
'''simple docstring'''


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__lowercase							:   Union[str, Any]					=     logging.get_logger(__name__)

enable_full_determinism()

class 	__lowercase				(  snake_case__      ,     snake_case__      ,     unittest.TestCase    ):
			lowerCamelCase			:		Any	      =			UNetaDModel
			lowerCamelCase			:		Optional[int]	      =			"sample"

			@property
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		4
								lowerCamelCase_					:      int     =		3
								lowerCamelCase_					:      Dict     =		(3_2, 3_2)

								lowerCamelCase_					:      str     =		floats_tensor((batch_size, num_channels) + sizes      ).to(lowercase_      )
								lowerCamelCase_					:      Union[str, Any]     =		torch.tensor([1_0]      ).to(lowercase_      )

								return {"sample": noise, "timestep": time_step}

			@property
			def        UpperCAmelCase__ (self      ):
								return (3, 3_2, 3_2)

			@property
			def        UpperCAmelCase__ (self      ):
								return (3, 3_2, 3_2)






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		{
								    "block_out_channels": (3_2, 6_4),
								    "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
								    "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
								    "attention_head_dim": 3,
								    "out_channels": 3,
								    "in_channels": 3,
								    "layers_per_block": 2,
								    "sample_size": 3_2,
								}
								lowerCamelCase_					:      List[Any]     =		self.dummy_input
								return init_dict, inputs_dict

class 	__lowercase				(  snake_case__      ,     snake_case__      ,     unittest.TestCase    ):
			lowerCamelCase			:		Optional[int]	      =			UNetaDModel
			lowerCamelCase			:		Any	      =			"sample"

			@property
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		4
								lowerCamelCase_					:      Dict     =		4
								lowerCamelCase_					:      Dict     =		(3_2, 3_2)

								lowerCamelCase_					:      Dict     =		floats_tensor((batch_size, num_channels) + sizes      ).to(lowercase_      )
								lowerCamelCase_					:      Any     =		torch.tensor([1_0]      ).to(lowercase_      )

								return {"sample": noise, "timestep": time_step}

			@property
			def        UpperCAmelCase__ (self      ):
								return (4, 3_2, 3_2)

			@property
			def        UpperCAmelCase__ (self      ):
								return (4, 3_2, 3_2)

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		{
								    "sample_size": 3_2,
								    "in_channels": 4,
								    "out_channels": 4,
								    "layers_per_block": 2,
								    "block_out_channels": (3_2, 6_4),
								    "attention_head_dim": 3_2,
								    "down_block_types": ("DownBlock2D", "DownBlock2D"),
								    "up_block_types": ("UpBlock2D", "UpBlock2D"),
								}
								lowerCamelCase_					:      Any     =		self.dummy_input
								return init_dict, inputs_dict

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''			,   output_loading_info=lowercase_      )

								self.assertIsNotNone(lowercase_      )
								self.assertEqual(len(loading_info['''missing_keys''']      )			,   0      )

								model.to(lowercase_      )
								lowerCamelCase_					:      int     =		model(**self.dummy_input      ).sample

								assert image is not None, "Make sure output is not None"

			@unittest.skipIf(torch_device != '''cuda'''			,   '''This test is supposed to run on GPU'''      )
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''			,   output_loading_info=lowercase_      )
								model.to(lowercase_      )
								lowerCamelCase_					:      str     =		model(**self.dummy_input      ).sample

								assert image is not None, "Make sure output is not None"

			@unittest.skipIf(torch_device != '''cuda'''			,   '''This test is supposed to run on GPU'''      )
			def        UpperCAmelCase__ (self      ):
								# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
								lowerCamelCase_					:      Any     =		UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''			,   output_loading_info=lowercase_      )
								model_accelerate.to(lowercase_      )
								model_accelerate.eval()

								lowerCamelCase_					:      Optional[int]     =		torch.randn(
								    1			,   model_accelerate.config.in_channels			,   model_accelerate.config.sample_size			,   model_accelerate.config.sample_size			,   generator=torch.manual_seed(0      )			,   )
								lowerCamelCase_					:      List[str]     =		noise.to(lowercase_      )
								lowerCamelCase_					:      Any     =		torch.tensor([1_0] * noise.shape[0]      ).to(lowercase_      )

								lowerCamelCase_					:      Optional[int]     =		model_accelerate(lowercase_			,   lowercase_      )["sample"]

								# two models don't need to stay in the device at the same time
								del model_accelerate
								torch.cuda.empty_cache()
								gc.collect()

								lowerCamelCase_					:      Union[str, Any]     =		UNetaDModel.from_pretrained(
								    '''fusing/unet-ldm-dummy-update'''			,   output_loading_info=lowercase_			,   low_cpu_mem_usage=lowercase_      )
								model_normal_load.to(lowercase_      )
								model_normal_load.eval()
								lowerCamelCase_					:      Optional[Any]     =		model_normal_load(lowercase_			,   lowercase_      )["sample"]

								assert torch_all_close(lowercase_			,   lowercase_			,   rtol=1E-3      )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''      )
								model.eval()
								model.to(lowercase_      )

								lowerCamelCase_					:      List[str]     =		torch.randn(
								    1			,   model.config.in_channels			,   model.config.sample_size			,   model.config.sample_size			,   generator=torch.manual_seed(0      )			,   )
								lowerCamelCase_					:      List[str]     =		noise.to(lowercase_      )
								lowerCamelCase_					:      str     =		torch.tensor([1_0] * noise.shape[0]      ).to(lowercase_      )

								with torch.no_grad():
													lowerCamelCase_					:      Tuple     =		model(lowercase_			,   lowercase_      ).sample

								lowerCamelCase_					:      Optional[int]     =		output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								lowerCamelCase_					:      Tuple     =		torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]      )
								# fmt: on

								self.assertTrue(torch_all_close(lowercase_			,   lowercase_			,   rtol=1E-3      )      )

class 	__lowercase				(  snake_case__      ,     snake_case__      ,     unittest.TestCase    ):
			lowerCamelCase			:		Dict	      =			UNetaDModel
			lowerCamelCase			:		str	      =			"sample"

