
'''simple docstring'''





import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 						a   (				__magic_name__				,__magic_name__				,unittest.TestCase    ):
				_snake_case		    = AutoencoderKL
				_snake_case		    = '''sample'''
				_snake_case		    = 1e-2




				@property
				def 	__snake_case							(							self	:				str      ):
										snake_case       :     Optional[int] =    4
										snake_case       :     Optional[int] =    3
										snake_case       :     Dict =    (32, 32)

										snake_case       :     Union[str, Any] =    floats_tensor((batch_size, num_channels) + sizes      ).to(SCREAMING_SNAKE_CASE_      )

										return {"sample": image}




				@property
				def 	__snake_case							(							self	:				Any      ):
										return (3, 32, 32)




				@property
				def 	__snake_case							(							self	:				str      ):
										return (3, 32, 32)




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     int =    {
										    '''block_out_channels''': [32, 64],
										    '''in_channels''': 3,
										    '''out_channels''': 3,
										    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
										    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
										    '''latent_channels''': 4,
										}
										snake_case       :     List[Any] =    self.dummy_input
										return init_dict, inputs_dict




				def 	__snake_case							(							self	:				Dict      ):
										pass




				def 	__snake_case							(							self	:				Optional[Any]      ):
										pass




				@unittest.skipIf(torch_device == '''mps''',    '''Gradient checkpointing skipped on MPS'''      )
				def 	__snake_case							(							self	:				Optional[int]      ):
										# enable deterministic behavior for gradient checkpointing
										snake_case       :     Dict =    self.prepare_init_args_and_inputs_for_common()
										snake_case       :     str =    self.model_class(**SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )

										assert not model.is_gradient_checkpointing and model.training

										snake_case       :     List[Any] =    model(**SCREAMING_SNAKE_CASE_      ).sample
										# run the backwards pass on the model. For backwards pass, for simplicity purpose,
										# we won't calculate the loss and rather backprop on out.sum()
										model.zero_grad()

										snake_case       :     int =    torch.randn_like(SCREAMING_SNAKE_CASE_      )
										snake_case       :     str =    (out - labels).mean()
										loss.backward()

										# re-instantiate the model now enabling gradient checkpointing
										snake_case       :     List[str] =    self.model_class(**SCREAMING_SNAKE_CASE_      )
										# clone model
										model_a.load_state_dict(model.state_dict()      )
										model_a.to(SCREAMING_SNAKE_CASE_      )
										model_a.enable_gradient_checkpointing()

										assert model_a.is_gradient_checkpointing and model_a.training

										snake_case       :     Dict =    model_a(**SCREAMING_SNAKE_CASE_      ).sample
										# run the backwards pass on the model. For backwards pass, for simplicity purpose,
										# we won't calculate the loss and rather backprop on out.sum()
										model_a.zero_grad()
										snake_case       :     Tuple =    (out_a - labels).mean()
										loss_a.backward()

										# compare the output and parameters gradients
										self.assertTrue((loss - loss_a).abs() < 1e-5      )
										snake_case       :     Union[str, Any] =    dict(model.named_parameters()      )
										snake_case       :     Any =    dict(model_a.named_parameters()      )
										for name, param in named_params.items():
																self.assertTrue(torch_all_close(param.grad.data,    named_params_a[name].grad.data,    atol=5e-5      )      )




				def 	__snake_case							(							self	:				str      ):
										snake_case       :     Optional[Any] =    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''',    output_loading_info=SCREAMING_SNAKE_CASE_      )
										self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )
										self.assertEqual(len(loading_info['''missing_keys''']      ),    0      )

										model.to(SCREAMING_SNAKE_CASE_      )
										snake_case       :     str =    model(**self.dummy_input      )

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




				def 	__snake_case							(							self	:				str      ):
										snake_case       :     Optional[Any] =    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''      )
										snake_case       :     Optional[int] =    model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()

										if torch_device == "mps":
																snake_case       :     Union[str, Any] =    torch.manual_seed(0      )
										else:
																snake_case       :     List[Any] =    torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(0      )

										snake_case       :     Any =    torch.randn(
										    1,    model.config.in_channels,    model.config.sample_size,    model.config.sample_size,    generator=torch.manual_seed(0      ),    )
										snake_case       :     int =    image.to(SCREAMING_SNAKE_CASE_      )
										with torch.no_grad():
																snake_case       :     Tuple =    model(SCREAMING_SNAKE_CASE_,    sample_posterior=SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_      ).sample

										snake_case       :     Optional[Any] =    output[0, -1, -3:, -3:].flatten().cpu()

