
'''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    :							Dict      =					logging.get_logger(__name__)

enable_full_determinism()


class 						__UpperCAmelCase      (					_lowerCamelCase						,   _lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       UNetaDModel
							__lowercase      		=       """sample"""


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		4
								_snake_case   						=		3
								_snake_case   						=		(32, 32)

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

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


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return (3, 32, 32)


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return (3, 32, 32)




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		{
								    '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,
								}
								_snake_case   						=		self.dummy_input
								return init_dict, inputs_dict


class 						__UpperCAmelCase      (					_lowerCamelCase						,   _lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       UNetaDModel
							__lowercase      		=       """sample"""


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		4
								_snake_case   						=		4
								_snake_case   						=		(32, 32)

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

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


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return (4, 32, 32)


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return (4, 32, 32)


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		{
								    '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'),
								}
								_snake_case   						=		self.dummy_input
								return init_dict, inputs_dict


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case						, _snake_case   						=		UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,   output_loading_info=lowerCAmelCase_  )

								self.assertIsNotNone(lowerCAmelCase_  )
								self.assertEqual(len(loading_info['missing_keys']  )    ,   0  )

								model.to(lowerCAmelCase_  )
								_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case						, _snake_case   						=		UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,   output_loading_info=lowerCAmelCase_  )
								model.to(lowerCAmelCase_  )
								_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case						, _snake_case   						=		UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,   output_loading_info=lowerCAmelCase_  )
								model_accelerate.to(lowerCAmelCase_  )
								model_accelerate.eval()

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

								_snake_case   						=		model_accelerate(lowerCAmelCase_    ,   lowerCAmelCase_  )['sample']

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

								_snake_case						, _snake_case   						=		UNetaDModel.from_pretrained(
								    'fusing/unet-ldm-dummy-update'    ,   output_loading_info=lowerCAmelCase_    ,   low_cpu_mem_usage=lowerCAmelCase_  )
								model_normal_load.to(lowerCAmelCase_  )
								model_normal_load.eval()
								_snake_case   						=		model_normal_load(lowerCAmelCase_    ,   lowerCAmelCase_  )['sample']

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




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'  )
								model.eval()
								model.to(lowerCAmelCase_  )

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

								with torch.no_grad():
									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  ).sample

								_snake_case   						=		output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								_snake_case   						=		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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   rtol=1E-3  )  )


class 						__UpperCAmelCase      (					_lowerCamelCase						,   _lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       UNetaDModel
							__lowercase      		=       """sample"""


							@property
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_=(32, 32)  ):





								"""simple docstring"""






								_snake_case   						=		4
								_snake_case   						=		3

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

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


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return (3, 32, 32)


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return (3, 32, 32)


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		{
								    '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',
								    ],
								}
								_snake_case   						=		self.dummy_input
								return init_dict, inputs_dict


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case						, _snake_case   						=		UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'    ,   output_loading_info=lowerCAmelCase_  )
								self.assertIsNotNone(lowerCAmelCase_  )
								self.assertEqual(len(loading_info['missing_keys']  )    ,   0  )

								model.to(lowerCAmelCase_  )
								_snake_case   						=		self.dummy_input
								_snake_case   						=		floats_tensor((4, 3) + (2_56, 2_56)  ).to(lowerCAmelCase_  )
								_snake_case   						=		noise
								_snake_case   						=		model(**lowerCAmelCase_  )

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


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'  )
								model.to(lowerCAmelCase_  )

								_snake_case   						=		4
								_snake_case   						=		3
								_snake_case   						=		(2_56, 2_56)

								_snake_case   						=		torch.ones((batch_size, num_channels) + sizes  ).to(lowerCAmelCase_  )
								_snake_case   						=		torch.tensor(batch_size * [1E-4]  ).to(lowerCAmelCase_  )

								with torch.no_grad():
									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  ).sample

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'  )
								model.to(lowerCAmelCase_  )

								_snake_case   						=		4
								_snake_case   						=		3
								_snake_case   						=		(32, 32)

								_snake_case   						=		torch.ones((batch_size, num_channels) + sizes  ).to(lowerCAmelCase_  )
								_snake_case   						=		torch.tensor(batch_size * [1E-4]  ).to(lowerCAmelCase_  )

								with torch.no_grad():
									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  ).sample

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




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								pass



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :							List[str]      =					{
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[int]      =					["Pix2StructImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					[
					    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Pix2StructPreTrainedModel",
					    "Pix2StructForConditionalGeneration",
					    "Pix2StructVisionModel",
					    "Pix2StructTextModel",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					lowercase    :							List[str]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

