											__lowerCamelCase					:	Optional[int]	       =				torch.Generator(device=__a     ).manual_seed(__a     )

								__lowerCamelCase					:	List[str]	       =				2

								__lowerCamelCase					:	str	       =				[
								    randn_tensor(
								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,	generator=__a						,	device=torch.device(__a     )						,	),
								    randn_tensor(
								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,	generator=__a						,	device=torch.device(__a     )						,	),
								]

								__lowerCamelCase					:	Optional[int]	       =				floats_tensor(control_image[0].shape						,	rng=random.Random(__a     )     ).to(__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				image.cpu().permute(0						,	2						,	3						,	1     )[0]
								__lowerCamelCase					:	Optional[Any]	       =				Image.fromarray(np.uinta(__a     )     ).convert('RGB'     ).resize((64, 64)     )
								__lowerCamelCase					:	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,
								    'control_image': control_image,
								}

								return inputs
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.get_dummy_components()
								__lowerCamelCase					:	Dict	       =				self.pipeline_class(**__a     )
								pipe.to(__a     )

								__lowerCamelCase					:	str	       =				10.0
								__lowerCamelCase					:	Optional[int]	       =				4

								__lowerCamelCase					:	List[Any]	       =				self.get_dummy_inputs(__a     )
								__lowerCamelCase					:	Dict	       =				steps
								__lowerCamelCase					:	Any	       =				scale
								__lowerCamelCase					:	Optional[int]	       =				pipe(**__a     )[0]

								__lowerCamelCase					:	Tuple	       =				self.get_dummy_inputs(__a     )
								__lowerCamelCase					:	Optional[Any]	       =				steps
								__lowerCamelCase					:	str	       =				scale
								__lowerCamelCase					:	Tuple	       =				pipe(**__a						,	control_guidance_start=0.1						,	control_guidance_end=0.2     )[0]

								__lowerCamelCase					:	Any	       =				self.get_dummy_inputs(__a     )
								__lowerCamelCase					:	List[str]	       =				steps
								__lowerCamelCase					:	List[Any]	       =				scale
								__lowerCamelCase					:	Dict	       =				pipe(**__a						,	control_guidance_start=[0.1, 0.3]						,	control_guidance_end=[0.2, 0.7]     )[0]

								__lowerCamelCase					:	int	       =				self.get_dummy_inputs(__a     )
								__lowerCamelCase					:	Tuple	       =				steps
								__lowerCamelCase					:	str	       =				scale
								__lowerCamelCase					:	int	       =				pipe(**__a						,	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     ):
								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     ):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )
					def     snake_case_				(    self     ):
								self._test_inference_batch_single_identical(expected_max_diff=2E-3     )





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



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




					'''simple docstring'''


					def     snake_case_				(    self     ):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'     )

								__lowerCamelCase					:	Dict	       =				StableDiffusionControlNetImgaImgPipeline.from_pretrained(
								    'runwayml/stable-diffusion-v1-5'						,	safety_checker=__a						,	controlnet=__a     )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__a     )

								__lowerCamelCase					:	Optional[Any]	       =				torch.Generator(device='cpu'     ).manual_seed(0     )
								__lowerCamelCase					:	Dict	       =				'evil space-punk bird'
								__lowerCamelCase					:	Optional[int]	       =				load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'     ).resize((512, 512)     )
								__lowerCamelCase					:	int	       =				load_image(
								    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'     ).resize((512, 512)     )

								__lowerCamelCase					:	int	       =				pipe(
								    __a						,	__a						,	control_image=__a						,	generator=__a						,	output_type='np'						,	num_inference_steps=50						,	strength=0.6						,	)

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

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

								__lowerCamelCase					:	int	       =				load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'     )

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









"""simple docstring"""
import math





def 						UpperCAmelCase				(			A__:    int = 100     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				sum(i * i for i in range(1							,				n + 1     )     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(math.pow(sum(range(1							,				n + 1     )     )							,				2     )     )
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
					print(F"""{solution() = }""")