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										if torch_device == "mps":
																snake_case       :     str =    torch.tensor(
																    [
																        -4.00_78e-01,
																        -3.83_23e-04,
																        -1.26_81e-01,
																        -1.14_62e-01,
																        2.00_95e-01,
																        1.08_93e-01,
																        -8.82_47e-02,
																        -3.03_61e-01,
																        -9.86_44e-03,
																    ]      )
										elif torch_device == "cpu":
																snake_case       :     int =    torch.tensor(
																    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]      )
										else:
																snake_case       :     Dict =    torch.tensor(
																    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]      )

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




@slow
class 						a   (				unittest.TestCase    ):




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict      ):
										return F"""gaussian_noise_s={seed}_shape={'_'.join([str(SCREAMING_SNAKE_CASE_      ) for s in shape]      )}.npy"""




				def 	__snake_case							(							self	:				Optional[int]      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				List[str]=0,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=(4, 3, 5_12, 5_12),    SCREAMING_SNAKE_CASE_	:				Tuple=False      ):
										snake_case       :     Union[str, Any] =    torch.floataa if fpaa else torch.floataa
										snake_case       :     List[str] =    torch.from_numpy(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )      )      ).to(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
										return image




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Dict="CompVis/stable-diffusion-v1-4",    SCREAMING_SNAKE_CASE_	:				Any=False      ):
										snake_case       :     List[str] =    '''fp16''' if fpaa else None
										snake_case       :     Any =    torch.floataa if fpaa else torch.floataa

										snake_case       :     List[Any] =    AutoencoderKL.from_pretrained(
										    SCREAMING_SNAKE_CASE_,    subfolder='''vae''',    torch_dtype=SCREAMING_SNAKE_CASE_,    revision=SCREAMING_SNAKE_CASE_,    )
										model.to(SCREAMING_SNAKE_CASE_      ).eval()

										return model




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=0      ):
										if torch_device == "mps":
																return torch.manual_seed(SCREAMING_SNAKE_CASE_      )
										return torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]      )
				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Any      ):
										snake_case       :     Dict =    self.get_sd_vae_model()
										snake_case       :     str =    self.get_sd_image(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Union[str, Any] =    self.get_generator(SCREAMING_SNAKE_CASE_      )

										with torch.no_grad():
																snake_case       :     List[Any] =    model(SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_,    sample_posterior=SCREAMING_SNAKE_CASE_      ).sample

										assert sample.shape == image.shape

										snake_case       :     Any =    sample[-1, -2:, -2:, :2].flatten().float().cpu()
										snake_case       :     Optional[int] =    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice      )

										assert torch_all_close(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=3e-3      )




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
				        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
				        # fmt: on
				    ]      )
				@require_torch_gpu
				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Optional[int] =    self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[Any] =    self.get_sd_image(SCREAMING_SNAKE_CASE_,    fpaa=SCREAMING_SNAKE_CASE_      )
										snake_case       :     int =    self.get_generator(SCREAMING_SNAKE_CASE_      )

										with torch.no_grad():
																snake_case       :     Tuple =    model(SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_,    sample_posterior=SCREAMING_SNAKE_CASE_      ).sample

										assert sample.shape == image.shape

										snake_case       :     str =    sample[-1, -2:, :2, -2:].flatten().float().cpu()
										snake_case       :     Union[str, Any] =    torch.tensor(SCREAMING_SNAKE_CASE_      )

										assert torch_all_close(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=1e-2      )




