

import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def 							__UpperCamelCase       (	self							:       Any		)							->				Optional[Any]:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			@property
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				1
				SCREAMING_SNAKE_CASE      :					Any    =				3
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(32, 32)

				SCREAMING_SNAKE_CASE      :					Any    =				floats_tensor((batch_size, num_channels) + sizes					,					rng=random.Random(0		)		).to(a		)
				return image
			@property
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Tuple:

				"""simple docstring"""

				torch.manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				UNetaDConditionModel(
				    block_out_channels=(32, 64)					,					layers_per_block=2					,					sample_size=32					,					in_channels=4					,					out_channels=4					,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,					cross_attention_dim=32					,					)
				return model
			@property
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				torch.manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				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=1000					,					)
				return CLIPTextModel(a		)
			@property
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[str]:

				"""simple docstring"""

				def extract(*a							:       List[Any]					,					**a							:       List[str]		):






					class _UpperCamelCase							:
								'''simple docstring'''







								def __init__(	self							:       Dict		)							->				Any:

									"""simple docstring"""

									SCREAMING_SNAKE_CASE      :					str    =				torch.ones([0]		)






								def 							__UpperCamelCase       (	self							:       Union[str, Any]					,					a							:       Optional[Any]		)							->				Tuple:

									"""simple docstring"""

									self.pixel_values.to(a		)
									return self

					return Out()

				return extract
			def 							__UpperCamelCase       (	self							:       Dict		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.dummy_cond_unet
				SCREAMING_SNAKE_CASE      :					List[Any]    =				DDIMScheduler(
				    beta_start=0.0_0085					,					beta_end=0.012					,					beta_schedule="scaled_linear"					,					clip_sample=a					,					set_alpha_to_one=a					,					)

				SCREAMING_SNAKE_CASE      :					Any    =				self.dummy_vae
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.dummy_text_encoder
				SCREAMING_SNAKE_CASE      :					Dict    =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

				# make sure here that pndm scheduler skips prk
				SCREAMING_SNAKE_CASE      :					Dict    =				StableDiffusionPipeline(
				    unet=a					,					scheduler=a					,					vae=a					,					text_encoder=a					,					tokenizer=a					,					safety_checker=a					,					feature_extractor=self.dummy_extractor					,					)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				sd_pipe.to(a		)
				sd_pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					str    =				"A painting of a squirrel eating a burger"

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				torch.Generator(device=a		).manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					Any    =				sd_pipe([prompt]					,					generator=a					,					guidance_scale=6.0					,					num_inference_steps=2					,					output_type="np"		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				output.images

				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.Generator(device=a		).manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					str    =				sd_pipe(
				    [prompt]					,					generator=a					,					guidance_scale=6.0					,					num_inference_steps=2					,					output_type="np"					,					return_dict=a					,					)[0]

				SCREAMING_SNAKE_CASE      :					int    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					int    =				image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]		)

				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							:       int		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.dummy_cond_unet
				SCREAMING_SNAKE_CASE      :					Tuple    =				PNDMScheduler(skip_prk_steps=a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.dummy_vae
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.dummy_text_encoder
				SCREAMING_SNAKE_CASE      :					int    =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

				# make sure here that pndm scheduler skips prk
				SCREAMING_SNAKE_CASE      :					Any    =				StableDiffusionPipeline(
				    unet=a					,					scheduler=a					,					vae=a					,					text_encoder=a					,					tokenizer=a					,					safety_checker=a					,					feature_extractor=self.dummy_extractor					,					)
				SCREAMING_SNAKE_CASE      :					Tuple    =				sd_pipe.to(a		)
				sd_pipe.set_progress_bar_config(disable=a		)

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

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				output.images

				SCREAMING_SNAKE_CASE      :					Any    =				torch.Generator(device=a		).manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					int    =				sd_pipe(
				    [prompt]					,					generator=a					,					guidance_scale=6.0					,					num_inference_steps=2					,					output_type="np"					,					return_dict=a					,					)[0]

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					List[str]    =				image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]		)

				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							:       List[Any]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				StableDiffusionPipeline.from_pretrained(
				    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"					,					safety_checker=a		)
				assert isinstance(a					,					a		)
				assert isinstance(pipe.scheduler					,					a		)
				assert pipe.safety_checker is None

				SCREAMING_SNAKE_CASE      :					List[Any]    =				pipe("example prompt"					,					num_inference_steps=2		).images[0]
				assert image is not None

				# check that there's no error when saving a pipeline with one of the models being None
				with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(a		)
					SCREAMING_SNAKE_CASE      :					Tuple    =				StableDiffusionPipeline.from_pretrained(a		)

				# sanity check that the pipeline still works
				assert pipe.safety_checker is None
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				pipe("example prompt"					,					num_inference_steps=2		).images[0]
				assert image is not None






			@unittest.skipIf(torch_device != "cuda"					,					"This test requires a GPU"		)
			def 							__UpperCamelCase       (	self							:       str		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.dummy_cond_unet
				SCREAMING_SNAKE_CASE      :					List[str]    =				PNDMScheduler(skip_prk_steps=a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.dummy_vae
				SCREAMING_SNAKE_CASE      :					List[str]    =				self.dummy_text_encoder
				SCREAMING_SNAKE_CASE      :					List[str]    =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

				# put models in fp16
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				unet.half()
				SCREAMING_SNAKE_CASE      :					str    =				vae.half()
				SCREAMING_SNAKE_CASE      :					Dict    =				bert.half()

