






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 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					KandinskyVaaPipeline
							A_						  =					[
							    "image_embeds",
							    "negative_image_embeds",
							]
							A_						  =					["image_embeds", "negative_image_embeds"]
							A_						  =					[
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							A_						  =					False

							@property
							def       __A (  self:   Any   )    ->				Dict:
												return 32

							@property
							def       __A (  self:   Optional[Any]   )    ->				List[Any]:
												return 32

							@property
							def       __A (  self:   List[str]   )    ->				List[Any]:
												return self.time_input_dim

							@property
							def       __A (  self:   str   )    ->				List[str]:
												return self.time_input_dim * 4

							@property
							def       __A (  self:   Tuple   )    ->				Optional[Any]:
												return 1_00

							@property
							def       __A (  self:   Union[str, Any]   )    ->				str:
												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(**__A   )
												return model

							@property
							def       __A (  self:   Optional[Any]   )    ->				Optional[int]:
												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:   Optional[Any]   )    ->				str:
												torch.manual_seed(0   )
												_A								= VQModel(**self.dummy_movq_kwargs   )
												return model

							def       __A (  self:   Optional[int]   )    ->				List[str]:
												_A								= self.dummy_unet
												_A								= self.dummy_movq

												_A								= DDIMScheduler(
												    num_train_timesteps=10_00		,     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		,     )

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

							def       __A (  self:   Optional[Any]		,     __A:   Optional[int]		,     __A:   Tuple=0   )    ->				List[str]:
												_A								= floats_tensor((1, self.text_embedder_hidden_size)		,     rng=random.Random(__A   )   ).to(__A   )
												_A								= floats_tensor((1, self.text_embedder_hidden_size)		,     rng=random.Random(seed + 1   )   ).to(
												    __A   )
												if str(__A   ).startswith('''mps'''   ):
																	_A								= torch.manual_seed(__A   )
												else:
																	_A								= torch.Generator(device=__A   ).manual_seed(__A   )
												_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       __A (  self:   int   )    ->				Any:
												_A								= '''cpu'''

												_A								= self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=__A   )

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

												_A								= pipe(
												    **self.get_dummy_inputs(__A   )		,     return_dict=__A		,     )[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_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 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   Optional[int]   )    ->				List[str]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



							def       __A (  self:   List[Any]   )    ->				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(__A   )

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

												_A								= '''red cat, 4k photo'''

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

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

												_A								= output.images[0]

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

												assert_mean_pixel_difference(__A		,     __A   )











import math




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



