





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 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Dict:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




       @property
       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							1
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							3
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							(3_2, 3_2)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							floats_tensor((batch_size, num_channels) + sizes    ,						rng=random.Random(0							)							).to(__lowerCamelCase							)
            return image




       @property
       def     UpperCamelCase_			(      self							)			->     Any:
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							UNetaDConditionModel(
                block_out_channels=(3_2, 3_2, 6_4)    ,						layers_per_block=2    ,						sample_size=3_2    ,						in_channels=7    ,						out_channels=4    ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")    ,						up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")    ,						cross_attention_dim=3_2    ,						attention_head_dim=8    ,						use_linear_projection=__lowerCamelCase    ,						only_cross_attention=(True, True, False)    ,						num_class_embeds=1_0_0    ,						)
            return model




       @property
       def     UpperCamelCase_			(      self							)			->     List[str]:
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							AutoencoderKL(
                block_out_channels=[3_2, 3_2, 6_4]    ,						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     UpperCamelCase_			(      self							)			->     Tuple:
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							CLIPTextConfig(
                bos_token_id=0    ,						eos_token_id=2    ,						hidden_size=3_2    ,						intermediate_size=3_7    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						pad_token_id=1    ,						vocab_size=1_0_0_0    ,						hidden_act="gelu"    ,						projection_dim=5_1_2    ,						)
            return CLIPTextModel(__lowerCamelCase							)




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

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.dummy_image.cpu().permute(0    ,						2    ,						3    ,						1							)[0]
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							Image.fromarray(np.uinta(__lowerCamelCase							)							).convert("RGB"							).resize((6_4, 6_4)							)

            # make sure here that pndm scheduler skips prk
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							StableDiffusionUpscalePipeline(
                unet=__lowerCamelCase    ,						low_res_scheduler=__lowerCamelCase    ,						scheduler=__lowerCamelCase    ,						vae=__lowerCamelCase    ,						text_encoder=__lowerCamelCase    ,						tokenizer=__lowerCamelCase    ,						max_noise_level=3_5_0    ,						)
            _SCREAMING_SNAKE_CASE       :	str						    =							sd_pipe.to(__lowerCamelCase							)
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"A painting of a squirrel eating a burger"
            _SCREAMING_SNAKE_CASE       :	Any						    =							torch.Generator(device=__lowerCamelCase							).manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							sd_pipe(
                [prompt]    ,						image=__lowerCamelCase    ,						generator=__lowerCamelCase    ,						guidance_scale=6.0    ,						noise_level=2_0    ,						num_inference_steps=2    ,						output_type="np"    ,						)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							output.images

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							torch.Generator(device=__lowerCamelCase							).manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							sd_pipe(
                [prompt]    ,						image=__lowerCamelCase    ,						generator=__lowerCamelCase    ,						guidance_scale=6.0    ,						noise_level=2_0    ,						num_inference_steps=2    ,						output_type="np"    ,						return_dict=__lowerCamelCase    ,						)[0]

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							image[0, -3:, -3:, -1]
            _SCREAMING_SNAKE_CASE       :	int						    =							image_from_tuple[0, -3:, -3:, -1]

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							low_res_image.size[0] * 4
            assert image.shape == (1, expected_height_width, expected_height_width, 3)
            _SCREAMING_SNAKE_CASE       :	int						    =							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     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							"cpu"  # ensure determinism for the device-dependent torch.Generator
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.dummy_cond_unet_upscale
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							DDPMScheduler()
            _SCREAMING_SNAKE_CASE       :	int						    =							DDIMScheduler(prediction_type="v_prediction"							)
            _SCREAMING_SNAKE_CASE       :	str						    =							self.dummy_vae
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.dummy_text_encoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.dummy_image.cpu().permute(0    ,						2    ,						3    ,						1							)[0]
            _SCREAMING_SNAKE_CASE       :	Any						    =							Image.fromarray(np.uinta(__lowerCamelCase							)							).convert("RGB"							).resize((6_4, 6_4)							)

            # make sure here that pndm scheduler skips prk
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							StableDiffusionUpscalePipeline(
                unet=__lowerCamelCase    ,						low_res_scheduler=__lowerCamelCase    ,						scheduler=__lowerCamelCase    ,						vae=__lowerCamelCase    ,						text_encoder=__lowerCamelCase    ,						tokenizer=__lowerCamelCase    ,						max_noise_level=3_5_0    ,						)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							sd_pipe.to(__lowerCamelCase							)
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							"A painting of a squirrel eating a burger"
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							sd_pipe(
                2 * [prompt]    ,						image=2 * [low_res_image]    ,						guidance_scale=6.0    ,						noise_level=2_0    ,						num_inference_steps=2    ,						output_type="np"    ,						)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							output.images
            assert image.shape[0] == 2

