
"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				KandinskyVaaControlnetImgaImgPipeline
			snake_case	      =				["image_embeds", "negative_image_embeds", "image", "hint"]
			snake_case	      =				["image_embeds", "negative_image_embeds", "image", "hint"]
			snake_case	      =				[
			    "generator",
			    "height",
			    "width",
			    "strength",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			snake_case	      =				False




			@property
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								return 32




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								return 32




			@property
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[int]:







								"""simple docstring"""
								return self.time_input_dim




			@property
			def      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								return self.time_input_dim * 4




			@property
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> List[str]:







								"""simple docstring"""
								return 100




			@property
			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[str]:







								"""simple docstring"""
								torch.manual_seed(0	)

								A_				 =						{
								    "in_channels": 8,
								    # Out channels is double in channels because predicts mean and variance
								    "out_channels": 8,
								    "addition_embed_type": "image_hint",
								    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
								    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
								    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
								    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
								    "layers_per_block": 1,
								    "encoder_hid_dim": self.text_embedder_hidden_size,
								    "encoder_hid_dim_type": "image_proj",
								    "cross_attention_dim": self.cross_attention_dim,
								    "attention_head_dim": 4,
								    "resnet_time_scale_shift": "scale_shift",
								    "class_embed_type": None,
								}

								A_				 =						UNetaDConditionModel(**_snake_case	)
								return model




			@property
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Optional[Any]:







								"""simple docstring"""
								return {
								    "block_out_channels": [32, 32, 64, 64],
								    "down_block_types": [
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "AttnDownEncoderBlock2D",
								    ],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "vq_embed_dim": 4,
								}




			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> Any:







								"""simple docstring"""
								torch.manual_seed(0	)
								A_				 =						VQModel(**self.dummy_movq_kwargs	)
								return model




			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						self.dummy_unet
								A_				 =						self.dummy_movq

								A_				 =						{
								    "num_train_timesteps": 1_000,
								    "beta_schedule": "linear",
								    "beta_start": 0.0_0_0_8_5,
								    "beta_end": 0.0_1_2,
								    "clip_sample": False,
								    "set_alpha_to_one": False,
								    "steps_offset": 0,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								}

								A_				 =						DDIMScheduler(**_snake_case	)

								A_				 =						{
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}

								return components




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







								"""simple docstring"""
								A_				 =						floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(_snake_case	)	).to(_snake_case	)
								A_				 =						floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(seed + 1	)	).to(
								    _snake_case	)
								# create init_image
								A_				 =						floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(_snake_case	)	).to(_snake_case	)
								A_				 =						image.cpu().permute(0       ,   2       ,   3       ,   1	)[0]
								A_				 =						Image.fromarray(np.uinta(_snake_case	)	).convert("RGB"	).resize((256, 256)	)
								# create hint
								A_				 =						floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(_snake_case	)	).to(_snake_case	)

								if str(_snake_case	).startswith("mps"	):
													A_				 =						torch.manual_seed(_snake_case	)
								else:
													A_				 =						torch.Generator(device=_snake_case	).manual_seed(_snake_case	)
								A_				 =						{
								    "image": init_image,
								    "image_embeds": image_embeds,
								    "negative_image_embeds": negative_image_embeds,
								    "hint": hint,
								    "generator": generator,
								    "height": 64,
								    "width": 64,
								    "num_inference_steps": 10,
								    "guidance_scale": 7.0,
								    "strength": 0.2,
								    "output_type": "np",
								}
								return inputs






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







								"""simple docstring"""
								A_				 =						"cpu"

								A_				 =						self.get_dummy_components()

								A_				 =						self.pipeline_class(**_snake_case	)
								A_				 =						pipe.to(_snake_case	)

								pipe.set_progress_bar_config(disable=_snake_case	)

								A_				 =						pipe(**self.get_dummy_inputs(_snake_case	)	)
								A_				 =						output.images

								A_				 =						pipe(
								    **self.get_dummy_inputs(_snake_case	)       ,   return_dict=_snake_case       ,   )[0]

								A_				 =						image[0, -3:, -3:, -1]
								A_				 =						image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								A_				 =						np.array(
								    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]	)
								assert (
								    np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
								), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
								), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'





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




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Dict	)      -> List[Any]:







								"""simple docstring"""
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"	)

								A_				 =						load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"	)
								A_				 =						init_image.resize((512, 512)	)

								A_				 =						load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinskyv22/hint_image_cat.png"	)
								A_				 =						torch.from_numpy(np.array(_snake_case	)	).float() / 255.0
								A_				 =						hint.permute(2       ,   0       ,   1	).unsqueeze(0	)

								A_				 =						"A robot, 4k photo"

								A_				 =						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-2-prior"       ,   torch_dtype=torch.floataa	)
								pipe_prior.to(_snake_case	)

								A_				 =						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-2-controlnet-depth"       ,   torch_dtype=torch.floataa	)
								A_				 =						pipeline.to(_snake_case	)

								pipeline.set_progress_bar_config(disable=_snake_case	)

								A_				 =						torch.Generator(device="cpu"	).manual_seed(0	)

								A_ ,    A_				 =						pipe_prior(
								    _snake_case       ,   image=_snake_case       ,   strength=0.8_5       ,   generator=_snake_case       ,   negative_prompt=""       ,   ).to_tuple()

								A_				 =						pipeline(
								    image=_snake_case       ,   image_embeds=_snake_case       ,   negative_image_embeds=_snake_case       ,   hint=_snake_case       ,   generator=_snake_case       ,   num_inference_steps=100       ,   height=512       ,   width=512       ,   strength=0.5       ,   output_type="np"       ,   )

								A_				 =						output.images[0]

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

								assert_mean_pixel_difference(_snake_case       ,   _snake_case	)






"""simple docstring"""





import torch
def 							A_     ():

					'''simple docstring'''





					if torch.cuda.is_available():
										A_				 =						torch.cuda.device_count()
					else:
										A_				 =						0
					print(f'Successfully ran on {num_gpus} GPUs'				)


if __name__ == "__main__":
		main()




