





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 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				1
				UpperCAmelCase   		=				3
				UpperCAmelCase   		=				(3_2, 3_2)

				UpperCAmelCase   		=				floats_tensor((batch_size, num_channels) + sizes							,					rng=random.Random(0		)		).to(_A		)
				return image
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				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=_A							,					only_cross_attention=(True, True, False)							,					num_class_embeds=1_0_0							,					)
				return model
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				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      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				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(_A		)
	def      _lowercase				(    self		):






				'''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(_A		)		).convert('''RGB'''		).resize((6_4, 6_4)		)

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

				UpperCAmelCase   		=				'''A painting of a squirrel eating a burger'''
				UpperCAmelCase   		=				torch.Generator(device=_A		).manual_seed(0		)
				UpperCAmelCase   		=				sd_pipe(
				    [prompt]							,					image=_A							,					generator=_A							,					guidance_scale=6.0							,					noise_level=2_0							,					num_inference_steps=2							,					output_type='''np'''							,					)

				UpperCAmelCase   		=				output.images

				UpperCAmelCase   		=				torch.Generator(device=_A		).manual_seed(0		)
				UpperCAmelCase   		=				sd_pipe(
				    [prompt]							,					image=_A							,					generator=_A							,					guidance_scale=6.0							,					noise_level=2_0							,					num_inference_steps=2							,					output_type='''np'''							,					return_dict=_A							,					)[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      _lowercase				(    self		):






				'''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(_A		)		).convert('''RGB'''		).resize((6_4, 6_4)		)

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

				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=2_0							,					num_inference_steps=2							,					output_type='''np'''							,					)
				UpperCAmelCase   		=				output.images
				assert image.shape[0] == 2

				UpperCAmelCase   		=				torch.Generator(device=_A		).manual_seed(0		)
				UpperCAmelCase   		=				sd_pipe(
				    [prompt]							,					image=_A							,					generator=_A							,					num_images_per_prompt=2							,					guidance_scale=6.0							,					noise_level=2_0							,					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      _lowercase				(    self		):






				'''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(_A		)		).convert('''RGB'''		).resize((6_4, 6_4)		)

				# 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=_A							,					low_res_scheduler=_A							,					scheduler=_A							,					vae=_A							,					text_encoder=_A							,					tokenizer=_A							,					max_noise_level=3_5_0							,					)
				UpperCAmelCase   		=				sd_pipe.to(_A		)
				sd_pipe.set_progress_bar_config(disable=_A		)

				UpperCAmelCase   		=				'''A painting of a squirrel eating a burger'''
				UpperCAmelCase   		=				torch.manual_seed(0		)
				UpperCAmelCase   		=				sd_pipe(
				    [prompt]							,					image=_A							,					generator=_A							,					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 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
	def      _lowercase				(    self		):






				'''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(_A		)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)
				pipe.enable_attention_slicing()

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

				UpperCAmelCase   		=				torch.manual_seed(0		)
				UpperCAmelCase   		=				pipe(
				    prompt=_A							,					image=_A							,					generator=_A							,					output_type='''np'''							,					)
				UpperCAmelCase   		=				output.images[0]

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






				'''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(
				    _A							,					torch_dtype=torch.floataa							,					)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)
				pipe.enable_attention_slicing()

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

				UpperCAmelCase   		=				torch.manual_seed(0		)
				UpperCAmelCase   		=				pipe(
				    prompt=_A							,					image=_A							,					generator=_A							,					output_type='''np'''							,					)
				UpperCAmelCase   		=				output.images[0]

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







	def      _lowercase				(    self		):






				'''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(
				    _A							,					torch_dtype=torch.floataa							,					)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)
				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=_A							,					image=_A							,					generator=_A							,					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 * 1_0**9







'''simple docstring'''

import math





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  math.loga(math.sqrt(4 * positive_integer + 1		) / 2 + 1 / 2		)

 return exponent == int(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						float = 1 / 12345		):
 lowerCamelCase_      =  0
 lowerCamelCase_      =  0

 lowerCamelCase_      =  3
 while True:
  lowerCamelCase_      =  (integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(UpperCAmelCase_		):
   lowerCamelCase_      =  int(UpperCAmelCase_		)
   total_partitions += 1
   if check_partition_perfect(UpperCAmelCase_		):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(UpperCAmelCase_		)
  integer += 1


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