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]      )
				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Optional[int] =    self.get_sd_vae_model()
										snake_case       :     Optional[int] =    self.get_sd_image(SCREAMING_SNAKE_CASE_      )

										with torch.no_grad():
																snake_case       :     List[str] =    model(SCREAMING_SNAKE_CASE_      ).sample

										assert sample.shape == image.shape

										snake_case       :     Any =    sample[-1, -2:, -2:, :2].flatten().float().cpu()
										snake_case       :     Optional[int] =    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice      )

										assert torch_all_close(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=3e-3      )




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
				        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
				        # fmt: on
				    ]      )
				@require_torch_gpu
				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     List[Any] =    self.get_sd_vae_model()
										snake_case       :     int =    self.get_sd_image(SCREAMING_SNAKE_CASE_,    shape=(3, 4, 64, 64)      )

										with torch.no_grad():
																snake_case       :     Optional[int] =    model.decode(SCREAMING_SNAKE_CASE_      ).sample

										assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

										snake_case       :     Optional[int] =    sample[-1, -2:, :2, -2:].flatten().cpu()
										snake_case       :     List[Any] =    torch.tensor(SCREAMING_SNAKE_CASE_      )

										assert torch_all_close(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=1e-3      )




				@parameterized.expand(
				    [
				        # fmt: off
				        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
				        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
				        # fmt: on
				    ]      )
				@require_torch_gpu
				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     int =    self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    self.get_sd_image(SCREAMING_SNAKE_CASE_,    shape=(3, 4, 64, 64),    fpaa=SCREAMING_SNAKE_CASE_      )

										with torch.no_grad():
																snake_case       :     int =    model.decode(SCREAMING_SNAKE_CASE_      ).sample

										assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

										snake_case       :     Any =    sample[-1, -2:, :2, -2:].flatten().float().cpu()
										snake_case       :     Optional[int] =    torch.tensor(SCREAMING_SNAKE_CASE_      )

										assert torch_all_close(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=5e-3      )




				@parameterized.expand([(13,), (16,), (27,)]      )
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available(),    reason='''xformers is not required when using PyTorch 2.0.'''      )
				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										snake_case       :     str =    self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    self.get_sd_image(SCREAMING_SNAKE_CASE_,    shape=(3, 4, 64, 64),    fpaa=SCREAMING_SNAKE_CASE_      )

										with torch.no_grad():
																snake_case       :     str =    model.decode(SCREAMING_SNAKE_CASE_      ).sample

										model.enable_xformers_memory_efficient_attention()
										with torch.no_grad():
																snake_case       :     int =    model.decode(SCREAMING_SNAKE_CASE_      ).sample

										assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

										assert torch_all_close(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=1e-1      )




				@parameterized.expand([(13,), (16,), (37,)]      )
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available(),    reason='''xformers is not required when using PyTorch 2.0.'''      )
				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Optional[Any] =    self.get_sd_vae_model()
										snake_case       :     Dict =    self.get_sd_image(SCREAMING_SNAKE_CASE_,    shape=(3, 4, 64, 64)      )

										with torch.no_grad():
																snake_case       :     Optional[Any] =    model.decode(SCREAMING_SNAKE_CASE_      ).sample

										model.enable_xformers_memory_efficient_attention()
										with torch.no_grad():
																snake_case       :     List[str] =    model.decode(SCREAMING_SNAKE_CASE_      ).sample

										assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

										assert torch_all_close(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=1e-2      )




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
				        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
				        # fmt: on
				    ]      )
				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Dict =    self.get_sd_vae_model()
										snake_case       :     Union[str, Any] =    self.get_sd_image(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Union[str, Any] =    self.get_generator(SCREAMING_SNAKE_CASE_      )

										with torch.no_grad():
																snake_case       :     Optional[int] =    model.encode(SCREAMING_SNAKE_CASE_      ).latent_dist
																snake_case       :     Union[str, Any] =    dist.sample(generator=SCREAMING_SNAKE_CASE_      )

										assert list(sample.shape      ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

										snake_case       :     Dict =    sample[0, -1, -3:, -3:].flatten().cpu()
										snake_case       :     Optional[int] =    torch.tensor(SCREAMING_SNAKE_CASE_      )

										snake_case       :     List[Any] =    3e-3 if torch_device != '''mps''' else 1e-2
										assert torch_all_close(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    atol=SCREAMING_SNAKE_CASE_      )



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

