





"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 			_a						(				_lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   KandinskyVaaImgaImgPipeline
				UpperCamelCase								=   ['''image_embeds''', '''negative_image_embeds''', '''image''']
				UpperCamelCase								=   [
				    '''image_embeds''',
				    '''negative_image_embeds''',
				    '''image''',
				]
				UpperCamelCase								=   [
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''strength''',
				    '''guidance_scale''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				UpperCamelCase								=   False






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




										'''simple docstring'''



										return 3_2






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




										'''simple docstring'''



										return 3_2






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




										'''simple docstring'''



										return self.time_input_dim






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




										'''simple docstring'''



										return self.time_input_dim * 4






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




										'''simple docstring'''



										return 1_0_0






				@property
				def     snake_case     (			self		:   Dict							)     ->				Optional[int]:




										'''simple docstring'''



										torch.manual_seed(0							)

										_UpperCamelCase      : List[str]         =   {
										    '''in_channels''': 4,
										    # Out channels is double in channels because predicts mean and variance
										    '''out_channels''': 8,
										    '''addition_embed_type''': '''image''',
										    '''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,
										}

										_UpperCamelCase      : Dict         =   UNetaDConditionModel(**lowerCAmelCase__							)
										return model






				@property
				def     snake_case     (			self		:   int							)     ->				int:




										'''simple docstring'''



										return {
										    "block_out_channels": [3_2, 6_4],
										    "down_block_types": ["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",
										    ],
										    "vq_embed_dim": 4,
										}






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




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase      : Union[str, Any]         =   VQModel(**self.dummy_movq_kwargs							)
										return model






				def     snake_case     (			self		:   Any							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.dummy_unet
										_UpperCamelCase      : List[str]         =   self.dummy_movq

										_UpperCamelCase      : List[str]         =   {
										    '''num_train_timesteps''': 1_0_0_0,
										    '''beta_schedule''': '''linear''',
										    '''beta_start''': 0.00_085,
										    '''beta_end''': 0.012,
										    '''clip_sample''': False,
										    '''set_alpha_to_one''': False,
										    '''steps_offset''': 0,
										    '''prediction_type''': '''epsilon''',
										    '''thresholding''': False,
										}

										_UpperCamelCase      : Tuple         =   DDIMScheduler(**lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   int=0							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   floats_tensor((1, self.text_embedder_hidden_size),			rng=random.Random(lowerCAmelCase__							)							).to(lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   floats_tensor((1, self.text_embedder_hidden_size),			rng=random.Random(seed + 1							)							).to(
										    lowerCAmelCase__							)
										# create init_image
										_UpperCamelCase      : int         =   floats_tensor((1, 3, 6_4, 6_4),			rng=random.Random(lowerCAmelCase__							)							).to(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   image.cpu().permute(0,			2,			3,			1							)[0]
										_UpperCamelCase      : Optional[int]         =   Image.fromarray(np.uinta(lowerCAmelCase__							)							).convert('''RGB'''							).resize((2_5_6, 2_5_6)							)

										if str(lowerCAmelCase__							).startswith('''mps'''							):
																_UpperCamelCase      : List[str]         =   torch.manual_seed(lowerCAmelCase__							)
										else:
																_UpperCamelCase      : List[str]         =   torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   {
										    '''image''': init_image,
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''generator''': generator,
										    '''height''': 6_4,
										    '''width''': 6_4,
										    '''num_inference_steps''': 1_0,
										    '''guidance_scale''': 7.0,
										    '''strength''': 0.2,
										    '''output_type''': '''np''',
										}
										return inputs






				def     snake_case     (			self		:   Optional[int]							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   '''cpu'''

										_UpperCamelCase      : int         =   self.get_dummy_components()

										_UpperCamelCase      : List[Any]         =   self.pipeline_class(**lowerCAmelCase__							)
										_UpperCamelCase      : str         =   pipe.to(lowerCAmelCase__							)

										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : str         =   pipe(**self.get_dummy_inputs(lowerCAmelCase__							)							)
										_UpperCamelCase      : Any         =   output.images

										_UpperCamelCase      : Optional[int]         =   pipe(
										    **self.get_dummy_inputs(lowerCAmelCase__							),			return_dict=lowerCAmelCase__,			)[0]

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

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

										_UpperCamelCase      : List[Any]         =   np.array(
										    [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263]							)
										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 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   int							)     ->				Optional[int]:




										'''simple docstring'''



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






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




										'''simple docstring'''



										_UpperCamelCase      : str         =   load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''							)

										_UpperCamelCase      : str         =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''							)
										_UpperCamelCase      : Tuple         =   '''A red cartoon frog, 4k'''

										_UpperCamelCase      : Optional[Any]         =   KandinskyVaaPriorPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior''',			torch_dtype=torch.floataa							)
										pipe_prior.to(lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   KandinskyVaaImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-decoder''',			torch_dtype=torch.floataa							)
										_UpperCamelCase      : Tuple         =   pipeline.to(lowerCAmelCase__							)

										pipeline.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : int         =   torch.Generator(device='''cpu'''							).manual_seed(0							)
										_UpperCamelCase       ,							_UpperCamelCase      : List[str]         =   pipe_prior(
										    lowerCAmelCase__,			generator=lowerCAmelCase__,			num_inference_steps=5,			negative_prompt='''''',			).to_tuple()

										_UpperCamelCase      : Union[str, Any]         =   pipeline(
										    image=lowerCAmelCase__,			image_embeds=lowerCAmelCase__,			negative_image_embeds=lowerCAmelCase__,			generator=lowerCAmelCase__,			num_inference_steps=1_0_0,			height=7_6_8,			width=7_6_8,			strength=0.2,			output_type='''np''',			)

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

										assert image.shape == (7_6_8, 7_6_8, 3)

										assert_mean_pixel_difference(lowerCAmelCase__,			lowerCAmelCase__							)










"""simple docstring"""




def 	a_    (							_lowercase = 100       ):
						_UpperCamelCase      : List[Any]         =   0
						_UpperCamelCase      : Optional[Any]         =   0
						for i in range(1	,						n + 1       ):
												sum_of_squares += i**2
												sum_of_ints += i
						return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
						print(F"{solution() = }")



