



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_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Union[str, Any]      =  KandinskyVaaControlnetPipeline
						UpperCamelCase__      :			Union[str, Any]      =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
						UpperCamelCase__      :			List[str]      =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
						UpperCamelCase__      :			Tuple      =  [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						UpperCamelCase__      :			Optional[Any]      =  False






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 32






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 32






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.time_input_dim






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.time_input_dim * 4






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 100






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)

													__SCREAMING_SNAKE_CASE									= {
													    '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,
													}

													__SCREAMING_SNAKE_CASE									= UNetaDConditionModel(**_A		)
													return model






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													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 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= VQModel(**self.dummy_movq_kwargs		)
													return model






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.dummy_unet
													__SCREAMING_SNAKE_CASE									= self.dummy_movq

													__SCREAMING_SNAKE_CASE									= DDIMScheduler(
													    num_train_timesteps=1_000				,							beta_schedule='linear'				,							beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							clip_sample=_A				,							set_alpha_to_one=_A				,							steps_offset=1				,							prediction_type='epsilon'				,							thresholding=_A				,							)

													__SCREAMING_SNAKE_CASE									= {
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}
													return components






						def 	_A     (   self				,							_A				,							_A=0		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= floats_tensor((1, self.text_embedder_hidden_size)				,							rng=random.Random(_A		)		).to(_A		)
													__SCREAMING_SNAKE_CASE									= floats_tensor((1, self.text_embedder_hidden_size)				,							rng=random.Random(seed + 1		)		).to(
													    _A		)

													# create hint
													__SCREAMING_SNAKE_CASE									= floats_tensor((1, 3, 64, 64)				,							rng=random.Random(_A		)		).to(_A		)

													if str(_A		).startswith('mps'		):
																				__SCREAMING_SNAKE_CASE									= torch.manual_seed(_A		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(_A		)
													__SCREAMING_SNAKE_CASE									= {
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'hint': hint,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()

													__SCREAMING_SNAKE_CASE									= self.pipeline_class(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)

													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= pipe(**self.get_dummy_inputs(_A		)		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= pipe(
													    **self.get_dummy_inputs(_A		)				,							return_dict=_A				,							)[0]

													__SCREAMING_SNAKE_CASE									= image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE									= image_from_tuple[0, -3:, -3:, -1]

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

													__SCREAMING_SNAKE_CASE									= np.array(
													    [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]		)

													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		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'		)

													__SCREAMING_SNAKE_CASE									= load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinskyv22/hint_image_cat.png'		)
													__SCREAMING_SNAKE_CASE									= torch.from_numpy(np.array(_A		)		).float() / 2_5_5.0
													__SCREAMING_SNAKE_CASE									= hint.permute(2				,							0				,							1		).unsqueeze(0		)

													__SCREAMING_SNAKE_CASE									= KandinskyVaaPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-2-prior'				,							torch_dtype=torch.floataa		)
													pipe_prior.to(_A		)

													__SCREAMING_SNAKE_CASE									= KandinskyVaaControlnetPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-2-controlnet-depth'				,							torch_dtype=torch.floataa		)
													__SCREAMING_SNAKE_CASE									= pipeline.to(_A		)
													pipeline.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= 'A robot, 4k photo'

													__SCREAMING_SNAKE_CASE									= torch.Generator(device='cuda'		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= pipe_prior(
													    _A				,							generator=_A				,							num_inference_steps=5				,							negative_prompt=''				,							).to_tuple()

													__SCREAMING_SNAKE_CASE									= torch.Generator(device='cuda'		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipeline(
													    image_embeds=_A				,							negative_image_embeds=_A				,							hint=_A				,							generator=_A				,							num_inference_steps=100				,							output_type='np'				,							)

													__SCREAMING_SNAKE_CASE									= output.images[0]

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

													assert_mean_pixel_difference(_A				,							_A		)











from __future__ import annotations

from collections.abc import Generator






def      __lowercase       (			)    ->			Generator[int, None, None]:
							__SCREAMING_SNAKE_CASE									= {}
							__SCREAMING_SNAKE_CASE									= 2
							while True:
														__SCREAMING_SNAKE_CASE									= factor_map.pop(a__							,       a__       )
														if factor:
																					__SCREAMING_SNAKE_CASE									= factor + prime
																					while x in factor_map:
																												x += factor
																					__SCREAMING_SNAKE_CASE									= factor
														else:
																					__SCREAMING_SNAKE_CASE									= prime
																					yield prime
														prime += 1






def      __lowercase       (			a__ = 1E10       )    ->			int:
							__SCREAMING_SNAKE_CASE									= sieve()

							__SCREAMING_SNAKE_CASE									= 1
							while True:
														__SCREAMING_SNAKE_CASE									= next(a__       )
														if (2 * prime * n) > limit:
																					return n
														# Ignore the next prime as the reminder will be 2.
														next(a__       )
														n += 2


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






