


'''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


__snake_case				 =	logging.get_logger(__name__)

enable_full_determinism()
class   lowercase      (				A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							UNetaDModel
		_a       					=							'sample'




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					4
							UpperCamelCase__  :Union[str, Any]				   =					3
							UpperCamelCase__  :Optional[Any]				   =					(32, 32)

							UpperCamelCase__  :Optional[int]				   =					floats_tensor((batch_size, num_channels) + sizes				).to(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					torch.tensor([10]				).to(UpperCamelCase_				)

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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return (3, 32, 32)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return (3, 32, 32)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					{
							    '''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,
							}
							UpperCamelCase__  :str				   =					self.dummy_input
							return init_dict, inputs_dict



class   lowercase      (				A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							UNetaDModel
		_a       					=							'sample'




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					4
							UpperCamelCase__  :Optional[int]				   =					4
							UpperCamelCase__  :str				   =					(32, 32)

							UpperCamelCase__  :int				   =					floats_tensor((batch_size, num_channels) + sizes				).to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					torch.tensor([10]				).to(UpperCamelCase_				)

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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return (4, 32, 32)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return (4, 32, 32)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					{
							    '''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'''),
							}
							UpperCamelCase__  :Any				   =					self.dummy_input
							return init_dict, inputs_dict




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :int				   =					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''       ,  output_loading_info=UpperCamelCase_				)

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

							model.to(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					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				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :Any				   =					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''       ,  output_loading_info=UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					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				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :Optional[Any]				   =					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''       ,  output_loading_info=UpperCamelCase_				)
							model_accelerate.to(UpperCamelCase_				)
							model_accelerate.eval()

							UpperCamelCase__  :Union[str, Any]				   =					torch.randn(
							    1       ,  model_accelerate.config.in_channels       ,  model_accelerate.config.sample_size       ,  model_accelerate.config.sample_size       ,  generator=torch.manual_seed(0				)       ,  )
							UpperCamelCase__  :Optional[Any]				   =					noise.to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					torch.tensor([10] * noise.shape[0]				).to(UpperCamelCase_				)

							UpperCamelCase__  :Union[str, Any]				   =					model_accelerate(UpperCamelCase_       ,  UpperCamelCase_				)['''sample''']

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

							UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					UNetaDModel.from_pretrained(
							    '''fusing/unet-ldm-dummy-update'''       ,  output_loading_info=UpperCamelCase_       ,  low_cpu_mem_usage=UpperCamelCase_				)
							model_normal_load.to(UpperCamelCase_				)
							model_normal_load.eval()
							UpperCamelCase__  :str				   =					model_normal_load(UpperCamelCase_       ,  UpperCamelCase_				)['''sample''']

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''				)
							model.eval()
							model.to(UpperCamelCase_				)

							UpperCamelCase__  :Union[str, Any]				   =					torch.randn(
							    1       ,  model.config.in_channels       ,  model.config.sample_size       ,  model.config.sample_size       ,  generator=torch.manual_seed(0				)       ,  )
							UpperCamelCase__  :Tuple				   =					noise.to(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					torch.tensor([10] * noise.shape[0]				).to(UpperCamelCase_				)

							with torch.no_grad():
												UpperCamelCase__  :str				   =					model(UpperCamelCase_       ,  UpperCamelCase_				).sample

							UpperCamelCase__  :int				   =					output[0, -1, -3:, -3:].flatten().cpu()
							# fmt: off
							UpperCamelCase__  :Optional[Any]				   =					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(UpperCamelCase_       ,  UpperCamelCase_       ,  rtol=1e-3				)				)



class   lowercase      (				A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							UNetaDModel
		_a       					=							'sample'




		@property
		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=(32, 32)				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					4
							UpperCamelCase__  :Optional[Any]				   =					3

							UpperCamelCase__  :Dict				   =					floats_tensor((batch_size, num_channels) + sizes				).to(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					torch.tensor(batch_size * [10]				).to(dtype=torch.intaa       ,  device=UpperCamelCase_				)

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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return (3, 32, 32)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return (3, 32, 32)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					{
							    '''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''',
							    ],
							}
							UpperCamelCase__  :str				   =					self.dummy_input
							return init_dict, inputs_dict




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :Union[str, Any]				   =					UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''       ,  output_loading_info=UpperCamelCase_				)
							self.assertIsNotNone(UpperCamelCase_				)
							self.assertEqual(len(loading_info['''missing_keys''']				)       ,  0				)

							model.to(UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					self.dummy_input
							UpperCamelCase__  :Optional[Any]				   =					floats_tensor((4, 3) + (256, 256)				).to(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					noise
							UpperCamelCase__  :str				   =					model(**UpperCamelCase_				)

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




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''				)
							model.to(UpperCamelCase_				)

							UpperCamelCase__  :Optional[Any]				   =					4
							UpperCamelCase__  :str				   =					3
							UpperCamelCase__  :Union[str, Any]				   =					(256, 256)

							UpperCamelCase__  :Any				   =					torch.ones((batch_size, num_channels) + sizes				).to(UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					torch.tensor(batch_size * [1e-4]				).to(UpperCamelCase_				)

							with torch.no_grad():
												UpperCamelCase__  :List[str]				   =					model(UpperCamelCase_       ,  UpperCamelCase_				).sample

							UpperCamelCase__  :Union[str, Any]				   =					output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							UpperCamelCase__  :List[Any]				   =					torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]				)
							# fmt: on

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''				)
							model.to(UpperCamelCase_				)

							UpperCamelCase__  :str				   =					4
							UpperCamelCase__  :int				   =					3
							UpperCamelCase__  :Optional[Any]				   =					(32, 32)

							UpperCamelCase__  :str				   =					torch.ones((batch_size, num_channels) + sizes				).to(UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					torch.tensor(batch_size * [1e-4]				).to(UpperCamelCase_				)

							with torch.no_grad():
												UpperCamelCase__  :Optional[int]				   =					model(UpperCamelCase_       ,  UpperCamelCase_				).sample

							UpperCamelCase__  :Optional[Any]				   =					output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							UpperCamelCase__  :List[str]				   =					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(UpperCamelCase_       ,  UpperCamelCase_       ,  rtol=1e-2				)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass



'''simple docstring'''
from __future__ import annotations

from math import pi, sqrt


def      a (     __a					,   __a  )    ->      tuple:



					'''simple docstring'''






					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''  )

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''  )

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance  )))  ),
										)


if __name__ == "__main__":
		import doctest

		doctest.testmod()