


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				KandinskyVaaControlnetPipeline
				_snake_case						       =				['''image_embeds''', '''negative_image_embeds''', '''hint''']
				_snake_case						       =				['''image_embeds''', '''negative_image_embeds''', '''hint''']
				_snake_case						       =				[
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''latents''',
				    '''guidance_scale''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				_snake_case						       =				False

				@property
				def 	a__		(     self							)    ->  Optional[Any]:
					return 3_2

				@property
				def 	a__		(     self							)    ->  Optional[int]:
					return 3_2

				@property
				def 	a__		(     self							)    ->  Optional[int]:
					return self.time_input_dim

				@property
				def 	a__		(     self							)    ->  Tuple:
					return self.time_input_dim * 4

				@property
				def 	a__		(     self							)    ->  Optional[Any]:
					return 1_0_0

				@property
				def 	a__		(     self							)    ->  Tuple:
					torch.manual_seed(0							)

					lowercase						:		Any   					=		{
					    "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,
					}

					lowercase						:		str   					=		UNetaDConditionModel(**a_							)
					return model

				@property
				def 	a__		(     self							)    ->  int:
					return {
					    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
					    "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": 1_2,
					    "out_channels": 3,
					    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
					    "vq_embed_dim": 4,
					}

				@property
				def 	a__		(     self							)    ->  int:
					torch.manual_seed(0							)
					lowercase						:		Optional[int]   					=		VQModel(**self.dummy_movq_kwargs							)
					return model

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Dict   					=		self.dummy_unet
					lowercase						:		int   					=		self.dummy_movq

					lowercase						:		Optional[int]   					=		DDIMScheduler(
					    num_train_timesteps=1_0_0_0							,      beta_schedule="linear"							,      beta_start=0.0_00_85							,      beta_end=0.0_12							,      clip_sample=a_							,      set_alpha_to_one=a_							,      steps_offset=1							,      prediction_type="epsilon"							,      thresholding=a_							,      )

					lowercase						:		Dict   					=		{
					    "unet": unet,
					    "scheduler": scheduler,
					    "movq": movq,
					}
					return components

				def 	a__		(     self							,      a_							,      a_=0							)    ->  Optional[int]:
					lowercase						:		Optional[Any]   					=		floats_tensor((1, self.text_embedder_hidden_size)							,      rng=random.Random(a_							)							).to(a_							)
					lowercase						:		List[Any]   					=		floats_tensor((1, self.text_embedder_hidden_size)							,      rng=random.Random(seed + 1							)							).to(
					    a_							)

					# create hint
					lowercase						:		Tuple   					=		floats_tensor((1, 3, 6_4, 6_4)							,      rng=random.Random(a_							)							).to(a_							)

					if str(a_							).startswith("mps"							):
						lowercase						:		Dict   					=		torch.manual_seed(a_							)
					else:
						lowercase						:		Optional[Any]   					=		torch.Generator(device=a_							).manual_seed(a_							)
					lowercase						:		Union[str, Any]   					=		{
					    "image_embeds": image_embeds,
					    "negative_image_embeds": negative_image_embeds,
					    "hint": hint,
					    "generator": generator,
					    "height": 6_4,
					    "width": 6_4,
					    "guidance_scale": 4.0,
					    "num_inference_steps": 2,
					    "output_type": "np",
					}
					return inputs


				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Optional[Any]   					=		"cpu"

					lowercase						:		List[str]   					=		self.get_dummy_components()

					lowercase						:		Any   					=		self.pipeline_class(**a_							)
					lowercase						:		Optional[Any]   					=		pipe.to(a_							)

					pipe.set_progress_bar_config(disable=a_							)

					lowercase						:		str   					=		pipe(**self.get_dummy_inputs(a_							)							)
					lowercase						:		Optional[int]   					=		output.images

					lowercase						:		List[Any]   					=		pipe(
					    **self.get_dummy_inputs(a_							)							,      return_dict=a_							,      )[0]

					lowercase						:		str   					=		image[0, -3:, -3:, -1]
					lowercase						:		Union[str, Any]   					=		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 6_4, 6_4, 3)

					lowercase						:		List[str]   					=		np.array(
					    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]							)

					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        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def 	a__		(     self							)    ->  Union[str, Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()


				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		Union[str, Any]   					=		load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"							)

					lowercase						:		Optional[int]   					=		load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/kandinskyv22/hint_image_cat.png"							)
					lowercase						:		List[str]   					=		torch.from_numpy(np.array(a_							)							).float() / 2_55.0
					lowercase						:		Tuple   					=		hint.permute(2							,      0							,      1							).unsqueeze(0							)

					lowercase						:		Union[str, Any]   					=		KandinskyVaaPriorPipeline.from_pretrained(
					    "kandinsky-community/kandinsky-2-2-prior"							,      torch_dtype=torch.floataa							)
					pipe_prior.to(a_							)

					lowercase						:		Optional[Any]   					=		KandinskyVaaControlnetPipeline.from_pretrained(
					    "kandinsky-community/kandinsky-2-2-controlnet-depth"							,      torch_dtype=torch.floataa							)
					lowercase						:		List[str]   					=		pipeline.to(a_							)
					pipeline.set_progress_bar_config(disable=a_							)

					lowercase						:		List[Any]   					=		"A robot, 4k photo"

					lowercase						:		int   					=		torch.Generator(device="cuda"							).manual_seed(0							)
					lowercase					,		lowercase						:		Union[str, Any]   					=		pipe_prior(
					    a_							,      generator=a_							,      num_inference_steps=5							,      negative_prompt=""							,      ).to_tuple()

					lowercase						:		Optional[Any]   					=		torch.Generator(device="cuda"							).manual_seed(0							)
					lowercase						:		Dict   					=		pipeline(
					    image_embeds=a_							,      negative_image_embeds=a_							,      hint=a_							,      generator=a_							,      num_inference_steps=1_0_0							,      output_type="np"							,      )

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

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

					assert_mean_pixel_difference(a_							,      a_							)






'''simple docstring'''
import baseaa




def 	_A  (       A							)  ->				bytes:
	return baseaa.aaaencode(string.encode("utf-8"							)							)




def 	_A  (       A							)  ->				str:
	return baseaa.aaadecode(A							).decode("utf-8"							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


