
"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()







class   _lowercase						(       unittest.TestCase				):


					'''simple docstring'''





					def  lowerCAmelCase__       (      self  )->					List[Any]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()



					@property
					def  lowerCAmelCase__       (      self  )->					Any:
										UpperCAmelCase__							: Union[str, Any] 							=						1
										UpperCAmelCase__							: Optional[Any] 							=						3
										UpperCAmelCase__							: List[Any] 							=						(32, 32)

										UpperCAmelCase__							: Any 							=						floats_tensor((batch_size, num_channels) + sizes ,			rng=random.Random(0  )  ).to(_UpperCAmelCase  )
										return image



					@property
					def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
										torch.manual_seed(0  )
										UpperCAmelCase__							: Dict 							=						UNetaDConditionModel(
										    block_out_channels=(32, 32, 64) ,			layers_per_block=2 ,			sample_size=32 ,			in_channels=7 ,			out_channels=4 ,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D") ,			up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D") ,			cross_attention_dim=32 ,			attention_head_dim=8 ,			use_linear_projection=_UpperCAmelCase ,			only_cross_attention=(True, True, False) ,			num_class_embeds=1_00 ,			)
										return model



					@property
					def  lowerCAmelCase__       (      self  )->					int:
										torch.manual_seed(0  )
										UpperCAmelCase__							: Tuple 							=						AutoencoderKL(
										    block_out_channels=[32, 32, 64] ,			in_channels=3 ,			out_channels=3 ,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"] ,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"] ,			latent_channels=4 ,			)
										return model



					@property
					def  lowerCAmelCase__       (      self  )->					Optional[Any]:
										torch.manual_seed(0  )
										UpperCAmelCase__							: int 							=						CLIPTextConfig(
										    bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=32 ,			intermediate_size=37 ,			layer_norm_eps=1E-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=10_00 ,			hidden_act="gelu" ,			projection_dim=5_12 ,			)
										return CLIPTextModel(_UpperCAmelCase  )



					def  lowerCAmelCase__       (      self  )->					Tuple:
										UpperCAmelCase__							: int 							=						"cpu"  # ensure determinism for the device-dependent torch.Generator
										UpperCAmelCase__							: Tuple 							=						self.dummy_cond_unet_upscale
										UpperCAmelCase__							: Union[str, Any] 							=						DDPMScheduler()
										UpperCAmelCase__							: Any 							=						DDIMScheduler(prediction_type="v_prediction"  )
										UpperCAmelCase__							: Dict 							=						self.dummy_vae
										UpperCAmelCase__							: List[Any] 							=						self.dummy_text_encoder
										UpperCAmelCase__							: List[Any] 							=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

										UpperCAmelCase__							: List[str] 							=						self.dummy_image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
										UpperCAmelCase__							: List[str] 							=						Image.fromarray(np.uinta(_UpperCAmelCase  )  ).convert("RGB"  ).resize((64, 64)  )

										# make sure here that pndm scheduler skips prk
										UpperCAmelCase__							: str 							=						StableDiffusionUpscalePipeline(
										    unet=_UpperCAmelCase ,			low_res_scheduler=_UpperCAmelCase ,			scheduler=_UpperCAmelCase ,			vae=_UpperCAmelCase ,			text_encoder=_UpperCAmelCase ,			tokenizer=_UpperCAmelCase ,			max_noise_level=3_50 ,			)
										UpperCAmelCase__							: Optional[int] 							=						sd_pipe.to(_UpperCAmelCase  )
										sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase  )

										UpperCAmelCase__							: Optional[Any] 							=						"A painting of a squirrel eating a burger"
										UpperCAmelCase__							: Union[str, Any] 							=						torch.Generator(device=_UpperCAmelCase  ).manual_seed(0  )
										UpperCAmelCase__							: Union[str, Any] 							=						sd_pipe(
										    [prompt] ,			image=_UpperCAmelCase ,			generator=_UpperCAmelCase ,			guidance_scale=6.0 ,			noise_level=20 ,			num_inference_steps=2 ,			output_type="np" ,			)

										UpperCAmelCase__							: Optional[int] 							=						output.images

										UpperCAmelCase__							: Tuple 							=						torch.Generator(device=_UpperCAmelCase  ).manual_seed(0  )
										UpperCAmelCase__							: List[str] 							=						sd_pipe(
										    [prompt] ,			image=_UpperCAmelCase ,			generator=_UpperCAmelCase ,			guidance_scale=6.0 ,			noise_level=20 ,			num_inference_steps=2 ,			output_type="np" ,			return_dict=_UpperCAmelCase ,			)[0]

										UpperCAmelCase__							: Optional[int] 							=						image[0, -3:, -3:, -1]
										UpperCAmelCase__							: List[str] 							=						image_from_tuple[0, -3:, -3:, -1]

