





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 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

			'''simple docstring'''


			a		:							Any     	=	AutoencoderKL
			a		:							str     	=	"sample"
			a		:							List[Any]     	=	1E-2




			@property
			def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






							"""simple docstring"""
							UpperCamelCase__							:       str      =   4
							UpperCamelCase__							:       Tuple      =   3
							UpperCamelCase__							:       Tuple      =   (32, 32)

							UpperCamelCase__							:       Optional[int]      =   floats_tensor((batch_size, num_channels) + sizes							).to(__magic_name__							)

							return {"sample": image}




			@property
			def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






							"""simple docstring"""
							return (3, 32, 32)




			@property
			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							return (3, 32, 32)




			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       List[Any]      =   {
							    '''block_out_channels''': [32, 64],
							    '''in_channels''': 3,
							    '''out_channels''': 3,
							    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
							    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
							    '''latent_channels''': 4,
							}
							UpperCamelCase__							:       Any      =   self.dummy_input
							return init_dict, inputs_dict




			def    UpperCamelCase__						(					self							)				->   Optional[int]:






							"""simple docstring"""
							pass




			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							pass




			@unittest.skipIf(torch_device == '''mps''',	'''Gradient checkpointing skipped on MPS'''							)
			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       List[str]      =   self.prepare_init_args_and_inputs_for_common()
							UpperCamelCase__							:       int      =   self.model_class(**__magic_name__							)
							model.to(__magic_name__							)

							assert not model.is_gradient_checkpointing and model.training

							UpperCamelCase__							:       List[str]      =   model(**__magic_name__							).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()

							UpperCamelCase__							:       Optional[int]      =   torch.randn_like(__magic_name__							)
							UpperCamelCase__							:       Union[str, Any]      =   (out - labels).mean()
							loss.backward()

							# re-instantiate the model now enabling gradient checkpointing
							UpperCamelCase__							:       Union[str, Any]      =   self.model_class(**__magic_name__							)
							# clone model
							model_a.load_state_dict(model.state_dict()							)
							model_a.to(__magic_name__							)
							model_a.enable_gradient_checkpointing()

							assert model_a.is_gradient_checkpointing and model_a.training

							UpperCamelCase__							:       Optional[int]      =   model_a(**__magic_name__							).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()
							UpperCamelCase__							:       Dict      =   (out_a - labels).mean()
							loss_a.backward()

							# compare the output and parameters gradients
							self.assertTrue((loss - loss_a).abs() < 1E-5							)
							UpperCamelCase__							:       Dict      =   dict(model.named_parameters()							)
							UpperCamelCase__							:       Optional[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    UpperCamelCase__						(					self							)				->   Dict:






							"""simple docstring"""
							UpperCamelCase__							:       List[Any]      =   AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''',	output_loading_info=__magic_name__							)
							self.assertIsNotNone(__magic_name__							)
							self.assertEqual(len(loading_info['''missing_keys''']							),	0							)

							model.to(__magic_name__							)
							UpperCamelCase__							:       str      =   model(**self.dummy_input							)

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




			def    UpperCamelCase__						(					self							)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''							)
							UpperCamelCase__							:       Any      =   model.to(__magic_name__							)
							model.eval()

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

							UpperCamelCase__							:       Any      =   torch.randn(
							    1,	model.config.in_channels,	model.config.sample_size,	model.config.sample_size,	generator=torch.manual_seed(0							),	)
							UpperCamelCase__							:       Optional[int]      =   image.to(__magic_name__							)
							with torch.no_grad():
											UpperCamelCase__							:       int      =   model(__magic_name__,	sample_posterior=__magic_name__,	generator=__magic_name__							).sample

							UpperCamelCase__							:       int      =   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":
											UpperCamelCase__							:       Any      =   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":
											UpperCamelCase__							:       Optional[int]      =   torch.tensor(
											    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]							)
							else:
											UpperCamelCase__							:       Union[str, Any]      =   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(__magic_name__,	__magic_name__,	rtol=1E-2							)							)






@slow
class 					lowercase__ (			unittest.TestCase       ):

			'''simple docstring'''


			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[Any]:






							"""simple docstring"""
							return f"gaussian_noise_s={seed}_shape={'_'.join([str(__magic_name__							) for s in shape]							)}.npy"




			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




			def    UpperCamelCase__						(					self,	__magic_name__=0,	__magic_name__=(4, 3, 512, 512),	__magic_name__=False							)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       int      =   torch.floataa if fpaa else torch.floataa
							UpperCamelCase__							:       Any      =   torch.from_numpy(load_hf_numpy(self.get_file_format(__magic_name__,	__magic_name__							)							)							).to(__magic_name__							).to(__magic_name__							)
							return image




			def    UpperCamelCase__						(					self,	__magic_name__="CompVis/stable-diffusion-v1-4",	__magic_name__=False							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   '''fp16''' if fpaa else None
							UpperCamelCase__							:       List[Any]      =   torch.floataa if fpaa else torch.floataa

							UpperCamelCase__							:       int      =   AutoencoderKL.from_pretrained(
							    __magic_name__,	subfolder='''vae''',	torch_dtype=__magic_name__,	revision=__magic_name__,	)
							model.to(__magic_name__							).eval()

							return model




			def    UpperCamelCase__						(					self,	__magic_name__=0							)				->   Optional[int]:






