




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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    lowercase_		(SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =KandinskyVaaInpaintPipeline
						lowerCAmelCase__			      =["image_embeds", "negative_image_embeds", "image", "mask_image"]
						lowerCAmelCase__			      =[
						    "image_embeds",
						    "negative_image_embeds",
						    "image",
						    "mask_image",
						]
						lowerCAmelCase__			      =[
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						lowerCAmelCase__			      =False



						@property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								return 32



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return 32



						@property
						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								return self.time_input_dim



						@property
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								return self.time_input_dim * 4



						@property
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								return 1_00



						@property
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								torch.manual_seed(0  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'in_channels': 9,
								    # 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,
								}

								SCREAMING_SNAKE_CASE_					      =							UNetaDConditionModel(**snake_case__  )
								return model



						@property
						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								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 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.dummy_unet
								SCREAMING_SNAKE_CASE_					      =							self.dummy_movq

								SCREAMING_SNAKE_CASE_					      =							DDIMScheduler(
								    num_train_timesteps=10_00					,							beta_schedule='linear'					,							beta_start=0.0_00_85					,							beta_end=0.0_12					,							clip_sample=snake_case__					,							set_alpha_to_one=snake_case__					,							steps_offset=1					,							prediction_type='epsilon'					,							thresholding=snake_case__					,							)

								SCREAMING_SNAKE_CASE_					      =							{
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}

								return components



						def 	__a					(			self	:   str					,							snake_case__	:   Tuple					,							snake_case__	:   Optional[int]=0  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							floats_tensor((1, self.text_embedder_hidden_size)					,							rng=random.Random(snake_case__  )  ).to(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							floats_tensor((1, self.text_embedder_hidden_size)					,							rng=random.Random(seed + 1  )  ).to(
								    snake_case__  )
								# create init_image
								SCREAMING_SNAKE_CASE_					      =							floats_tensor((1, 3, 64, 64)					,							rng=random.Random(snake_case__  )  ).to(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							image.cpu().permute(0					,							2					,							3					,							1  )[0]
								SCREAMING_SNAKE_CASE_					      =							Image.fromarray(np.uinta(snake_case__  )  ).convert('RGB'  ).resize((2_56, 2_56)  )
								# create mask
								SCREAMING_SNAKE_CASE_					      =							np.ones((64, 64)					,							dtype=np.floataa  )
								SCREAMING_SNAKE_CASE_					      =							0

								if str(snake_case__  ).startswith('mps'  ):
										SCREAMING_SNAKE_CASE_					      =							torch.manual_seed(snake_case__  )
								else:
										SCREAMING_SNAKE_CASE_					      =							torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							{
								    'image': init_image,
								    'mask_image': mask,
								    'image_embeds': image_embeds,
								    'negative_image_embeds': negative_image_embeds,
								    'generator': generator,
								    'height': 64,
								    'width': 64,
								    'num_inference_steps': 2,
								    'guidance_scale': 4.0,
								    'output_type': 'np',
								}
								return inputs



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'cpu'

								SCREAMING_SNAKE_CASE_					      =							self.get_dummy_components()

								SCREAMING_SNAKE_CASE_					      =							self.pipeline_class(**snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							pipe.to(snake_case__  )

								pipe.set_progress_bar_config(disable=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							pipe(**self.get_dummy_inputs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							output.images

								SCREAMING_SNAKE_CASE_					      =							pipe(
								    **self.get_dummy_inputs(snake_case__  )					,							return_dict=snake_case__					,							)[0]

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

								print(f'''image.shape {image.shape}'''  )

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

								SCREAMING_SNAKE_CASE_					      =							np.array(
								    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]  )

								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()}'''






						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class    lowercase_		(unittest.TestCase ):



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

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






						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'  )

								SCREAMING_SNAKE_CASE_					      =							load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
								SCREAMING_SNAKE_CASE_					      =							np.ones((7_68, 7_68)					,							dtype=np.floataa  )
								SCREAMING_SNAKE_CASE_					      =							0

								SCREAMING_SNAKE_CASE_					      =							'a hat'

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

								SCREAMING_SNAKE_CASE_					      =							KandinskyVaaInpaintPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-2-decoder-inpaint'					,							torch_dtype=torch.floataa  )
								SCREAMING_SNAKE_CASE_					      =							pipeline.to(snake_case__  )
								pipeline.set_progress_bar_config(disable=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							torch.Generator(device='cpu'  ).manual_seed(0  )
								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							pipe_prior(
								    snake_case__					,							generator=snake_case__					,							num_inference_steps=5					,							negative_prompt=''					,							).to_tuple()

								SCREAMING_SNAKE_CASE_					      =							pipeline(
								    image=snake_case__					,							mask_image=snake_case__					,							image_embeds=snake_case__					,							negative_image_embeds=snake_case__					,							generator=snake_case__					,							num_inference_steps=1_00					,							height=7_68					,							width=7_68					,							output_type='np'					,							)

								SCREAMING_SNAKE_CASE_					      =							output.images[0]

								assert image.shape == (7_68, 7_68, 3)

								assert_mean_pixel_difference(snake_case__					,							snake_case__  )





from __future__ import annotations

import math


def 					_a							(					lowerCAmelCase )->	bool:

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5    ,						int(math.sqrt(lowerCAmelCase ) + 1 )    ,						6 ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True


SCREAMING_SNAKE_CASE:     List[str]									= [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]


def 					_a							(					lowerCAmelCase )->	list[int]:
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
				raise ValueError('n must be an integer' )
		if n <= 0:
				raise ValueError('n must be >= 0' )

		SCREAMING_SNAKE_CASE_					      =							[]
		for num in range(len(lowerCAmelCase ) ):
				SCREAMING_SNAKE_CASE_					      =							0
				while 2 * i * i <= odd_composites[num]:
						SCREAMING_SNAKE_CASE_					      =							odd_composites[num] - 2 * i * i
						if is_prime(lowerCAmelCase ):
								break
						i += 1
				else:
						list_nums.append(odd_composites[num] )
						if len(lowerCAmelCase ) == n:
								return list_nums

		return []


def 					_a							(					)->	int:
		return compute_nums(1 )[0]


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