										UpperCAmelCase__							: Tuple 							=						low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)
										UpperCAmelCase__							: str 							=						np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]  )

										assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2



					def  lowerCAmelCase__       (      self  )->					int:
										UpperCAmelCase__							: Dict 							=						"cpu"  # ensure determinism for the device-dependent torch.Generator
										UpperCAmelCase__							: int 							=						self.dummy_cond_unet_upscale
										UpperCAmelCase__							: Dict 							=						DDPMScheduler()
										UpperCAmelCase__							: List[str] 							=						DDIMScheduler(prediction_type="v_prediction"  )
										UpperCAmelCase__							: str 							=						self.dummy_vae
										UpperCAmelCase__							: Tuple 							=						self.dummy_text_encoder
										UpperCAmelCase__							: Any 							=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

										UpperCAmelCase__							: Tuple 							=						self.dummy_image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
										UpperCAmelCase__							: Union[str, Any] 							=						Image.fromarray(np.uinta(_UpperCAmelCase  )  ).convert("RGB"  ).resize((64, 64)  )

										# make sure here that pndm scheduler skips prk
										UpperCAmelCase__							: str 							=						StableDiffusionUpscalePipeline(
										    unet=_UpperCAmelCase ,			low_res_scheduler=_UpperCAmelCase ,			scheduler=_UpperCAmelCase ,			vae=_UpperCAmelCase ,			text_encoder=_UpperCAmelCase ,			tokenizer=_UpperCAmelCase ,			max_noise_level=3_50 ,			)
										UpperCAmelCase__							: Dict 							=						sd_pipe.to(_UpperCAmelCase  )
										sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase  )

										UpperCAmelCase__							: Dict 							=						"A painting of a squirrel eating a burger"
										UpperCAmelCase__							: Any 							=						sd_pipe(
										    2 * [prompt] ,			image=2 * [low_res_image] ,			guidance_scale=6.0 ,			noise_level=20 ,			num_inference_steps=2 ,			output_type="np" ,			)
										UpperCAmelCase__							: Tuple 							=						output.images
										assert image.shape[0] == 2

										UpperCAmelCase__							: Any 							=						torch.Generator(device=_UpperCAmelCase  ).manual_seed(0  )
										UpperCAmelCase__							: Union[str, Any] 							=						sd_pipe(
										    [prompt] ,			image=_UpperCAmelCase ,			generator=_UpperCAmelCase ,			num_images_per_prompt=2 ,			guidance_scale=6.0 ,			noise_level=20 ,			num_inference_steps=2 ,			output_type="np" ,			)
										UpperCAmelCase__							: int 							=						output.images
										assert image.shape[0] == 2



					@unittest.skipIf(torch_device != "cuda" ,			"This test requires a GPU"  )
					def  lowerCAmelCase__       (      self  )->					Tuple:
										UpperCAmelCase__							: Tuple 							=						self.dummy_cond_unet_upscale
										UpperCAmelCase__							: Tuple 							=						DDPMScheduler()
										UpperCAmelCase__							: Union[str, Any] 							=						DDIMScheduler(prediction_type="v_prediction"  )
										UpperCAmelCase__							: Tuple 							=						self.dummy_vae
										UpperCAmelCase__							: Optional[Any] 							=						self.dummy_text_encoder
										UpperCAmelCase__							: str 							=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

										UpperCAmelCase__							: Any 							=						self.dummy_image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
										UpperCAmelCase__							: Optional[int] 							=						Image.fromarray(np.uinta(_UpperCAmelCase  )  ).convert("RGB"  ).resize((64, 64)  )

										# put models in fp16, except vae as it overflows in fp16
										UpperCAmelCase__							: Any 							=						unet.half()
										UpperCAmelCase__							: List[Any] 							=						text_encoder.half()

										# make sure here that pndm scheduler skips prk
										UpperCAmelCase__							: Any 							=						StableDiffusionUpscalePipeline(
										    unet=_UpperCAmelCase ,			low_res_scheduler=_UpperCAmelCase ,			scheduler=_UpperCAmelCase ,			vae=_UpperCAmelCase ,			text_encoder=_UpperCAmelCase ,			tokenizer=_UpperCAmelCase ,			max_noise_level=3_50 ,			)
										UpperCAmelCase__							: List[Any] 							=						sd_pipe.to(_UpperCAmelCase  )
										sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase  )

										UpperCAmelCase__							: Union[str, Any] 							=						"A painting of a squirrel eating a burger"
										UpperCAmelCase__							: Tuple 							=						torch.manual_seed(0  )
										UpperCAmelCase__							: Optional[Any] 							=						sd_pipe(
										    [prompt] ,			image=_UpperCAmelCase ,			generator=_UpperCAmelCase ,			num_inference_steps=2 ,			output_type="np" ,			).images

