"""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


_A   					=    logging.get_logger(__name__)

enable_full_determinism()

class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		UNetaDModel
					a   	=		"sample"



					@property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				3
									SCREAMING_SNAKE_CASE__		  =				(32, 32)

									SCREAMING_SNAKE_CASE__		  =				floats_tensor((batch_size, num_channels) + sizes		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([10]		).to(_snake_case		)

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



					@property
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									return (3, 32, 32)



					@property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Any:
									return (3, 32, 32)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				{
									    "block_out_channels": (32, 64),
									    "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": 32,
									}
									SCREAMING_SNAKE_CASE__		  =				self.dummy_input
									return init_dict, inputs_dict



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		UNetaDModel
					a   	=		"sample"



					@property
					def 			lowerCAmelCase_					(						self			:				int		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				(32, 32)

									SCREAMING_SNAKE_CASE__		  =				floats_tensor((batch_size, num_channels) + sizes		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([10]		).to(_snake_case		)

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



					@property
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       str:
									return (4, 32, 32)



					@property
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       str:
									return (4, 32, 32)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				{
									    "sample_size": 32,
									    "in_channels": 4,
									    "out_channels": 4,
									    "layers_per_block": 2,
									    "block_out_channels": (32, 64),
									    "attention_head_dim": 32,
									    "down_block_types": ("DownBlock2D", "DownBlock2D"),
									    "up_block_types": ("UpBlock2D", "UpBlock2D"),
									}
									SCREAMING_SNAKE_CASE__		  =				self.dummy_input
									return init_dict, inputs_dict



					def 			lowerCAmelCase_					(						self			:				int		)							->       Dict:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"   , output_loading_info=_snake_case		)

									self.assertIsNotNone(_snake_case		)
									self.assertEqual(len(loading_info["missing_keys"]		)   , 0		)

									model.to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				List[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"   , output_loading_info=_snake_case		)
									model.to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       str:
									# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"   , output_loading_info=_snake_case		)
									model_accelerate.to(_snake_case		)
									model_accelerate.eval()

									SCREAMING_SNAKE_CASE__		  =				torch.randn(
									    1   , model_accelerate.config.in_channels   , model_accelerate.config.sample_size   , model_accelerate.config.sample_size   , generator=torch.manual_seed(0		)   , )
									SCREAMING_SNAKE_CASE__		  =				noise.to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([10] * noise.shape[0]		).to(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				model_accelerate(_snake_case   , _snake_case		)["sample"]

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

									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				UNetaDModel.from_pretrained(
									    "fusing/unet-ldm-dummy-update"   , output_loading_info=_snake_case   , low_cpu_mem_usage=_snake_case		)
									model_normal_load.to(_snake_case		)
									model_normal_load.eval()
									SCREAMING_SNAKE_CASE__		  =				model_normal_load(_snake_case   , _snake_case		)["sample"]

									assert torch_all_close(_snake_case   , _snake_case   , rtol=1e-3		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"		)
									model.eval()
									model.to(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				torch.randn(
									    1   , model.config.in_channels   , model.config.sample_size   , model.config.sample_size   , generator=torch.manual_seed(0		)   , )
									SCREAMING_SNAKE_CASE__		  =				noise.to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([10] * noise.shape[0]		).to(_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , _snake_case		).sample

									SCREAMING_SNAKE_CASE__		  =				output[0, -1, -3:, -3:].flatten().cpu()
									# fmt: off
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]		)
									# fmt: on

									self.assertTrue(torch_all_close(_snake_case   , _snake_case   , rtol=1e-3		)		)



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		UNetaDModel
					a   	=		"sample"



					@property
					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				str=(32, 32)		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				3

									SCREAMING_SNAKE_CASE__		  =				floats_tensor((batch_size, num_channels) + sizes		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(batch_size * [10]		).to(dtype=torch.intaa   , device=_snake_case		)

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



					@property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									return (3, 32, 32)



					@property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Dict:
									return (3, 32, 32)



					def 			lowerCAmelCase_					(						self			:				str		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				{
									    "block_out_channels": [32, 64, 64, 64],
									    "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",
									    ],
									}
									SCREAMING_SNAKE_CASE__		  =				self.dummy_input
									return init_dict, inputs_dict



					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"   , output_loading_info=_snake_case		)
									self.assertIsNotNone(_snake_case		)
									self.assertEqual(len(loading_info["missing_keys"]		)   , 0		)

									model.to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.dummy_input
									SCREAMING_SNAKE_CASE__		  =				floats_tensor((4, 3) + (256, 256)		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				noise
									SCREAMING_SNAKE_CASE__		  =				model(**_snake_case		)

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



					@slow
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"		)
									model.to(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				3
									SCREAMING_SNAKE_CASE__		  =				(256, 256)

									SCREAMING_SNAKE_CASE__		  =				torch.ones((batch_size, num_channels) + sizes		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(batch_size * [1e-4]		).to(_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , _snake_case		).sample

									SCREAMING_SNAKE_CASE__		  =				output[0, -3:, -3:, -1].flatten().cpu()
									# fmt: off
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608]		)
									# fmt: on

									self.assertTrue(torch_all_close(_snake_case   , _snake_case   , rtol=1e-2		)		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"		)
									model.to(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				3
									SCREAMING_SNAKE_CASE__		  =				(32, 32)

									SCREAMING_SNAKE_CASE__		  =				torch.ones((batch_size, num_channels) + sizes		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(batch_size * [1e-4]		).to(_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , _snake_case		).sample

									SCREAMING_SNAKE_CASE__		  =				output[0, -3:, -3:, -1].flatten().cpu()
									# fmt: off
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]		)
									# fmt: on

									self.assertTrue(torch_all_close(_snake_case   , _snake_case   , rtol=1e-2		)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Dict:
									# not required for this model
									pass





"""simple docstring"""






import math




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:

				if initial_intensity < 0:
								raise ValueError("The value of intensity cannot be negative"       )
								# handling of negative values of initial intensity
				if angle < 0 or angle > 360:
								raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"       )
								# handling of values out of allowed range
				return initial_intensity * (math.cos(math.radians(__UpperCAmelCase       )       ) ** 2)


if __name__ == "__main__":
						import doctest

						doctest.testmod(name='malus_law')




