
'''simple docstring'''
# 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_ (      UpperCamelCase__		,  UpperCamelCase__		,  UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			str       =    StableDiffusionControlNetImgaImgPipeline
			lowerCamelCase__	:			Optional[Any]       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
			lowerCamelCase__	:			Any       =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			lowerCamelCase__	:			Dict       =    IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}					)
			lowerCamelCase__	:			Optional[Any]       =    IMAGE_TO_IMAGE_IMAGE_PARAMS




			def 					lowerCAmelCase__					(       self	):
				torch.manual_seed(0	)
				a_      				=						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_      				=						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_      				=						DDIMScheduler(
				    beta_start=0.0_00_85					,   beta_end=0.0_12					,   beta_schedule="""scaled_linear"""					,   clip_sample=UpperCAmelCase					,   set_alpha_to_one=UpperCAmelCase					,   )
				torch.manual_seed(0	)
				a_      				=						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_      				=						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=10_00					,   )
				a_      				=						CLIPTextModel(UpperCAmelCase	)
				a_      				=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

				a_      				=						{
				    """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					,   UpperCAmelCase					,   UpperCAmelCase=0	):
				if str(UpperCAmelCase	).startswith("""mps"""	):
					a_      				=						torch.manual_seed(UpperCAmelCase	)
				else:
					a_      				=						torch.Generator(device=UpperCAmelCase	).manual_seed(UpperCAmelCase	)

				a_      				=						2
				a_      				=						randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,   generator=UpperCAmelCase					,   device=torch.device(UpperCAmelCase	)					,   )
				a_      				=						floats_tensor(control_image.shape					,   rng=random.Random(UpperCAmelCase	)	).to(UpperCAmelCase	)
				a_      				=						image.cpu().permute(0					,   2					,   3					,   1	)[0]
				a_      				=						Image.fromarray(np.uinta(UpperCAmelCase	)	).convert("""RGB"""	).resize((64, 64)	)
				a_      				=						{
				    """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	):
				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	):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)






			def 					lowerCAmelCase__					(       self	):
				self._test_inference_batch_single_identical(expected_max_diff=2e-3	)







class   a_ (      UpperCamelCase__		,  UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			List[Any]       =    StableDiffusionControlNetImgaImgPipeline
			lowerCamelCase__	:			Optional[Any]       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
			lowerCamelCase__	:			str       =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			lowerCamelCase__	:			List[str]       =    frozenset([]					)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




			def 					lowerCAmelCase__					(       self	):
				torch.manual_seed(0	)
				a_      				=						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	):
					if isinstance(UpperCAmelCase					,   torch.nn.Convad	):
						torch.nn.init.normal(m.weight	)
						m.bias.data.fill_(1.0	)

				a_      				=						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	)
				a_      				=						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	)
				a_      				=						DDIMScheduler(
				    beta_start=0.0_00_85					,   beta_end=0.0_12					,   beta_schedule="""scaled_linear"""					,   clip_sample=UpperCAmelCase					,   set_alpha_to_one=UpperCAmelCase					,   )
				torch.manual_seed(0	)
				a_      				=						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_      				=						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=10_00					,   )
				a_      				=						CLIPTextModel(UpperCAmelCase	)
				a_      				=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

				a_      				=						MultiControlNetModel([controlneta, controlneta]	)

				a_      				=						{
				    """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					,   UpperCAmelCase					,   UpperCAmelCase=0	):
				if str(UpperCAmelCase	).startswith("""mps"""	):
					a_      				=						torch.manual_seed(UpperCAmelCase	)
				else:
					a_      				=						torch.Generator(device=UpperCAmelCase	).manual_seed(UpperCAmelCase	)

				a_      				=						2

				a_      				=						[
				    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	)					,   ),
				]

				a_      				=						floats_tensor(control_image[0].shape					,   rng=random.Random(UpperCAmelCase	)	).to(UpperCAmelCase	)
				a_      				=						image.cpu().permute(0					,   2					,   3					,   1	)[0]
				a_      				=						Image.fromarray(np.uinta(UpperCAmelCase	)	).convert("""RGB"""	).resize((64, 64)	)
				a_      				=						{
				    """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	):
				a_      				=						self.get_dummy_components()
				a_      				=						self.pipeline_class(**UpperCAmelCase	)
				pipe.to(UpperCAmelCase	)

				a_      				=						10.0
				a_      				=						4

				a_      				=						self.get_dummy_inputs(UpperCAmelCase	)
				a_      				=						steps
				a_      				=						scale
				a_      				=						pipe(**UpperCAmelCase	)[0]

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

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

				a_      				=						self.get_dummy_inputs(UpperCAmelCase	)
				a_      				=						steps
				a_      				=						scale
				a_      				=						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 					lowerCAmelCase__					(       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 					lowerCAmelCase__					(       self	):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)




			def 					lowerCAmelCase__					(       self	):
				self._test_inference_batch_single_identical(expected_max_diff=2e-3	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_dummy_components()
				a_      				=						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   a_ (      unittest.TestCase					):




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






			def 					lowerCAmelCase__					(       self	):
				a_      				=						ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""	)

				a_      				=						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	)

				a_      				=						torch.Generator(device="""cpu"""	).manual_seed(0	)
				a_      				=						"""evil space-punk bird"""
				a_      				=						load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""	).resize((5_12, 5_12)	)
				a_      				=						load_image(
				    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""	).resize((5_12, 5_12)	)

				a_      				=						pipe(
				    UpperCAmelCase					,   UpperCAmelCase					,   control_image=UpperCAmelCase					,   generator=UpperCAmelCase					,   output_type="""np"""					,   num_inference_steps=50					,   strength=0.6					,   )

				a_      				=						output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				a_      				=						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 unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowercase__											=False







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=32	):
				set_seed(0	)
				a_      				=						UNetaDModel(sample_size=UpperCAmelCase					,   in_channels=3					,   out_channels=3	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.00_01	)
				return model, optimizer






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				a_      				=						DDPMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )
				a_      				=						DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				a_      				=						[torch.randn((4, 3, 32, 32)	).clip(-1					,   1	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randn((4, 3, 32, 32)	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randint(0					,   10_00					,   (4,)	).long().to(UpperCAmelCase	) for _ in range(4	)]

				# train with a DDPM scheduler
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddpm_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddim_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