							"""simple docstring"""
							if torch_device == "mps":
											return torch.manual_seed(__magic_name__							)
							return torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)




			@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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[Any]      =   self.get_sd_vae_model()
							UpperCamelCase__							:       Optional[int]      =   self.get_sd_image(__magic_name__							)
							UpperCamelCase__							:       Tuple      =   self.get_generator(__magic_name__							)

							with torch.no_grad():
											UpperCamelCase__							:       Optional[Any]      =   model(__magic_name__,	generator=__magic_name__,	sample_posterior=__magic_name__							).sample

							assert sample.shape == image.shape

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

							assert torch_all_close(__magic_name__,	__magic_name__,	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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__							:       Dict      =   self.get_sd_vae_model(fpaa=__magic_name__							)
							UpperCamelCase__							:       str      =   self.get_sd_image(__magic_name__,	fpaa=__magic_name__							)
							UpperCamelCase__							:       Union[str, Any]      =   self.get_generator(__magic_name__							)

							with torch.no_grad():
											UpperCamelCase__							:       Union[str, Any]      =   model(__magic_name__,	generator=__magic_name__,	sample_posterior=__magic_name__							).sample

							assert sample.shape == image.shape

							UpperCamelCase__							:       List[Any]      =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
							UpperCamelCase__							:       Dict      =   torch.tensor(__magic_name__							)

							assert torch_all_close(__magic_name__,	__magic_name__,	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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Union[str, Any]:






							"""simple docstring"""
							UpperCamelCase__							:       List[Any]      =   self.get_sd_vae_model()
							UpperCamelCase__							:       Optional[int]      =   self.get_sd_image(__magic_name__							)

							with torch.no_grad():
											UpperCamelCase__							:       Union[str, Any]      =   model(__magic_name__							).sample

							assert sample.shape == image.shape

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

							assert torch_all_close(__magic_name__,	__magic_name__,	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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   Optional[Any]:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   self.get_sd_vae_model()
							UpperCamelCase__							:       int      =   self.get_sd_image(__magic_name__,	shape=(3, 4, 64, 64)							)

							with torch.no_grad():
											UpperCamelCase__							:       Optional[Any]      =   model.decode(__magic_name__							).sample

							assert list(sample.shape							) == [3, 3, 512, 512]

							UpperCamelCase__							:       Optional[Any]      =   sample[-1, -2:, :2, -2:].flatten().cpu()
							UpperCamelCase__							:       Optional[Any]      =   torch.tensor(__magic_name__							)

							assert torch_all_close(__magic_name__,	__magic_name__,	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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   Optional[int]:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   self.get_sd_vae_model(fpaa=__magic_name__							)
							UpperCamelCase__							:       Any      =   self.get_sd_image(__magic_name__,	shape=(3, 4, 64, 64),	fpaa=__magic_name__							)

							with torch.no_grad():
											UpperCamelCase__							:       List[Any]      =   model.decode(__magic_name__							).sample

							assert list(sample.shape							) == [3, 3, 512, 512]

							UpperCamelCase__							:       List[Any]      =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
							UpperCamelCase__							:       Tuple      =   torch.tensor(__magic_name__							)

							assert torch_all_close(__magic_name__,	__magic_name__,	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    UpperCamelCase__						(					self,	__magic_name__							)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[Any]      =   self.get_sd_vae_model(fpaa=__magic_name__							)
							UpperCamelCase__							:       List[str]      =   self.get_sd_image(__magic_name__,	shape=(3, 4, 64, 64),	fpaa=__magic_name__							)

							with torch.no_grad():
											UpperCamelCase__							:       int      =   model.decode(__magic_name__							).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
											UpperCamelCase__							:       Optional[Any]      =   model.decode(__magic_name__							).sample

							assert list(sample.shape							) == [3, 3, 512, 512]

							assert torch_all_close(__magic_name__,	__magic_name__,	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    UpperCamelCase__						(					self,	__magic_name__							)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       Tuple      =   self.get_sd_vae_model()
							UpperCamelCase__							:       Tuple      =   self.get_sd_image(__magic_name__,	shape=(3, 4, 64, 64)							)

							with torch.no_grad():
											UpperCamelCase__							:       Any      =   model.decode(__magic_name__							).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
											UpperCamelCase__							:       Optional[Any]      =   model.decode(__magic_name__							).sample

							assert list(sample.shape							) == [3, 3, 512, 512]

							assert torch_all_close(__magic_name__,	__magic_name__,	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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[Any]:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[Any]      =   self.get_sd_vae_model()
							UpperCamelCase__							:       Tuple      =   self.get_sd_image(__magic_name__							)
							UpperCamelCase__							:       int      =   self.get_generator(__magic_name__							)

							with torch.no_grad():
											UpperCamelCase__							:       Tuple      =   model.encode(__magic_name__							).latent_dist
											UpperCamelCase__							:       str      =   dist.sample(generator=__magic_name__							)

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

							UpperCamelCase__							:       List[str]      =   sample[0, -1, -3:, -3:].flatten().cpu()
							UpperCamelCase__							:       int      =   torch.tensor(__magic_name__							)

							UpperCamelCase__							:       Tuple      =   3E-3 if torch_device != '''mps''' else 1E-2
							assert torch_all_close(__magic_name__,	__magic_name__,	atol=__magic_name__							)








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_  							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

