

"""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  lowerCamelCase    (A__      ,A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[str]       		=				AutoencoderKL
			lowerCamelCase__ :		List[str]       		=				'sample'
			lowerCamelCase__ :		str       		=				1E-2

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   3
						SCREAMING_SNAKE_CASE__										=   (3_2, 3_2)

						SCREAMING_SNAKE_CASE__										=   floats_tensor((batch_size, num_channels) + sizes							).to(__UpperCAmelCase							)

						return {"sample": image}

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     int:
						return (3, 3_2, 3_2)

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Optional[Any]:
						return (3, 3_2, 3_2)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   {
						    """block_out_channels""": [3_2, 6_4],
						    """in_channels""": 3,
						    """out_channels""": 3,
						    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
						    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
						    """latent_channels""": 4,
						}
						SCREAMING_SNAKE_CASE__										=   self.dummy_input
						return init_dict, inputs_dict

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						pass

			@unittest.skipIf(torch_device == """mps"""     ,					"""Gradient checkpointing skipped on MPS"""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Tuple:
						# enable deterministic behavior for gradient checkpointing
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.prepare_init_args_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__										=   self.model_class(**__UpperCAmelCase							)
						model.to(__UpperCAmelCase							)

						assert not model.is_gradient_checkpointing and model.training

						SCREAMING_SNAKE_CASE__										=   model(**__UpperCAmelCase							).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()

						SCREAMING_SNAKE_CASE__										=   torch.randn_like(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   (out - labels).mean()
						loss.backward()

						# re-instantiate the model now enabling gradient checkpointing
						SCREAMING_SNAKE_CASE__										=   self.model_class(**__UpperCAmelCase							)
						# clone model
						model_a.load_state_dict(model.state_dict()							)
						model_a.to(__UpperCAmelCase							)
						model_a.enable_gradient_checkpointing()

						assert model_a.is_gradient_checkpointing and model_a.training

						SCREAMING_SNAKE_CASE__										=   model_a(**__UpperCAmelCase							).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()
						SCREAMING_SNAKE_CASE__										=   (out_a - labels).mean()
						loss_a.backward()

						# compare the output and parameters gradients
						self.assertTrue((loss - loss_a).abs() < 1e-5							)
						SCREAMING_SNAKE_CASE__										=   dict(model.named_parameters()							)
						SCREAMING_SNAKE_CASE__										=   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        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""     ,					output_loading_info=__UpperCAmelCase							)
						self.assertIsNotNone(__UpperCAmelCase							)
						self.assertEqual(len(loading_info["""missing_keys"""]							)     ,					0							)

						model.to(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(**self.dummy_input							)

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





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""							)
						SCREAMING_SNAKE_CASE__										=   model.to(__UpperCAmelCase							)
						model.eval()

						if torch_device == "mps":
									SCREAMING_SNAKE_CASE__										=   torch.manual_seed(0							)
						else:
									SCREAMING_SNAKE_CASE__										=   torch.Generator(device=__UpperCAmelCase							).manual_seed(0							)

						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__										=   image.to(__UpperCAmelCase							)
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					sample_posterior=__UpperCAmelCase     ,					generator=__UpperCAmelCase							).sample

						SCREAMING_SNAKE_CASE__										=   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":
									SCREAMING_SNAKE_CASE__										=   torch.tensor(
									    [
									        -4.0078e-01,
									        -3.8323e-04,
									        -1.2681e-01,
									        -1.1462e-01,
									        2.0095e-01,
									        1.0893e-01,
									        -8.8247e-02,
									        -3.0361e-01,
									        -9.8644e-03,
									    ]							)
						elif torch_device == "cpu":
									SCREAMING_SNAKE_CASE__										=   torch.tensor(
									    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]							)
						else:
									SCREAMING_SNAKE_CASE__										=   torch.tensor(
									    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]							)

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





@slow
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Union[str, Any]							) ->     Tuple:
						return F"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCAmelCase							) for s in shape]							)}.npy"""

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Dict=0     ,					__UpperCAmelCase					:    str=(4, 3, 5_1_2, 5_1_2)     ,					__UpperCAmelCase					:    Dict=False							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   torch.floataa if fpaa else torch.floataa
						SCREAMING_SNAKE_CASE__										=   torch.from_numpy(load_hf_numpy(self.get_file_format(__UpperCAmelCase     ,					__UpperCAmelCase							)							)							).to(__UpperCAmelCase							).to(__UpperCAmelCase							)
						return image

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    int="CompVis/stable-diffusion-v1-4"     ,					__UpperCAmelCase					:    Dict=False							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   """fp16""" if fpaa else None
						SCREAMING_SNAKE_CASE__										=   torch.floataa if fpaa else torch.floataa

						SCREAMING_SNAKE_CASE__										=   AutoencoderKL.from_pretrained(
						    __UpperCAmelCase     ,					subfolder="""vae"""     ,					torch_dtype=__UpperCAmelCase     ,					revision=__UpperCAmelCase     ,					)
						model.to(__UpperCAmelCase							).eval()

						return model

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    int=0							) ->     List[Any]:
						if torch_device == "mps":
									return torch.manual_seed(__UpperCAmelCase							)
						return torch.Generator(device=__UpperCAmelCase							).manual_seed(__UpperCAmelCase							)