			@property
			def        UpperCAmelCase__ (self			,   A=(3_2, 3_2)      ):
								lowerCamelCase_					:      List[Any]     =		4
								lowerCamelCase_					:      str     =		3

								lowerCamelCase_					:      str     =		floats_tensor((batch_size, num_channels) + sizes      ).to(lowercase_      )
								lowerCamelCase_					:      Dict     =		torch.tensor(batch_size * [1_0]      ).to(dtype=torch.intaa			,   device=lowercase_      )

								return {"sample": noise, "timestep": time_step}

			@property
			def        UpperCAmelCase__ (self      ):
								return (3, 3_2, 3_2)

			@property
			def        UpperCAmelCase__ (self      ):
								return (3, 3_2, 3_2)

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		{
								    "block_out_channels": [3_2, 6_4, 6_4, 6_4],
								    "in_channels": 3,
								    "layers_per_block": 1,
								    "out_channels": 3,
								    "time_embedding_type": "fourier",
								    "norm_eps": 1E-6,
								    "mid_block_scale_factor": math.sqrt(2.0      ),
								    "norm_num_groups": None,
								    "down_block_types": [
								        "SkipDownBlock2D",
								        "AttnSkipDownBlock2D",
								        "SkipDownBlock2D",
								        "SkipDownBlock2D",
								    ],
								    "up_block_types": [
								        "SkipUpBlock2D",
								        "SkipUpBlock2D",
								        "AttnSkipUpBlock2D",
								        "SkipUpBlock2D",
								    ],
								}
								lowerCamelCase_					:      Optional[Any]     =		self.dummy_input
								return init_dict, inputs_dict

			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''			,   output_loading_info=lowercase_      )
								self.assertIsNotNone(lowercase_      )
								self.assertEqual(len(loading_info['''missing_keys''']      )			,   0      )

								model.to(lowercase_      )
								lowerCamelCase_					:      Dict     =		self.dummy_input
								lowerCamelCase_					:      Tuple     =		floats_tensor((4, 3) + (2_5_6, 2_5_6)      ).to(lowercase_      )
								lowerCamelCase_					:      Tuple     =		noise
								lowerCamelCase_					:      Tuple     =		model(**lowercase_      )

								assert image is not None, "Make sure output is not None"

			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
								model.to(lowercase_      )

								lowerCamelCase_					:      List[Any]     =		4
								lowerCamelCase_					:      str     =		3
								lowerCamelCase_					:      Dict     =		(2_5_6, 2_5_6)

								lowerCamelCase_					:      Tuple     =		torch.ones((batch_size, num_channels) + sizes      ).to(lowercase_      )
								lowerCamelCase_					:      List[Any]     =		torch.tensor(batch_size * [1E-4]      ).to(lowercase_      )

								with torch.no_grad():
													lowerCamelCase_					:      int     =		model(lowercase_			,   lowercase_      ).sample

								lowerCamelCase_					:      List[str]     =		output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCamelCase_					:      Union[str, Any]     =		torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_67.76_08]      )
								# fmt: on

								self.assertTrue(torch_all_close(lowercase_			,   lowercase_			,   rtol=1E-2      )      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
								model.to(lowercase_      )

								lowerCamelCase_					:      Dict     =		4
								lowerCamelCase_					:      str     =		3
								lowerCamelCase_					:      List[Any]     =		(3_2, 3_2)

								lowerCamelCase_					:      int     =		torch.ones((batch_size, num_channels) + sizes      ).to(lowercase_      )
								lowerCamelCase_					:      List[Any]     =		torch.tensor(batch_size * [1E-4]      ).to(lowercase_      )

								with torch.no_grad():
													lowerCamelCase_					:      Optional[Any]     =		model(lowercase_			,   lowercase_      ).sample

								lowerCamelCase_					:      List[Any]     =		output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCamelCase_					:      Union[str, Any]     =		torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]      )
								# fmt: on

								self.assertTrue(torch_all_close(lowercase_			,   lowercase_			,   rtol=1E-2      )      )






			def        UpperCAmelCase__ (self      ):
								# not required for this model
								pass




'''simple docstring'''


def lowercase_	(   _lowercase = 1_000				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		-1
					lowerCamelCase_					:      Optional[Any]     =		0
					for a in range(1			,       n // 3				):
										# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
										lowerCamelCase_					:      Any     =		(n * n - 2 * a * n) // (2 * n - 2 * a)
										lowerCamelCase_					:      int     =		n - a - b
										if c * c == (a * a + b * b):
															lowerCamelCase_					:      int     =		a * b * c
															if candidate >= product:
																				lowerCamelCase_					:      Any     =		candidate
					return product


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


