
'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: List[Any]										=  KandinskyVaaImgaImgPipeline
						_lowercase						: List[Any]										=  ['''image_embeds''', '''negative_image_embeds''', '''image''']
						_lowercase						: str										=  [
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						]
						_lowercase						: int										=  [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''strength''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_lowercase						: str										=  False




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 32




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 32




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.time_input_dim




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.time_input_dim * 4




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 100




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )

												_lowerCAmelCase    		=					{
												    """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,
												}

												_lowerCAmelCase    		=					UNetaDConditionModel(**_lowercase )
												return model




						@property
						def    _lowercase      (   self ):

												"""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    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.dummy_unet
												_lowerCAmelCase    		=					self.dummy_movq

												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_schedule""": """linear""",
												    """beta_start""": 0.0_0085,
												    """beta_end""": 0.012,
												    """clip_sample""": False,
												    """set_alpha_to_one""": False,
												    """steps_offset""": 0,
												    """prediction_type""": """epsilon""",
												    """thresholding""": False,
												}

												_lowerCAmelCase    		=					DDIMScheduler(**_lowercase )

												_lowerCAmelCase    		=					{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """movq""": movq,
												}

												return components




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

												"""simple docstring"""



												_lowerCAmelCase    		=					floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1 ) ).to(
												    _lowercase )
												# create init_image
												_lowerCAmelCase    		=					floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												_lowerCAmelCase    		=					Image.fromarray(np.uinta(_lowercase ) ).convert("""RGB""" ).resize((256, 256) )

												if str(_lowercase ).startswith("""mps""" ):
																		_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												else:
																		_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(_lowercase )
												_lowerCAmelCase    		=					{
												    """image""": init_image,
												    """image_embeds""": image_embeds,
												    """negative_image_embeds""": negative_image_embeds,
												    """generator""": generator,
												    """height""": 64,
												    """width""": 64,
												    """num_inference_steps""": 10,
												    """guidance_scale""": 7.0,
												    """strength""": 0.2,
												    """output_type""": """np""",
												}
												return inputs




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""cpu"""

												_lowerCAmelCase    		=					self.get_dummy_components()

												_lowerCAmelCase    		=					self.pipeline_class(**_lowercase )
												_lowerCAmelCase    		=					pipe.to(_lowercase )

												pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					pipe(**self.get_dummy_inputs(_lowercase ) )
												_lowerCAmelCase    		=					output.images

												_lowerCAmelCase    		=					pipe(
												    **self.get_dummy_inputs(_lowercase )       ,  return_dict=_lowercase       ,  )[0]

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

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

												_lowerCAmelCase    		=					np.array(
												    [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263] )
												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    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/kandinskyv22/kandinskyv22_img2img_frog.npy""" )

												_lowerCAmelCase    		=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
												_lowerCAmelCase    		=					"""A red cartoon frog, 4k"""

												_lowerCAmelCase    		=					KandinskyVaaPriorPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-prior"""       ,  torch_dtype=torch.floataa )
												pipe_prior.to(_lowercase )

												_lowerCAmelCase    		=					KandinskyVaaImgaImgPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-decoder"""       ,  torch_dtype=torch.floataa )
												_lowerCAmelCase    		=					pipeline.to(_lowercase )

												pipeline.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					torch.Generator(device="""cpu""" ).manual_seed(0 )
												_lowerCAmelCase		,       _lowerCAmelCase    		=					pipe_prior(
												    _lowercase       ,  generator=_lowercase       ,  num_inference_steps=5       ,  negative_prompt=""""""       ,  ).to_tuple()

												_lowerCAmelCase    		=					pipeline(
												    image=_lowercase       ,  image_embeds=_lowercase       ,  negative_image_embeds=_lowercase       ,  generator=_lowercase       ,  num_inference_steps=100       ,  height=768       ,  width=768       ,  strength=0.2       ,  output_type="""np"""       ,  )

												_lowerCAmelCase    		=					output.images[0]

												assert image.shape == (768, 768, 3)

												assert_mean_pixel_difference(_lowercase       ,  _lowercase )


from math import isqrt, loga
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						list[int]:
       SCREAMING_SNAKE_CASE_							:				int		=[True] * max_number
       for i in range(2			,							isqrt(max_number - 1				) + 1				):
              if is_prime[i]:
                     for j in range(i**2			,							UpperCAmelCase_			,							UpperCAmelCase_				):
                            SCREAMING_SNAKE_CASE_							:				Dict		=False

       return [i for i in range(2			,							UpperCAmelCase_				) if is_prime[i]]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8_0_0_8_0_0			,							UpperCAmelCase_	:     int = 8_0_0_8_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				str		=degree * loga(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=int(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				int		=calculate_prime_numbers(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				int		=0
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0
       SCREAMING_SNAKE_CASE_							:				Any		=len(UpperCAmelCase_				) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]				)
                  + prime_numbers[left] * loga(prime_numbers[right]				)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


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