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 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any])						->	str:


   """simple docstring"""







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







 @property
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    1
   _UpperCamelCase           =    3
   _UpperCamelCase           =    (32, 32)

   _UpperCamelCase           =    floats_tensor((batch_size, num_channels) + sizes							, rng=random.Random(0)).to(UpperCamelCase__)
   return image







 @property
 def 		__UpperCAmelCase				(		self				:   Any)						->	List[str]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    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=100							, )
   return model







 @property
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Dict:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    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 		__UpperCAmelCase				(		self				:   Any)						->	int:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    CLIPTextConfig(
       bos_token_id=0							, eos_token_id=2							, hidden_size=32							, intermediate_size=37							, layer_norm_eps=1e-0_5							, num_attention_heads=4							, num_hidden_layers=5							, pad_token_id=1							, vocab_size=1000							, hidden_act="gelu"							, projection_dim=512							, )
   return CLIPTextModel(UpperCamelCase__)







 def 		__UpperCAmelCase				(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase           =    self.dummy_cond_unet_upscale
   _UpperCamelCase           =    DDPMScheduler()
   _UpperCamelCase           =    DDIMScheduler(prediction_type="v_prediction")
   _UpperCamelCase           =    self.dummy_vae
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   _UpperCamelCase           =    self.dummy_image.cpu().permute(0							, 2							, 3							, 1)[0]
   _UpperCamelCase           =    Image.fromarray(np.uinta(UpperCamelCase__)).convert("RGB").resize((64, 64))

   # make sure here that pndm scheduler skips prk
   _UpperCamelCase           =    StableDiffusionUpscalePipeline(
       unet=UpperCamelCase__							, low_res_scheduler=UpperCamelCase__							, scheduler=UpperCamelCase__							, vae=UpperCamelCase__							, text_encoder=UpperCamelCase__							, tokenizer=UpperCamelCase__							, max_noise_level=350							, )
   _UpperCamelCase           =    sd_pipe.to(UpperCamelCase__)
   sd_pipe.set_progress_bar_config(disable=UpperCamelCase__)

   _UpperCamelCase           =    '''A painting of a squirrel eating a burger'''
   _UpperCamelCase           =    torch.Generator(device=UpperCamelCase__).manual_seed(0)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, image=UpperCamelCase__							, generator=UpperCamelCase__							, guidance_scale=6.0							, noise_level=20							, num_inference_steps=2							, output_type="np"							, )

   _UpperCamelCase           =    output.images

   _UpperCamelCase           =    torch.Generator(device=UpperCamelCase__).manual_seed(0)
   _UpperCamelCase           =    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           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    image_from_tuple[0, -3:, -3:, -1]

   _UpperCamelCase           =    low_res_image.size[0] * 4
   assert image.shape == (1, expected_height_width, expected_height_width, 3)
   _UpperCamelCase           =    np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61])

   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[str])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase           =    self.dummy_cond_unet_upscale
   _UpperCamelCase           =    DDPMScheduler()
   _UpperCamelCase           =    DDIMScheduler(prediction_type="v_prediction")
   _UpperCamelCase           =    self.dummy_vae
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   _UpperCamelCase           =    self.dummy_image.cpu().permute(0							, 2							, 3							, 1)[0]
   _UpperCamelCase           =    Image.fromarray(np.uinta(UpperCamelCase__)).convert("RGB").resize((64, 64))

   # make sure here that pndm scheduler skips prk
   _UpperCamelCase           =    StableDiffusionUpscalePipeline(
       unet=UpperCamelCase__							, low_res_scheduler=UpperCamelCase__							, scheduler=UpperCamelCase__							, vae=UpperCamelCase__							, text_encoder=UpperCamelCase__							, tokenizer=UpperCamelCase__							, max_noise_level=350							, )
   _UpperCamelCase           =    sd_pipe.to(UpperCamelCase__)
   sd_pipe.set_progress_bar_config(disable=UpperCamelCase__)

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

   _UpperCamelCase           =    torch.Generator(device=UpperCamelCase__).manual_seed(0)
   _UpperCamelCase           =    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           =    output.images
   assert image.shape[0] == 2







 @unittest.skipIf(torch_device != "cuda"							, "This test requires a GPU")
 def 		__UpperCAmelCase				(		self				:   Dict)						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.dummy_cond_unet_upscale
   _UpperCamelCase           =    DDPMScheduler()
   _UpperCamelCase           =    DDIMScheduler(prediction_type="v_prediction")
   _UpperCamelCase           =    self.dummy_vae
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   _UpperCamelCase           =    self.dummy_image.cpu().permute(0							, 2							, 3							, 1)[0]
   _UpperCamelCase           =    Image.fromarray(np.uinta(UpperCamelCase__)).convert("RGB").resize((64, 64))

