
'''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    =		logging.get_logger(__name__)

enable_full_determinism()




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






						'''simple docstring'''






						_lowercase		:				Tuple									=       UNetaDModel
						_lowercase		:				Union[str, Any]									=       '''sample'''




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			4
												_lowerCAmelCase  						=			3
												_lowerCAmelCase  						=			(32, 32)

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

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




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return (3, 32, 32)




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return (3, 32, 32)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{
												    """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,
												}
												_lowerCAmelCase  						=			self.dummy_input
												return init_dict, inputs_dict






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






						'''simple docstring'''






						_lowercase		:				int									=       UNetaDModel
						_lowercase		:				str									=       '''sample'''




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			4
												_lowerCAmelCase  						=			4
												_lowerCAmelCase  						=			(32, 32)

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

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




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return (4, 32, 32)




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return (4, 32, 32)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{
												    """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"""),
												}
												_lowerCAmelCase  						=			self.dummy_input
												return init_dict, inputs_dict




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			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  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""   ,       output_loading_info=_lowercase				)
												model.to(_lowercase				)
												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""   ,       output_loading_info=_lowercase				)
												model_accelerate.to(_lowercase				)
												model_accelerate.eval()

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

												_lowerCAmelCase  						=			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						,   _lowerCAmelCase  						=			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  						=			model_normal_load(_lowercase   ,       _lowercase				)["""sample"""]

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""				)
												model.eval()
												model.to(_lowercase				)

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

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				).sample

												_lowerCAmelCase  						=			output[0, -1, -3:, -3:].flatten().cpu()
												# fmt: off
												_lowerCAmelCase  						=			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(_lowercase   ,       _lowercase   ,       rtol=1e-3				)				)






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






						'''simple docstring'''






						_lowercase		:				Optional[int]									=       UNetaDModel
						_lowercase		:				List[str]									=       '''sample'''




						@property
						def  _lowercase     ( self   ,       _lowercase=(32, 32)				):

												"""simple docstring"""



												_lowerCAmelCase  						=			4
												_lowerCAmelCase  						=			3

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

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




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return (3, 32, 32)




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return (3, 32, 32)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{
												    """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""",
												    ],
												}
												_lowerCAmelCase  						=			self.dummy_input
												return init_dict, inputs_dict




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



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

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




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""				)
												model.to(_lowercase				)

												_lowerCAmelCase  						=			4
												_lowerCAmelCase  						=			3
												_lowerCAmelCase  						=			(256, 256)

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

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				).sample

												_lowerCAmelCase  						=			output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												_lowerCAmelCase  						=			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(_lowercase   ,       _lowercase   ,       rtol=1e-2				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""				)
												model.to(_lowercase				)

												_lowerCAmelCase  						=			4
												_lowerCAmelCase  						=			3
												_lowerCAmelCase  						=			(32, 32)

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

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				).sample

												_lowerCAmelCase  						=			output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												_lowerCAmelCase  						=			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(_lowercase   ,       _lowercase   ,       rtol=1e-2				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												pass


'''simple docstring'''



from __future__ import annotations




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			order

												# a_{0} ... a_{k}
												_lowerCAmelCase  						=			[1.0] + [0.0] * order
												# b_{0} ... b_{k}
												_lowerCAmelCase  						=			[1.0] + [0.0] * order

												# x[n-1] ... x[n-k]
												_lowerCAmelCase  						=			[0.0] * self.order
												# y[n-1] ... y[n-k]
												_lowerCAmelCase  						=			[0.0] * self.order




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												if len(_lowercase				) < self.order:
																		_lowerCAmelCase  						=			[1.0, *a_coeffs]

												if len(_lowercase				) != self.order + 1:
																		_lowerCAmelCase  						=			(
																		    F'Expected a_coeffs to have {self.order + 1} elements '
																		    F'for {self.order}-order filter, got {len(_lowercase				)}'
																		)
																		raise ValueError(_lowercase				)

												if len(_lowercase				) != self.order + 1:
																		_lowerCAmelCase  						=			(
																		    F'Expected b_coeffs to have {self.order + 1} elements '
																		    F'for {self.order}-order filter, got {len(_lowercase				)}'
																		)
																		raise ValueError(_lowercase				)

												_lowerCAmelCase  						=			a_coeffs
												_lowerCAmelCase  						=			b_coeffs




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			0.0

												# Start at index 1 and do index 0 at the end.
												for i in range(1   ,       self.order + 1				):
																		result += (
																		    self.b_coeffs[i] * self.input_history[i - 1]
																		    - self.a_coeffs[i] * self.output_history[i - 1]
																		)

												_lowerCAmelCase  						=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

												_lowerCAmelCase  						=			self.input_history[:-1]
												_lowerCAmelCase  						=			self.output_history[:-1]

												_lowerCAmelCase  						=			sample
												_lowerCAmelCase  						=			result

												return result
