





# 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 					__A    (    lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						StableDiffusionControlNetImgaImgPipeline
			lowerCAmelCase_							     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
			lowerCAmelCase_							     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			lowerCAmelCase_							     =						IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""} )
			lowerCAmelCase_							     =						IMAGE_TO_IMAGE_IMAGE_PARAMS





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__								=		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							)
				lowerCamelCase__								=		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							)
				lowerCamelCase__								=		DDIMScheduler(
				    beta_start=0.0_0085       ,  beta_end=0.012       ,  beta_schedule='''scaled_linear'''       ,  clip_sample=__lowerCAmelCase       ,  set_alpha_to_one=__lowerCAmelCase       ,  )
				torch.manual_seed(0							)
				lowerCamelCase__								=		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							)
				lowerCamelCase__								=		CLIPTextConfig(
				    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=3_2       ,  intermediate_size=3_7       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1_0_0_0       ,  )
				lowerCamelCase__								=		CLIPTextModel(__lowerCAmelCase							)
				lowerCamelCase__								=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

				lowerCamelCase__								=		{
				    '''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       ,  __lowerCAmelCase       ,  __lowerCAmelCase=0							):



				'''simple docstring'''







				if str(__lowerCAmelCase							).startswith('''mps'''							):
					lowerCamelCase__								=		torch.manual_seed(__lowerCAmelCase							)
				else:
					lowerCamelCase__								=		torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)

				lowerCamelCase__								=		2
				lowerCamelCase__								=		randn_tensor(
				    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)       ,  generator=__lowerCAmelCase       ,  device=torch.device(__lowerCAmelCase							)       ,  )
				lowerCamelCase__								=		floats_tensor(control_image.shape       ,  rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		image.cpu().permute(0       ,  2       ,  3       ,  1							)[0]
				lowerCamelCase__								=		Image.fromarray(np.uinta(__lowerCAmelCase							)							).convert('''RGB'''							).resize((6_4, 6_4)							)
				lowerCamelCase__								=		{
				    '''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 		__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 					__A    (    lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__								=		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							)

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

				lowerCamelCase__								=		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							)
				lowerCamelCase__								=		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							)
				lowerCamelCase__								=		DDIMScheduler(
				    beta_start=0.0_0085       ,  beta_end=0.012       ,  beta_schedule='''scaled_linear'''       ,  clip_sample=__lowerCAmelCase       ,  set_alpha_to_one=__lowerCAmelCase       ,  )
				torch.manual_seed(0							)
				lowerCamelCase__								=		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							)
				lowerCamelCase__								=		CLIPTextConfig(
				    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=3_2       ,  intermediate_size=3_7       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1_0_0_0       ,  )
				lowerCamelCase__								=		CLIPTextModel(__lowerCAmelCase							)
				lowerCamelCase__								=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

				lowerCamelCase__								=		MultiControlNetModel([controlneta, controlneta]							)

				lowerCamelCase__								=		{
				    '''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       ,  __lowerCAmelCase       ,  __lowerCAmelCase=0							):



				'''simple docstring'''







				if str(__lowerCAmelCase							).startswith('''mps'''							):
					lowerCamelCase__								=		torch.manual_seed(__lowerCAmelCase							)
				else:
					lowerCamelCase__								=		torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)

				lowerCamelCase__								=		2

				lowerCamelCase__								=		[
				    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							)       ,  ),
				]

				lowerCamelCase__								=		floats_tensor(control_image[0].shape       ,  rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		image.cpu().permute(0       ,  2       ,  3       ,  1							)[0]
				lowerCamelCase__								=		Image.fromarray(np.uinta(__lowerCAmelCase							)							).convert('''RGB'''							).resize((6_4, 6_4)							)
				lowerCamelCase__								=		{
				    '''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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_dummy_components()
				lowerCamelCase__								=		self.pipeline_class(**__lowerCAmelCase							)
				pipe.to(__lowerCAmelCase							)

				lowerCamelCase__								=		10.0
				lowerCamelCase__								=		4

				lowerCamelCase__								=		self.get_dummy_inputs(__lowerCAmelCase							)
				lowerCamelCase__								=		steps
				lowerCamelCase__								=		scale
				lowerCamelCase__								=		pipe(**__lowerCAmelCase							)[0]

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

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

				lowerCamelCase__								=		self.get_dummy_inputs(__lowerCAmelCase							)
				lowerCamelCase__								=		steps
				lowerCamelCase__								=		scale
				lowerCamelCase__								=		pipe(**__lowerCAmelCase       ,  control_guidance_start=0.4       ,  control_guidance_end=[0.5, 0.8]							)[0]

				# make sure that all outputs are different
				assert np.sum(np.abs(output_a - output_a							)							) > 1E-3
				assert np.sum(np.abs(output_a - output_a							)							) > 1E-3
				assert np.sum(np.abs(output_a - output_a							)							) > 1E-3





			def 		__lowerCamelCase				(    self							):



				'''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'''







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






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

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''							)

				lowerCamelCase__								=		StableDiffusionControlNetImgaImgPipeline.from_pretrained(
				    '''runwayml/stable-diffusion-v1-5'''       ,  safety_checker=__lowerCAmelCase       ,  controlnet=__lowerCAmelCase							)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

				lowerCamelCase__								=		torch.Generator(device='''cpu'''							).manual_seed(0							)
				lowerCamelCase__								=		'''evil space-punk bird'''
				lowerCamelCase__								=		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)							)
				lowerCamelCase__								=		load_image(
				    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''							).resize((5_1_2, 5_1_2)							)

				lowerCamelCase__								=		pipe(
				    __lowerCAmelCase       ,  __lowerCAmelCase       ,  control_image=__lowerCAmelCase       ,  generator=__lowerCAmelCase       ,  output_type='''np'''       ,  num_inference_steps=5_0       ,  strength=0.6       ,  )

				lowerCamelCase__								=		output.images[0]

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

				lowerCamelCase__								=		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










def lowerCAmelCase__(__snake_case							)				->      str:







	'''simple docstring'''







	return "".join(chr(ord(__snake_case							) - 32							) if '''a''' <= char <= '''z''' else char for char in word							)


if __name__ == "__main__":
			from doctest import testmod

			testmod()



