"""simple docstring"""






import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[tensor.shape for tensor in tensor_list]
				return all(shape == shapes[0] for shape in shapes[1:] )
class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				StableDiffusionLatentUpscalePipeline
					snake_case__       					=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
					    "height",
					    "width",
					    "cross_attention_kwargs",
					    "negative_prompt_embeds",
					    "prompt_embeds",
					}
					snake_case__       					=				PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
					snake_case__       					=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					snake_case__       					=				frozenset(
					    []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					snake_case__       					=				frozenset([]       )

					snake_case__       					=				True







					@property
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							1
									UpperCAmelCase__    =							4
									UpperCAmelCase__    =							(16, 16)

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







					def 	__lowerCAmelCase		(       self     :			str    ):
									torch.manual_seed(0    )
									UpperCAmelCase__    =							UNetaDConditionModel(
									    act_fn='gelu'					,attention_head_dim=8					,norm_num_groups=lowerCamelCase__					,block_out_channels=[32, 32, 64, 64]					,time_cond_proj_dim=160					,conv_in_kernel=1					,conv_out_kernel=1					,cross_attention_dim=32					,down_block_types=(
									        'KDownBlock2D',
									        'KCrossAttnDownBlock2D',
									        'KCrossAttnDownBlock2D',
									        'KCrossAttnDownBlock2D',
									    )					,in_channels=8					,mid_block_type=lowerCamelCase__					,only_cross_attention=lowerCamelCase__					,out_channels=5					,resnet_time_scale_shift='scale_shift'					,time_embedding_type='fourier'					,timestep_post_act='gelu'					,up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')					,)
									UpperCAmelCase__    =							AutoencoderKL(
									    block_out_channels=[32, 32, 64, 64]					,in_channels=3					,out_channels=3					,down_block_types=[
									        'DownEncoderBlock2D',
									        'DownEncoderBlock2D',
									        'DownEncoderBlock2D',
									        'DownEncoderBlock2D',
									    ]					,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']					,latent_channels=4					,)
									UpperCAmelCase__    =							EulerDiscreteScheduler(prediction_type='sample'    )
									UpperCAmelCase__    =							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=1_000					,hidden_act='quick_gelu'					,projection_dim=512					,)
									UpperCAmelCase__    =							CLIPTextModel(lowerCamelCase__    )
									UpperCAmelCase__    =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

									UpperCAmelCase__    =							{
									    'unet': model.eval(),
									    'vae': vae.eval(),
									    'scheduler': scheduler,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									}

									return components







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Dict=0    ):
									if str(lowerCamelCase__    ).startswith('mps'    ):
													UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
									else:
													UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': self.dummy_image.cpu(),
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}
									return inputs







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							'cpu'

									UpperCAmelCase__    =							self.get_dummy_components()
									UpperCAmelCase__    =							self.pipeline_class(**lowerCamelCase__    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							self.get_dummy_inputs(lowerCamelCase__    )
									UpperCAmelCase__    =							pipe(**lowerCamelCase__    ).images
									UpperCAmelCase__    =							image[0, -3:, -3:, -1]

									self.assertEqual(image.shape					,(1, 256, 256, 3)    )
									UpperCAmelCase__    =							np.array(
									    [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]    )
									UpperCAmelCase__    =							np.abs(image_slice.flatten() - expected_slice    ).max()
									self.assertLessEqual(lowerCamelCase__					,1e-3    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									super().test_attention_slicing_forward_pass(expected_max_diff=7e-3    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									super().test_cpu_offload_forward_pass(expected_max_diff=3e-3    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									super().test_inference_batch_single_identical(expected_max_diff=7e-3    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									super().test_save_load_local(expected_max_difference=3e-3    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									super().test_save_load_optional_components(expected_max_difference=3e-3    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							[
									    'DDIMScheduler',
									    'DDPMScheduler',
									    'PNDMScheduler',
									    'HeunDiscreteScheduler',
									    'EulerAncestralDiscreteScheduler',
									    'KDPM2DiscreteScheduler',
									    'KDPM2AncestralDiscreteScheduler',
									    'DPMSolverSDEScheduler',
									]
									UpperCAmelCase__    =							self.get_dummy_components()
									UpperCAmelCase__    =							self.pipeline_class(**lowerCamelCase__    )