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

   # make sure here that pndm scheduler skips prk
   _UpperCamelCase           =    StableDiffusionUpscalePipeline(
       unet=UpperCamelCase__							, low_res_scheduler=UpperCamelCase__							, scheduler=UpperCamelCase__							, vae=UpperCamelCase__							, text_encoder=UpperCamelCase__							, tokenizer=UpperCamelCase__							, max_noise_level=350							, )
   _UpperCamelCase           =    sd_pipe.to(UpperCamelCase__)
   sd_pipe.set_progress_bar_config(disable=UpperCamelCase__)

   _UpperCamelCase           =    '''A painting of a squirrel eating a burger'''
   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, image=UpperCamelCase__							, generator=UpperCamelCase__							, num_inference_steps=2							, output_type="np"							, ).images

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




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



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	str:


   """simple docstring"""







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







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Dict:


   """simple docstring"""







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

   _UpperCamelCase           =    '''stabilityai/stable-diffusion-x4-upscaler'''
   _UpperCamelCase           =    StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase__)
   pipe.to(UpperCamelCase__)
   pipe.set_progress_bar_config(disable=UpperCamelCase__)
   pipe.enable_attention_slicing()

   _UpperCamelCase           =    '''a cat sitting on a park bench'''

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    pipe(
       prompt=UpperCamelCase__							, image=UpperCamelCase__							, generator=UpperCamelCase__							, output_type="np"							, )
   _UpperCamelCase           =    output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image).max() < 1e-3







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/sd2-upscale/low_res_cat.png")
   _UpperCamelCase           =    load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
       "/upsampled_cat_fp16.npy")

   _UpperCamelCase           =    '''stabilityai/stable-diffusion-x4-upscaler'''
   _UpperCamelCase           =    StableDiffusionUpscalePipeline.from_pretrained(
       UpperCamelCase__							, torch_dtype=torch.floataa							, )
   pipe.to(UpperCamelCase__)
   pipe.set_progress_bar_config(disable=UpperCamelCase__)
   pipe.enable_attention_slicing()

   _UpperCamelCase           =    '''a cat sitting on a park bench'''

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    pipe(
       prompt=UpperCamelCase__							, image=UpperCamelCase__							, generator=UpperCamelCase__							, output_type="np"							, )
   _UpperCamelCase           =    output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image).max() < 5e-1







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


   """simple docstring"""







   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

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

   _UpperCamelCase           =    '''stabilityai/stable-diffusion-x4-upscaler'''
   _UpperCamelCase           =    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           =    '''a cat sitting on a park bench'''

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    pipe(
       prompt=UpperCamelCase__							, image=UpperCamelCase__							, generator=UpperCamelCase__							, num_inference_steps=5							, output_type="np"							, )

   _UpperCamelCase           =    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 __future__ import annotations

from math import ceil, floor, sqrt




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = 200_0000			)  ->							int:


		'''simple docstring'''


		snake_case  :       list[int]     			=						[0]
		snake_case  :       int

		for idx in range(1 ,       ceil(sqrt(target * 2			) * 1.1			)			):
				triangle_numbers.append(triangle_numbers[-1] + idx			)

		# we want this to be as close as possible to target
		snake_case  :       int     			=						0
		# the area corresponding to the grid that gives the product closest to target
		snake_case  :       int     			=						0
		# an estimate of b, using the quadratic formula
		snake_case  :       float
		# the largest integer less than b_estimate
		snake_case  :       int
		# the largest integer less than b_estimate
		snake_case  :       int
		# the triangle number corresponding to b_floor
		snake_case  :       int
		# the triangle number corresponding to b_ceil
		snake_case  :       int

		for idx_a, triangle_a in enumerate(triangle_numbers[1:] ,       1			):
				snake_case  :       int     			=						(-1 + sqrt(1 + 8 * target / triangle_a			)) / 2
				snake_case  :       Optional[int]     			=						floor(SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						ceil(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Optional[Any]     			=						triangle_numbers[b_floor]
				snake_case  :       Union[str, Any]     			=						triangle_numbers[b_ceil]

				if abs(target - triangle_b_first_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       str     			=						triangle_b_first_guess * triangle_a
						snake_case  :       int     			=						idx_a * b_floor

				if abs(target - triangle_b_second_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       List[Any]     			=						triangle_b_second_guess * triangle_a
						snake_case  :       Any     			=						idx_a * b_ceil

		return area


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