				# make sure here that pndm scheduler skips prk
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				StableDiffusionPipeline(
				    unet=a					,					scheduler=a					,					vae=a					,					text_encoder=a					,					tokenizer=a					,					safety_checker=a					,					feature_extractor=self.dummy_extractor					,					)
				SCREAMING_SNAKE_CASE      :					Any    =				sd_pipe.to(a		)
				sd_pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					str    =				"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE      :					Any    =				sd_pipe([prompt]					,					num_inference_steps=2					,					output_type="np"		).images

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






@nightly
@require_torch_gpu
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				List[Any]:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 							__UpperCamelCase       (	self							:       Dict		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"					,					safety_checker=a		)
				SCREAMING_SNAKE_CASE      :					Any    =				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
				SCREAMING_SNAKE_CASE      :					int    =				sd_pipe.to(a		)
				sd_pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(
				    "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
				    " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
				    " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
				    " children from bahnhof zoo, detailed "
				)
				SCREAMING_SNAKE_CASE      :					Tuple    =				40_0366_0346
				SCREAMING_SNAKE_CASE      :					Dict    =				7

				# without safety guidance (sld_guidance_scale = 0)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				torch.manual_seed(a		)
				SCREAMING_SNAKE_CASE      :					int    =				sd_pipe(
				    [prompt]					,					generator=a					,					guidance_scale=a					,					num_inference_steps=50					,					output_type="np"					,					width=512					,					height=512					,					sld_guidance_scale=0					,					)

				SCREAMING_SNAKE_CASE      :					Any    =				output.images
				SCREAMING_SNAKE_CASE      :					str    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					List[str]    =				[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

				assert image.shape == (1, 512, 512, 3)

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

				# without safety guidance (strong configuration)
				SCREAMING_SNAKE_CASE      :					int    =				torch.manual_seed(a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				sd_pipe(
				    [prompt]					,					generator=a					,					guidance_scale=a					,					num_inference_steps=50					,					output_type="np"					,					width=512					,					height=512					,					sld_guidance_scale=2000					,					sld_warmup_steps=7					,					sld_threshold=0.025					,					sld_momentum_scale=0.5					,					sld_mom_beta=0.7					,					)

				SCREAMING_SNAKE_CASE      :					str    =				output.images
				SCREAMING_SNAKE_CASE      :					Dict    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					Any    =				[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"					,					safety_checker=a		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				sd_pipe.to(a		)
				sd_pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					str    =				"padme amidala taking a bath artwork, safe for work, no nudity"
				SCREAMING_SNAKE_CASE      :					List[Any]    =				27_3497_1755
				SCREAMING_SNAKE_CASE      :					List[Any]    =				7

				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.manual_seed(a		)
				SCREAMING_SNAKE_CASE      :					Dict    =				sd_pipe(
				    [prompt]					,					generator=a					,					guidance_scale=a					,					num_inference_steps=50					,					output_type="np"					,					width=512					,					height=512					,					sld_guidance_scale=0					,					)

				SCREAMING_SNAKE_CASE      :					Dict    =				output.images
				SCREAMING_SNAKE_CASE      :					List[str]    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					List[str]    =				[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

				assert image.shape == (1, 512, 512, 3)

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

				SCREAMING_SNAKE_CASE      :					int    =				torch.manual_seed(a		)
				SCREAMING_SNAKE_CASE      :					str    =				sd_pipe(
				    [prompt]					,					generator=a					,					guidance_scale=a					,					num_inference_steps=50					,					output_type="np"					,					width=512					,					height=512					,					sld_guidance_scale=2000					,					sld_warmup_steps=7					,					sld_threshold=0.025					,					sld_momentum_scale=0.5					,					sld_mom_beta=0.7					,					)

				SCREAMING_SNAKE_CASE      :					Tuple    =				output.images
				SCREAMING_SNAKE_CASE      :					Any    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					Tuple    =				[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

				assert image.shape == (1, 512, 512, 3)

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






			def 							__UpperCamelCase       (	self							:       Dict		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				sd_pipe.to(a		)
				sd_pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					int    =				(
				    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
				    " leyendecker"
				)
				SCREAMING_SNAKE_CASE      :					int    =				10_4435_5234
				SCREAMING_SNAKE_CASE      :					Tuple    =				12

				SCREAMING_SNAKE_CASE      :					Any    =				torch.manual_seed(a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				sd_pipe(
				    [prompt]					,					generator=a					,					guidance_scale=a					,					num_inference_steps=50					,					output_type="np"					,					width=512					,					height=512					,					sld_guidance_scale=0					,					)

				SCREAMING_SNAKE_CASE      :					int    =				output.images
				SCREAMING_SNAKE_CASE      :					List[str]    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					Dict    =				np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]		)

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-7

				SCREAMING_SNAKE_CASE      :					int    =				torch.manual_seed(a		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				sd_pipe(
				    [prompt]					,					generator=a					,					guidance_scale=a					,					num_inference_steps=50					,					output_type="np"					,					width=512					,					height=512					,					sld_guidance_scale=2000					,					sld_warmup_steps=7					,					sld_threshold=0.025					,					sld_momentum_scale=0.5					,					sld_mom_beta=0.7					,					)

				SCREAMING_SNAKE_CASE      :					Tuple    =				output.images
				SCREAMING_SNAKE_CASE      :					Tuple    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]		)
				assert image.shape == (1, 512, 512, 3)

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




'''simple docstring'''


def 				__lowerCamelCase				(   A__ = 10**9    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  1
   UpperCamelCase					=  2

   UpperCamelCase					=  0
   UpperCamelCase					=  0
   UpperCamelCase					=  0
   while perimeter <= max_perimeter:
      perimeters_sum += perimeter

      prev_value += 2 * value
      value += prev_value

      UpperCamelCase					=  2 * value + 2 if i % 2 == 0 else 2 * value - 2
      i += 1

   return perimeters_sum


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

