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


__lowerCamelCase					:	Dict       =  logging.get_logger(__name__)

enable_full_determinism()






class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							UNetaDModel
					A	       =							'sample'

					@property
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    4
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    3
							SCREAMING_SNAKE_CASE_  :		str				      =    (32, 32)

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    floats_tensor((batch_size, num_channels) + sizes    ).to(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    torch.tensor([10]    ).to(_A    )

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

					@property
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							return (3, 32, 32)

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return (3, 32, 32)






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    {
							    "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,
							}
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.dummy_input
							return init_dict, inputs_dict







class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							UNetaDModel
					A	       =							'sample'

					@property
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    4
							SCREAMING_SNAKE_CASE_  :		str				      =    4
							SCREAMING_SNAKE_CASE_  :		Any				      =    (32, 32)

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    floats_tensor((batch_size, num_channels) + sizes    ).to(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    torch.tensor([10]    ).to(_A    )

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

					@property
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							return (4, 32, 32)

					@property
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							return (4, 32, 32)

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {
							    "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"),
							}
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.dummy_input
							return init_dict, inputs_dict

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update",output_loading_info=_A    )

							self.assertIsNotNone(_A    )
							self.assertEqual(len(loading_info["missing_keys"]    ),0    )

							model.to(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    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 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update",output_loading_info=_A    )
							model.to(_A    )
							SCREAMING_SNAKE_CASE_  :		List[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 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update",output_loading_info=_A    )
							model_accelerate.to(_A    )
							model_accelerate.eval()

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

							SCREAMING_SNAKE_CASE_  :		int				      =    model_accelerate(_A,_A    )["sample"]

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

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    UNetaDModel.from_pretrained(
							    "fusing/unet-ldm-dummy-update",output_loading_info=_A,low_cpu_mem_usage=_A    )
							model_normal_load.to(_A    )
							model_normal_load.eval()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model_normal_load(_A,_A    )["sample"]

							assert torch_all_close(_A,_A,rtol=1E-3    )






					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"    )
							model.eval()
							model.to(_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    torch.randn(
							    1,model.config.in_channels,model.config.sample_size,model.config.sample_size,generator=torch.manual_seed(0    ),)
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    noise.to(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.tensor([10] * noise.shape[0]    ).to(_A    )

							with torch.no_grad():
									SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A,_A    ).sample

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    output[0, -1, -3:, -3:].flatten().cpu()
							# fmt: off
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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(_A,_A,rtol=1E-3    )    )







class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							UNetaDModel
					A	       =							'sample'

					@property
					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       str=(32, 32)    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    4
							SCREAMING_SNAKE_CASE_  :		str				      =    3

							SCREAMING_SNAKE_CASE_  :		str				      =    floats_tensor((batch_size, num_channels) + sizes    ).to(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.tensor(batch_size * [10]    ).to(dtype=torch.intaa,device=_A    )

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

					@property
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							return (3, 32, 32)

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							return (3, 32, 32)

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[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",
							    ],
							}
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.dummy_input
							return init_dict, inputs_dict

					@slow
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256",output_loading_info=_A    )
							self.assertIsNotNone(_A    )
							self.assertEqual(len(loading_info["missing_keys"]    ),0    )

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

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

					@slow
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"    )
							model.to(_A    )

							SCREAMING_SNAKE_CASE_  :		str				      =    4
							SCREAMING_SNAKE_CASE_  :		Dict				      =    3
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    (256, 256)

							SCREAMING_SNAKE_CASE_  :		str				      =    torch.ones((batch_size, num_channels) + sizes    ).to(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.tensor(batch_size * [1E-4]    ).to(_A    )

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

							SCREAMING_SNAKE_CASE_  :		int				      =    output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							SCREAMING_SNAKE_CASE_  :		int				      =    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(_A,_A,rtol=1E-2    )    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"    )
							model.to(_A    )

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

							SCREAMING_SNAKE_CASE_  :		int				      =    torch.ones((batch_size, num_channels) + sizes    ).to(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    torch.tensor(batch_size * [1E-4]    ).to(_A    )

							with torch.no_grad():
									SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A,_A    ).sample

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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(_A,_A,rtol=1E-2    )    )






					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							pass

from typing import TYPE_CHECKING

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


__lowerCamelCase					:	Dict       =  {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Any       =  [
		    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMAEForPreTraining''',
		    '''ViTMAELayer''',
		    '''ViTMAEModel''',
		    '''ViTMAEPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Optional[Any]       =  [
		    '''TFViTMAEForPreTraining''',
		    '''TFViTMAEModel''',
		    '''TFViTMAEPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_mae import (
				    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMAEForPreTraining,
				    ViTMAELayer,
				    ViTMAEModel,
				    ViTMAEPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
		import sys

		__lowerCamelCase					:	Any       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
