


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

												a__    =  {
												    '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,
												}

												a__    =  UNetaDConditionModel(**__snake_case   )
												return model
					@property
					def 				lowerCamelCase__(   self     :int   )      ->					Any:
												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 				lowerCamelCase__(   self     :str   )      ->					Dict:
												torch.manual_seed(0   )
												a__    =  VQModel(**self.dummy_movq_kwargs   )
												return model
					def 				lowerCamelCase__(   self     :Tuple   )      ->					str:
												a__    =  self.dummy_unet
												a__    =  self.dummy_movq

												a__    =  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    ,)

												a__    =  {
												    'unet': unet,
												    'scheduler': scheduler,
												    'movq': movq,
												}
												return components
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Tuple    ,__snake_case     :List[str]=0   )      ->					Dict:
												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   )
												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_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 				lowerCamelCase__(   self     :Any   )      ->					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(
												    [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]   )

												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     :Union[str, Any]   )      ->					List[Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def 				lowerCamelCase__(   self     :Any   )      ->					Optional[int]:
												a__    =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'   )

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

												a__    =  KandinskyVaaPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-decoder'    ,torch_dtype=torch.floataa   )
												a__    =  pipeline.to(__snake_case   )
												pipeline.set_progress_bar_config(disable=__snake_case   )

												a__    =  'red cat, 4k photo'

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

												a__    =  torch.Generator(device='cuda'   ).manual_seed(0   )
												a__    =  pipeline(
												    image_embeds=__snake_case    ,negative_image_embeds=__snake_case    ,generator=__snake_case    ,num_inference_steps=1_00    ,output_type='np'    ,)

												a__    =  output.images[0]

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

												assert_mean_pixel_difference(__snake_case    ,__snake_case   )








from math import sqrt






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							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(sqrt(__lowerCAmelCase     ) + 1     )      ,					6     ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True






def 							__lowercase     (				__lowerCAmelCase							:       int = 1_0_0_0_1     ):
							a__    =  0
							a__    =  1
							while count != nth and number < 3:
														number += 1
														if is_prime(__lowerCAmelCase     ):
																					count += 1
							while count != nth:
														number += 2
														if is_prime(__lowerCAmelCase     ):
																					count += 1
							return number


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