										UpperCAmelCase__							: Tuple 							=						low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)







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


					'''simple docstring'''





					def  lowerCAmelCase__       (      self  )->					Optional[Any]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()



					def  lowerCAmelCase__       (      self  )->					Tuple:
										UpperCAmelCase__							: int 							=						load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"  )
										UpperCAmelCase__							: int 							=						load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
										    "/upsampled_cat.npy"  )

										UpperCAmelCase__							: List[Any] 							=						"stabilityai/stable-diffusion-x4-upscaler"
										UpperCAmelCase__							: Optional[Any] 							=						StableDiffusionUpscalePipeline.from_pretrained(_UpperCAmelCase  )
										pipe.to(_UpperCAmelCase  )
										pipe.set_progress_bar_config(disable=_UpperCAmelCase  )
										pipe.enable_attention_slicing()

										UpperCAmelCase__							: Tuple 							=						"a cat sitting on a park bench"

										UpperCAmelCase__							: Any 							=						torch.manual_seed(0  )
										UpperCAmelCase__							: Any 							=						pipe(
										    prompt=_UpperCAmelCase ,			image=_UpperCAmelCase ,			generator=_UpperCAmelCase ,			output_type="np" ,			)
										UpperCAmelCase__							: Any 							=						output.images[0]

										assert image.shape == (5_12, 5_12, 3)
										assert np.abs(expected_image - image  ).max() < 1E-3



					def  lowerCAmelCase__       (      self  )->					Any:
										UpperCAmelCase__							: List[str] 							=						load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"  )
										UpperCAmelCase__							: List[Any] 							=						load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
										    "/upsampled_cat_fp16.npy"  )

										UpperCAmelCase__							: Optional[int] 							=						"stabilityai/stable-diffusion-x4-upscaler"
										UpperCAmelCase__							: List[str] 							=						StableDiffusionUpscalePipeline.from_pretrained(
										    _UpperCAmelCase ,			torch_dtype=torch.floataa ,			)
										pipe.to(_UpperCAmelCase  )
										pipe.set_progress_bar_config(disable=_UpperCAmelCase  )
										pipe.enable_attention_slicing()

										UpperCAmelCase__							: Dict 							=						"a cat sitting on a park bench"

										UpperCAmelCase__							: Dict 							=						torch.manual_seed(0  )
										UpperCAmelCase__							: Optional[int] 							=						pipe(
										    prompt=_UpperCAmelCase ,			image=_UpperCAmelCase ,			generator=_UpperCAmelCase ,			output_type="np" ,			)
										UpperCAmelCase__							: Optional[int] 							=						output.images[0]

										assert image.shape == (5_12, 5_12, 3)
										assert np.abs(expected_image - image  ).max() < 5E-1



					def  lowerCAmelCase__       (      self  )->					Optional[Any]:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										UpperCAmelCase__							: Dict 							=						load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"  )

										UpperCAmelCase__							: Optional[Any] 							=						"stabilityai/stable-diffusion-x4-upscaler"
										UpperCAmelCase__							: Optional[Any] 							=						StableDiffusionUpscalePipeline.from_pretrained(
										    _UpperCAmelCase ,			torch_dtype=torch.floataa ,			)
										pipe.to(_UpperCAmelCase  )
										pipe.set_progress_bar_config(disable=_UpperCAmelCase  )
										pipe.enable_attention_slicing(1  )
										pipe.enable_sequential_cpu_offload()

										UpperCAmelCase__							: int 							=						"a cat sitting on a park bench"

										UpperCAmelCase__							: Any 							=						torch.manual_seed(0  )
										UpperCAmelCase__							: Dict 							=						pipe(
										    prompt=_UpperCAmelCase ,			image=_UpperCAmelCase ,			generator=_UpperCAmelCase ,			num_inference_steps=5 ,			output_type="np" ,			)

										UpperCAmelCase__							: Any 							=						torch.cuda.max_memory_allocated()
										# make sure that less than 2.9 GB is allocated
										assert mem_bytes < 2.9 * 10**9





"""simple docstring"""




from math import isqrt



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						[True] * max_number
     for i in range(2       ,    isqrt(max_number - 1		) + 1		):
          if is_prime[i]:
               for j in range(i**2       ,    lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: List[Any] 							=						False

     return [i for i in range(2       ,    lowerCAmelCase		) if is_prime[i]]



def 							a__ (				lowerCAmelCase	:       int = 10**8		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						calculate_prime_numbers(max_number // 2		)

     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Tuple 							=						len(lowerCAmelCase		) - 1
     while left <= right:
          while prime_numbers[left] * prime_numbers[right] >= max_number:
               right -= 1
          semiprimes_count += right - left + 1
          left += 1

     return semiprimes_count


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



