


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    '''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionImg2ImgPipeline` instead.'''
)







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


snake_case							:	Tuple					      =				logging.get_logger(__name__)

enable_full_determinism()







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   str   				=      UNetaDModel
					UpperCAmelCase__ :   str   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   List[str]:
												a__					=					4
												a__					=					3
												a__					=					(32, 32)

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

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Tuple:
												return (3, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[Any]:
												return (3, 32, 32)



					def 			lowerCamelCase__(				self       :str				) ->   Tuple:
												a__					=					{
												    '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,
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      UNetaDModel
					UpperCAmelCase__ :   Any   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   List[str]:
												a__					=					4
												a__					=					4
												a__					=					(32, 32)

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

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   str:
												return (4, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   Dict:
												return (4, 32, 32)
					def 			lowerCamelCase__(				self       :int				) ->   int:
												a__					=					{
												    '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'),
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict
					def 			lowerCamelCase__(				self       :str				) ->   Any:
												a__				, a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case				)

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

												model.to(__snake_case				)
												a__					=					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       :Tuple				) ->   Optional[int]:
												a__				, a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case				)
												model.to(__snake_case				)
												a__					=					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       :Union[str, Any]				) ->   int:
												# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
												a__				, a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case				)
												model_accelerate.to(__snake_case				)
												model_accelerate.eval()

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

												a__					=					model_accelerate(__snake_case      ,__snake_case				)['sample']

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

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

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



					def 			lowerCamelCase__(				self       :str				) ->   Union[str, Any]:
												a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'				)
												model.eval()
												model.to(__snake_case				)

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

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -1, -3:, -3:].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]				)
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case      ,__snake_case      ,rtol=1E-3				)				)







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Dict   				=      UNetaDModel
					UpperCAmelCase__ :   Optional[Any]   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :List[Any]=(32, 32)				) ->   Optional[int]:
												a__					=					4
												a__					=					3

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

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return (3, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[int]:
												return (3, 32, 32)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												a__					=					{
												    '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',
												    ],
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict
					@slow
					def 			lowerCamelCase__(				self       :str				) ->   Tuple:
												a__				, a__					=					UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'      ,output_loading_info=__snake_case				)
												self.assertIsNotNone(__snake_case				)
												self.assertEqual(len(loading_info['missing_keys']				)      ,0				)

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

												assert image is not None, "Make sure output is not None"
					@slow
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Dict:
												a__					=					UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'				)
												model.to(__snake_case				)

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

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

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]				)
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case      ,__snake_case      ,rtol=1E-2				)				)
					def 			lowerCamelCase__(				self       :Dict				) ->   int:
												a__					=					UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'				)
												model.to(__snake_case				)

												a__					=					4
												a__					=					3
												a__					=					(32, 32)

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

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]				)
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case      ,__snake_case      ,rtol=1E-2				)				)



					def 			lowerCamelCase__(				self       :int				) ->   str:
												# not required for this model
												pass