			@parameterized.expand(
			    [
			        # fmt: off
			        [3_3, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
			        [4_7, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
			        # fmt: on
			    ]							)
			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Union[str, Any]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE__										=   self.get_sd_image(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_generator(__UpperCAmelCase							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					sample_posterior=__UpperCAmelCase							).sample

						assert sample.shape == image.shape

						SCREAMING_SNAKE_CASE__										=   sample[-1, -2:, -2:, :2].flatten().float().cpu()
						SCREAMING_SNAKE_CASE__										=   torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice							)

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

			@parameterized.expand(
			    [
			        # fmt: off
			        [3_3, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
			        [4_7, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
			        # fmt: on
			    ]							)
			@require_torch_gpu
			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.get_sd_vae_model(fpaa=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_sd_image(__UpperCAmelCase     ,					fpaa=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_generator(__UpperCAmelCase							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					sample_posterior=__UpperCAmelCase							).sample

						assert sample.shape == image.shape

						SCREAMING_SNAKE_CASE__										=   sample[-1, -2:, :2, -2:].flatten().float().cpu()
						SCREAMING_SNAKE_CASE__										=   torch.tensor(__UpperCAmelCase							)

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

			@parameterized.expand(
			    [
			        # fmt: off
			        [3_3, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
			        [4_7, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
			        # fmt: on
			    ]							)
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    List[str]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE__										=   self.get_sd_image(__UpperCAmelCase							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							).sample

						assert sample.shape == image.shape

						SCREAMING_SNAKE_CASE__										=   sample[-1, -2:, -2:, :2].flatten().float().cpu()
						SCREAMING_SNAKE_CASE__										=   torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice							)

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

			@parameterized.expand(
			    [
			        # fmt: off
			        [1_3, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
			        [3_7, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
			        # fmt: on
			    ]							)
			@require_torch_gpu
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Tuple							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE__										=   self.get_sd_image(__UpperCAmelCase     ,					shape=(3, 4, 6_4, 6_4)							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model.decode(__UpperCAmelCase							).sample

						assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

						SCREAMING_SNAKE_CASE__										=   sample[-1, -2:, :2, -2:].flatten().cpu()
						SCREAMING_SNAKE_CASE__										=   torch.tensor(__UpperCAmelCase							)

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

			@parameterized.expand(
			    [
			        # fmt: off
			        [2_7, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
			        [1_6, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
			        # fmt: on
			    ]							)
			@require_torch_gpu
			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.get_sd_vae_model(fpaa=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_sd_image(__UpperCAmelCase     ,					shape=(3, 4, 6_4, 6_4)     ,					fpaa=__UpperCAmelCase							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model.decode(__UpperCAmelCase							).sample

						assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

						SCREAMING_SNAKE_CASE__										=   sample[-1, -2:, :2, -2:].flatten().float().cpu()
						SCREAMING_SNAKE_CASE__										=   torch.tensor(__UpperCAmelCase							)

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

			@parameterized.expand([(1_3,), (1_6,), (2_7,)]							)
			@require_torch_gpu
			@unittest.skipIf(not is_xformers_available()     ,					reason="""xformers is not required when using PyTorch 2.0."""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    Dict							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.get_sd_vae_model(fpaa=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_sd_image(__UpperCAmelCase     ,					shape=(3, 4, 6_4, 6_4)     ,					fpaa=__UpperCAmelCase							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model.decode(__UpperCAmelCase							).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model.decode(__UpperCAmelCase							).sample

						assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

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

			@parameterized.expand([(1_3,), (1_6,), (3_7,)]							)
			@require_torch_gpu
			@unittest.skipIf(not is_xformers_available()     ,					reason="""xformers is not required when using PyTorch 2.0."""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    str							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE__										=   self.get_sd_image(__UpperCAmelCase     ,					shape=(3, 4, 6_4, 6_4)							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model.decode(__UpperCAmelCase							).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model.decode(__UpperCAmelCase							).sample

						assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

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





			@parameterized.expand(
			    [
			        # fmt: off
			        [3_3, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
			        [4_7, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
			        # fmt: on
			    ]							)
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    str							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE__										=   self.get_sd_image(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_generator(__UpperCAmelCase							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model.encode(__UpperCAmelCase							).latent_dist
									SCREAMING_SNAKE_CASE__										=   dist.sample(generator=__UpperCAmelCase							)

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

						SCREAMING_SNAKE_CASE__										=   sample[0, -1, -3:, -3:].flatten().cpu()
						SCREAMING_SNAKE_CASE__										=   torch.tensor(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   3e-3 if torch_device != """mps""" else 1e-2
						assert torch_all_close(__UpperCAmelCase     ,					__UpperCAmelCase     ,					atol=__UpperCAmelCase							)





"""simple docstring"""




# flake8: noqa
# Lint as: python3
A_				:      List[str]           =			[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental


