




"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 __lowercase(      lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						str     		= KandinskyVaaPipeline
					__a						:						Optional[int]     		= [
					    'image_embeds',
					    'negative_image_embeds',
					]
					__a						:						Tuple     		= ['image_embeds', 'negative_image_embeds']
					__a						:						Dict     		= [
					    'generator',
					    'height',
					    'width',
					    'latents',
					    'guidance_scale',
					    'num_inference_steps',
					    'return_dict',
					    'guidance_scale',
					    'num_images_per_prompt',
					    'output_type',
					    'return_dict',
					]
					__a						:						int     		= False
					@property
					def     snake_case_				(    self     ):
								return 32
					@property
					def     snake_case_				(    self     ):
								return 32
					@property
					def     snake_case_				(    self     ):
								return self.time_input_dim
					@property
					def     snake_case_				(    self     ):
								return self.time_input_dim * 4
					@property
					def     snake_case_				(    self     ):
								return 100
					@property
					def     snake_case_				(    self     ):
								torch.manual_seed(0     )

								__lowerCamelCase					:	Optional[int]	       =				{
								    '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,
								}

								__lowerCamelCase					:	Dict	       =				UNetaDConditionModel(**__a     )
								return model
					@property
					def     snake_case_				(    self     ):
								return {
								    "block_out_channels": [32, 64],
								    "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": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}
					@property
					def     snake_case_				(    self     ):
								torch.manual_seed(0     )
								__lowerCamelCase					:	Tuple	       =				VQModel(**self.dummy_movq_kwargs     )
								return model
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.dummy_unet
								__lowerCamelCase					:	List[str]	       =				self.dummy_movq

								__lowerCamelCase					:	Any	       =				DDIMScheduler(
								    num_train_timesteps=1000						,	beta_schedule='linear'						,	beta_start=0.00_085						,	beta_end=0.012						,	clip_sample=__a						,	set_alpha_to_one=__a						,	steps_offset=1						,	prediction_type='epsilon'						,	thresholding=__a						,	)

								__lowerCamelCase					:	Dict	       =				{
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}
								return components
					def     snake_case_				(    self						,	__a						,	__a=0     ):
								__lowerCamelCase					:	List[str]	       =				floats_tensor((1, self.text_embedder_hidden_size)						,	rng=random.Random(__a     )     ).to(__a     )
								__lowerCamelCase					:	Any	       =				floats_tensor((1, self.text_embedder_hidden_size)						,	rng=random.Random(seed + 1     )     ).to(
								    __a     )
								if str(__a     ).startswith('mps'     ):
											__lowerCamelCase					:	Optional[int]	       =				torch.manual_seed(__a     )
								else:
											__lowerCamelCase					:	Union[str, Any]	       =				torch.Generator(device=__a     ).manual_seed(__a     )
								__lowerCamelCase					:	List[Any]	       =				{
								    'image_embeds': image_embeds,
								    'negative_image_embeds': negative_image_embeds,
								    'generator': generator,
								    'height': 64,
								    'width': 64,
								    'guidance_scale': 4.0,
								    'num_inference_steps': 2,
								    'output_type': 'np',
								}
								return inputs





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				'cpu'

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

								__lowerCamelCase					:	int	       =				self.pipeline_class(**__a     )
								__lowerCamelCase					:	Any	       =				pipe.to(__a     )

								pipe.set_progress_bar_config(disable=__a     )

								__lowerCamelCase					:	List[Any]	       =				pipe(**self.get_dummy_inputs(__a     )     )
								__lowerCamelCase					:	Union[str, Any]	       =				output.images

								__lowerCamelCase					:	Union[str, Any]	       =				pipe(
								    **self.get_dummy_inputs(__a     )						,	return_dict=__a						,	)[0]

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

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

								__lowerCamelCase					:	int	       =				np.array(
								    [0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046]     )

								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 __lowercase(      unittest.TestCase				):




					'''simple docstring'''


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





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'     )

								__lowerCamelCase					:	int	       =				KandinskyVaaPriorPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-2-prior'						,	torch_dtype=torch.floataa     )
								pipe_prior.to(__a     )

								__lowerCamelCase					:	int	       =				KandinskyVaaPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-2-decoder'						,	torch_dtype=torch.floataa     )
								__lowerCamelCase					:	int	       =				pipeline.to(__a     )
								pipeline.set_progress_bar_config(disable=__a     )

								__lowerCamelCase					:	Dict	       =				'red cat, 4k photo'

								__lowerCamelCase					:	Optional[int]	       =				torch.Generator(device='cuda'     ).manual_seed(0     )
								__lowerCamelCase       ,      __lowerCamelCase					:	List[Any]	       =				pipe_prior(
								    __a						,	generator=__a						,	num_inference_steps=5						,	negative_prompt=''						,	).to_tuple()

								__lowerCamelCase					:	Any	       =				torch.Generator(device='cuda'     ).manual_seed(0     )
								__lowerCamelCase					:	Any	       =				pipeline(
								    image_embeds=__a						,	negative_image_embeds=__a						,	generator=__a						,	num_inference_steps=100						,	output_type='np'						,	)

								__lowerCamelCase					:	Optional[Any]	       =				output.images[0]

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

								assert_mean_pixel_difference(__a						,	__a     )









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations(A__:    int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations_with_dp_array(
			    A__:    int							,				A__:    list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						__lowerCamelCase					:	Dict	       =				sum(
						    count_of_possible_combinations_with_dp_array(target - item							,				A__     )
						    for item in array     )
						__lowerCamelCase					:	str	       =				answer
						return answer

			__lowerCamelCase					:	Union[str, Any]	       =				[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:
			__lowerCamelCase					:	int	       =				[0] * (target + 1)
			__lowerCamelCase					:	str	       =				1

			for i in range(1							,				target + 1     ):
						for j in range(A__     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					a_				:    Any										=     3
					a_				:    str										=     5
					a_				:    List[Any]										=     [1, 2, 5]
					print(combination_sum_iv(n, array, target))



