


'''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				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Dict			      = KandinskyVaaControlnetImgaImgPipeline
	_lowerCamelCase   :					int			      = ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
	_lowerCamelCase   :					Union[str, Any]			      = ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
	_lowerCamelCase   :					Optional[int]			      = [
	    """generator""",
	    """height""",
	    """width""",
	    """strength""",
	    """guidance_scale""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	_lowerCamelCase   :					Any			      = False



	@property
	def 				lowercase     (					self    ):
		return 32



	@property
	def 				lowercase     (					self    ):
		return 32



	@property
	def 				lowercase     (					self    ):
		return self.time_input_dim



	@property
	def 				lowercase     (					self    ):
		return self.time_input_dim * 4



	@property
	def 				lowercase     (					self    ):
		return 100



	@property
	def 				lowercase     (					self    ):
		torch.manual_seed(0    )

		lowerCAmelCase					:    Optional[int]      							= {
		    '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,
		}

		lowerCAmelCase					:    int      							= UNetaDConditionModel(**snake_case__    )
		return model



	@property
	def 				lowercase     (					self    ):
		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 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    List[Any]      							= VQModel(**self.dummy_movq_kwargs    )
		return model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.dummy_unet
		lowerCAmelCase					:    int      							= self.dummy_movq

		lowerCAmelCase					:    str      							= {
		    'num_train_timesteps': 1000,
		    '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,
		}

		lowerCAmelCase					:    Dict      							= DDIMScheduler(**snake_case__    )

		lowerCAmelCase					:    str      							= {
		    'unet': unet,
		    'scheduler': scheduler,
		    'movq': movq,
		}

		return components



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		lowerCAmelCase					:    int      							= floats_tensor((1, self.text_embedder_hidden_size) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    Tuple      							= floats_tensor((1, self.text_embedder_hidden_size) ,					rng=random.Random(seed + 1    )    ).to(
		    snake_case__    )
		# create init_image
		lowerCAmelCase					:    Tuple      							= floats_tensor((1, 3, 64, 64) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= image.cpu().permute(0 ,					2 ,					3 ,					1    )[0]
		lowerCAmelCase					:    int      							= Image.fromarray(np.uinta(snake_case__    )    ).convert('RGB'    ).resize((256, 256)    )
		# create hint
		lowerCAmelCase					:    List[str]      							= floats_tensor((1, 3, 64, 64) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )

		if str(snake_case__    ).startswith('mps'    ):
			lowerCAmelCase					:    Dict      							= torch.manual_seed(snake_case__    )
		else:
			lowerCAmelCase					:    str      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
		lowerCAmelCase					:    str      							= {
		    '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 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= 'cpu'

		lowerCAmelCase					:    int      							= self.get_dummy_components()

		lowerCAmelCase					:    Optional[Any]      							= self.pipeline_class(**snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= pipe.to(snake_case__    )

		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    int      							= pipe(**self.get_dummy_inputs(snake_case__    )    )
		lowerCAmelCase					:    Any      							= output.images

		lowerCAmelCase					:    str      							= pipe(
		    **self.get_dummy_inputs(snake_case__    ) ,					return_dict=snake_case__ ,					)[0]

		lowerCAmelCase					:    int      							= image[0, -3:, -3:, -1]
		lowerCAmelCase					:    List[str]      							= image_from_tuple[0, -3:, -3:, -1]

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

		lowerCAmelCase					:    List[Any]      							= 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							):



	def 				lowercase     (					self    ):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'    )

		lowerCAmelCase					:    List[str]      							= load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
		lowerCAmelCase					:    List[Any]      							= init_image.resize((512, 512)    )

		lowerCAmelCase					:    str      							= load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/kandinskyv22/hint_image_cat.png'    )
		lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(np.array(snake_case__    )    ).float() / 255.0
		lowerCAmelCase					:    int      							= hint.permute(2 ,					0 ,					1    ).unsqueeze(0    )

		lowerCAmelCase					:    List[str]      							= 'A robot, 4k photo'

		lowerCAmelCase					:    int      							= KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
		    'kandinsky-community/kandinsky-2-2-prior' ,					torch_dtype=torch.floataa    )
		pipe_prior.to(snake_case__    )

		lowerCAmelCase					:    Union[str, Any]      							= KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
		    'kandinsky-community/kandinsky-2-2-controlnet-depth' ,					torch_dtype=torch.floataa    )
		lowerCAmelCase					:    str      							= pipeline.to(snake_case__    )

		pipeline.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Dict      							= torch.Generator(device='cpu'    ).manual_seed(0    )

		lowerCAmelCase					:    int      							= pipe_prior(
		    snake_case__ ,					image=snake_case__ ,					strength=0.8_5 ,					generator=snake_case__ ,					negative_prompt='' ,					).to_tuple()

		lowerCAmelCase					:    int      							= 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' ,					)

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

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

		assert_mean_pixel_difference(snake_case__ ,					snake_case__    )





'''simple docstring'''




_lowerCAmelCase  :		List[str]		     =	{str(digit): digit**5 for digit in range(10)}

def 			__UpperCamelCase    (    _A    :				int				)    ->      int:




	"""simple docstring"""





	return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_A				)				)

def 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	return sum(
	    number
	    for number in range(10_00					,   1_00_00_00				)
	    if number == digits_fifth_powers_sum(_A				)				)


if __name__ == "__main__":
				print(solution())