									# make sure that PNDM does not need warm-up
									pipe.scheduler.register_to_config(skip_prk_steps=lowerCamelCase__    )

									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )
									UpperCAmelCase__    =							self.get_dummy_inputs(lowerCamelCase__    )
									UpperCAmelCase__    =							2

									UpperCAmelCase__    =							[]
									for scheduler_enum in KarrasDiffusionSchedulers:
													if scheduler_enum.name in skip_schedulers:
																	# no sigma schedulers are not supported
																	# no schedulers
																	continue

													UpperCAmelCase__    =							getattr(lowerCamelCase__					,scheduler_enum.name    )
													UpperCAmelCase__    =							scheduler_cls.from_config(pipe.scheduler.config    )
													UpperCAmelCase__    =							pipe(**lowerCamelCase__    )[0]
													outputs.append(lowerCamelCase__    )

									assert check_same_shape(lowerCamelCase__    )






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




					"""simple docstring"""

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







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							torch.manual_seed(33    )

									UpperCAmelCase__    =							StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'					,torch_dtype=torch.floataa    )
									pipe.to('cuda'    )

									UpperCAmelCase__    =							StableDiffusionLatentUpscalePipeline.from_pretrained(
									    'stabilityai/sd-x2-latent-upscaler'					,torch_dtype=torch.floataa    )
									upscaler.to('cuda'    )

									UpperCAmelCase__    =							'a photo of an astronaut high resolution, unreal engine, ultra realistic'

									UpperCAmelCase__    =							pipe(lowerCamelCase__					,generator=lowerCamelCase__					,output_type='latent'    ).images

									UpperCAmelCase__    =							upscaler(
									    prompt=lowerCamelCase__					,image=lowerCamelCase__					,num_inference_steps=20					,guidance_scale=0					,generator=lowerCamelCase__					,output_type='np'					,).images[0]

									UpperCAmelCase__    =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'    )
									assert np.abs((expected_image - image).mean()    ) < 5e-2







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							torch.manual_seed(33    )

									UpperCAmelCase__    =							StableDiffusionLatentUpscalePipeline.from_pretrained(
									    'stabilityai/sd-x2-latent-upscaler'					,torch_dtype=torch.floataa    )
									upscaler.to('cuda'    )

									UpperCAmelCase__    =							'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'    )

									UpperCAmelCase__    =							upscaler(
									    prompt=lowerCamelCase__					,image=lowerCamelCase__					,num_inference_steps=20					,guidance_scale=0					,generator=lowerCamelCase__					,output_type='np'					,).images[0]

									UpperCAmelCase__    =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'    )
									assert np.abs((expected_image - image).max()    ) < 5e-2




"""simple docstring"""






from __future__ import annotations

from math import ceil, floor, sqrt



def 						a_		(      lowerCamelCase = 2_0_0_0_0_0_0 ):
				UpperCAmelCase__    =							[0]
				UpperCAmelCase__    =							42

				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
				UpperCAmelCase__    =							0
				# the area corresponding to the grid that gives the product closest to target
				UpperCAmelCase__    =							0
				# an estimate of b, using the quadratic formula
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_floor
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_ceil
				UpperCAmelCase__    =							42

				for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,    1 ):
								UpperCAmelCase__    =							(-1 + sqrt(1 + 8 * target / triangle_a )) / 2
								UpperCAmelCase__    =							floor(lowerCamelCase )
								UpperCAmelCase__    =							ceil(lowerCamelCase )
								UpperCAmelCase__    =							triangle_numbers[b_floor]
								UpperCAmelCase__    =							triangle_numbers[b_ceil]

								if abs(target - triangle_b_first_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_first_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_floor

								if abs(target - triangle_b_second_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_second_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_ceil

				return area


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



