# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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__		(     UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	StableDiffusionControlNetImgaImgPipeline
						UpperCamelCase_  =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
						UpperCamelCase_  =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						UpperCamelCase_  =	IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""})
						UpperCamelCase_  =	IMAGE_TO_IMAGE_IMAGE_PARAMS


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					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		)
												SCREAMING_SNAKE_CASE							:					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)					,	)
												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							DDIMScheduler(
												    beta_start=0.0_0085					,	beta_end=0.012					,	beta_schedule='''scaled_linear'''					,	clip_sample=UpperCamelCase__					,	set_alpha_to_one=UpperCamelCase__					,	)
												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							AutoencoderKL(
												    block_out_channels=[32, 64]					,	in_channels=3					,	out_channels=3					,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,	latent_channels=4					,	)
												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							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					,	)
												SCREAMING_SNAKE_CASE							:					Dict           =							CLIPTextModel(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Dict=0		):





												'''simple docstring'''





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

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





												'''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  __A ( self			:       int		):





												'''simple docstring'''





												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)



						def  __A ( self			:       Dict		):





												'''simple docstring'''





												self._test_inference_batch_single_identical(expected_max_diff=2E-3		)






class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	StableDiffusionControlNetImgaImgPipeline
						UpperCamelCase_  =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
						UpperCamelCase_  =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						UpperCamelCase_  =	frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


						def  __A ( self			:       Dict		):





												'''simple docstring'''





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

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

												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					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(UpperCamelCase__		)

												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							DDIMScheduler(
												    beta_start=0.0_0085					,	beta_end=0.012					,	beta_schedule='''scaled_linear'''					,	clip_sample=UpperCamelCase__					,	set_alpha_to_one=UpperCamelCase__					,	)
												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					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		)
												SCREAMING_SNAKE_CASE							:					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					,	)
												SCREAMING_SNAKE_CASE							:					Any           =							CLIPTextModel(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							MultiControlNetModel([controlneta, controlneta]		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{
												    '''unet''': unet,
												    '''controlnet''': controlnet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}
												return components


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Union[str, Any]=0		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							2

												SCREAMING_SNAKE_CASE							:					Any           =							[
												    randn_tensor(
												        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,	generator=UpperCamelCase__					,	device=torch.device(UpperCamelCase__		)					,	),
												    randn_tensor(
												        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,	generator=UpperCamelCase__					,	device=torch.device(UpperCamelCase__		)					,	),
												]

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							floats_tensor(control_image[0].shape					,	rng=random.Random(UpperCamelCase__		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							image.cpu().permute(0					,	2					,	3					,	1		)[0]
												SCREAMING_SNAKE_CASE							:					Any           =							Image.fromarray(np.uinta(UpperCamelCase__		)		).convert('''RGB'''		).resize((64, 64)		)
												SCREAMING_SNAKE_CASE							:					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  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.get_dummy_components()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.pipeline_class(**UpperCamelCase__		)
												pipe.to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							10.0
												SCREAMING_SNAKE_CASE							:					Dict           =							4

												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_dummy_inputs(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							steps
												SCREAMING_SNAKE_CASE							:					str           =							scale
												SCREAMING_SNAKE_CASE							:					str           =							pipe(**UpperCamelCase__		)[0]

												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_dummy_inputs(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							steps
												SCREAMING_SNAKE_CASE							:					Tuple           =							scale
												SCREAMING_SNAKE_CASE							:					Any           =							pipe(**UpperCamelCase__					,	control_guidance_start=0.1					,	control_guidance_end=0.2		)[0]

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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.get_dummy_inputs(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							steps
												SCREAMING_SNAKE_CASE							:					int           =							scale
												SCREAMING_SNAKE_CASE							:					str           =							pipe(**UpperCamelCase__					,	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  __A ( self			:       List[str]		):





												'''simple docstring'''





												return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)


						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()					,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,	)
						def  __A ( self			:       str		):





												'''simple docstring'''





												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												self._test_inference_batch_single_identical(expected_max_diff=2E-3		)



						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_dummy_components()
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.pipeline_class(**UpperCamelCase__		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)
												with tempfile.TemporaryDirectory() as tmpdir:
																		try:
																								# save_pretrained is not implemented for Multi-ControlNet
																								pipe.save_pretrained(UpperCamelCase__		)
																		except NotImplementedError:
																								pass






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


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





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



						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''		)

												SCREAMING_SNAKE_CASE							:					str           =							StableDiffusionControlNetImgaImgPipeline.from_pretrained(
												    '''runwayml/stable-diffusion-v1-5'''					,	safety_checker=UpperCamelCase__					,	controlnet=UpperCamelCase__		)
												pipe.enable_model_cpu_offload()
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

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

												SCREAMING_SNAKE_CASE							:					List[str]           =							pipe(
												    UpperCamelCase__					,	UpperCamelCase__					,	control_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''np'''					,	num_inference_steps=50					,	strength=0.6					,	)

												SCREAMING_SNAKE_CASE							:					List[str]           =							output.images[0]

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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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






from __future__ import annotations

from typing import Any





class       lowercase__		(     UpperCamelCase_):
						pass






class       lowercase__		:


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							data
												SCREAMING_SNAKE_CASE							:					Node | None           =							None


						def __iter__( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self
												SCREAMING_SNAKE_CASE							:					Tuple           =							[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(UpperCamelCase__		)
																		yield node.data
																		SCREAMING_SNAKE_CASE							:					Dict           =							node.next_node



						@property
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												try:
																		list(self		)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
					__UpperCamelCase	: List[Any]    		=  Node(1)
					__UpperCamelCase	: str    		=  Node(2)
					__UpperCamelCase	: Dict    		=  Node(3)
					__UpperCamelCase	: List[Any]    		=  Node(4)
					print(root_node.has_loop)  # False
					__UpperCamelCase	: int    		=  root_node.next_node
					print(root_node.has_loop)  # True

					__UpperCamelCase	: Union[str, Any]    		=  Node(5)
					__UpperCamelCase	: Union[str, Any]    		=  Node(6)
					__UpperCamelCase	: List[Any]    		=  Node(5)
					__UpperCamelCase	: List[str]    		=  Node(6)
					print(root_node.has_loop)  # False

					__UpperCamelCase	: List[Any]    		=  Node(1)
					print(root_node.has_loop)  # False





