


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 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Dict   				=      KandinskyVaaControlnetImgaImgPipeline
					UpperCAmelCase__ :   Any   				=      ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
					UpperCAmelCase__ :   Dict   				=      ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
					UpperCAmelCase__ :   Union[str, Any]   				=      [
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__ :   str   				=      False
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Tuple:
												return 32
					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Tuple:
												return 32
					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   List[Any]:
												return self.time_input_dim
					@property
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Dict:
												return self.time_input_dim * 4
					@property
					def 			lowerCamelCase__(				self       :List[str]				) ->   Any:
												return 1_00
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   Optional[Any]:
												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       :Union[str, Any]				) ->   Optional[Any]:
												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       :int				) ->   Optional[int]:
												torch.manual_seed(0				)
												a__					=					VQModel(**self.dummy_movq_kwargs				)
												return model
					def 			lowerCamelCase__(				self       :str				) ->   Optional[int]:
												a__					=					self.dummy_unet
												a__					=					self.dummy_movq

												a__					=					{
												    'num_train_timesteps': 10_00,
												    'beta_schedule': 'linear',
												    'beta_start': 0.0_00_85,
												    'beta_end': 0.0_12,
												    '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       :Tuple      ,__snake_case       :Optional[int]      ,__snake_case       :str=0				) ->   Optional[Any]:
												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((2_56, 2_56)				)
												# 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       :Tuple				) ->   Optional[int]:
												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(
												    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]				)
												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 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def 			lowerCamelCase__(				self       :int				) ->   List[Any]:
												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((5_12, 5_12)				)

												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() / 2_55.0
												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=0.85      ,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=1_00      ,height=5_12      ,width=5_12      ,strength=0.5      ,output_type='np'      ,)

												a__					=					output.images[0]

												assert image.shape == (5_12, 5_12, 3)

												assert_mean_pixel_difference(__snake_case      ,__snake_case				)








def 							__lowercase       (			__lowerCAmelCase							:    int       ):

							if num <= 0:
														raise ValueError('Input must be a positive integer'       )

							a__					=					[True] * (num + 1)

							a__					=					2
							while p * p <= num:
														if primes[p]:
																					for i in range(p * p ,   num + 1 ,   __lowerCAmelCase       ):
																												a__					=					False
														p += 1

							return [prime for prime in range(2 ,   num + 1       ) if primes[prime]]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						snake_case							:	Optional[Any]					      =				int(input('''Enter a positive integer: ''').strip())
						print(prime_sieve_eratosthenes(user_num))