            _SCREAMING_SNAKE_CASE       :	str						    =							torch.Generator(device=__lowerCamelCase							).manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							sd_pipe(
                [prompt]    ,						image=__lowerCamelCase    ,						generator=__lowerCamelCase    ,						num_images_per_prompt=2    ,						guidance_scale=6.0    ,						noise_level=2_0    ,						num_inference_steps=2    ,						output_type="np"    ,						)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							output.images
            assert image.shape[0] == 2







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

            _SCREAMING_SNAKE_CASE       :	str						    =							self.dummy_image.cpu().permute(0    ,						2    ,						3    ,						1							)[0]
            _SCREAMING_SNAKE_CASE       :	int						    =							Image.fromarray(np.uinta(__lowerCamelCase							)							).convert("RGB"							).resize((6_4, 6_4)							)

            # put models in fp16, except vae as it overflows in fp16
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							unet.half()
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							text_encoder.half()

            # make sure here that pndm scheduler skips prk
            _SCREAMING_SNAKE_CASE       :	Any						    =							StableDiffusionUpscalePipeline(
                unet=__lowerCamelCase    ,						low_res_scheduler=__lowerCamelCase    ,						scheduler=__lowerCamelCase    ,						vae=__lowerCamelCase    ,						text_encoder=__lowerCamelCase    ,						tokenizer=__lowerCamelCase    ,						max_noise_level=3_5_0    ,						)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							sd_pipe.to(__lowerCamelCase							)
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	int						    =							"A painting of a squirrel eating a burger"
            _SCREAMING_SNAKE_CASE       :	Any						    =							torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	int						    =							sd_pipe(
                [prompt]    ,						image=__lowerCamelCase    ,						generator=__lowerCamelCase    ,						num_inference_steps=2    ,						output_type="np"    ,						).images

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							low_res_image.size[0] * 4
            assert image.shape == (1, expected_height_width, expected_height_width, 3)





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






       '''simple docstring'''


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




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							load_image(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                "/sd2-upscale/low_res_cat.png"							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
                "/upsampled_cat.npy"							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"stabilityai/stable-diffusion-x4-upscaler"
            _SCREAMING_SNAKE_CASE       :	Dict						    =							StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase							)
            pipe.to(__lowerCamelCase							)
            pipe.set_progress_bar_config(disable=__lowerCamelCase							)
            pipe.enable_attention_slicing()

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"a cat sitting on a park bench"

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							pipe(
                prompt=__lowerCamelCase    ,						image=__lowerCamelCase    ,						generator=__lowerCamelCase    ,						output_type="np"    ,						)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							output.images[0]

            assert image.shape == (5_1_2, 5_1_2, 3)
            assert np.abs(expected_image - image							).max() < 1E-3




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							load_image(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                "/sd2-upscale/low_res_cat.png"							)
            _SCREAMING_SNAKE_CASE       :	str						    =							load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
                "/upsampled_cat_fp16.npy"							)

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

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

            _SCREAMING_SNAKE_CASE       :	Any						    =							torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							pipe(
                prompt=__lowerCamelCase    ,						image=__lowerCamelCase    ,						generator=__lowerCamelCase    ,						output_type="np"    ,						)
            _SCREAMING_SNAKE_CASE       :	int						    =							output.images[0]

            assert image.shape == (5_1_2, 5_1_2, 3)
            assert np.abs(expected_image - image							).max() < 5E-1







       def     UpperCamelCase_			(      self							)			->     str:
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

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

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

            _SCREAMING_SNAKE_CASE       :	Any						    =							"a cat sitting on a park bench"

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	str						    =							pipe(
                prompt=__lowerCamelCase    ,						image=__lowerCamelCase    ,						generator=__lowerCamelCase    ,						num_inference_steps=5    ,						output_type="np"    ,						)

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.cuda.max_memory_allocated()
            # make sure that less than 2.9 GB is allocated
            assert mem_bytes < 2.9 * 1_0**9






def     lowerCamelCase__			(__lowerCamelCase = 10**9	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							1
     _SCREAMING_SNAKE_CASE       :	Any						    =							2

     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							0
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							0
     while perimeter <= max_perimeter:
          perimeters_sum += perimeter

          prev_value += 2 * value
          value += prev_value

          _SCREAMING_SNAKE_CASE       :	Tuple						    =							2 * value + 2 if i % 2 == 0 else 2 * value - 2
          i += 1

     return perimeters_sum


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