


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_   (a_	,						a_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    str          =			UNetaDModel
					UpperCAmelCase__					:    Union[str, Any]          =			'''sample'''
					@property
					def  lowerCamelCase__(      self   :Dict      )					->    int:
												a__             =     4
												a__             =     3
												a__             =     (32, 32)

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

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



					def  lowerCamelCase__(      self   :Any      )					->    List[Any]:
												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_   (a_	,						a_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Optional[int]          =			UNetaDModel
					UpperCAmelCase__					:    Dict          =			'''sample'''
					@property
					def  lowerCamelCase__(      self   :Any      )					->    Tuple:
												a__             =     4
												a__             =     4
												a__             =     (32, 32)

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

												return {"sample": noise, "timestep": time_step}
					@property
					def  lowerCamelCase__(      self   :Tuple      )					->    Optional[int]:
												return (4, 32, 32)
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    List[Any]:
												return (4, 32, 32)
					def  lowerCamelCase__(      self   :Any      )					->    List[str]:
												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   :Tuple      )					->    Any:
												a__      ,					a__             =     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      )
												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      )					->    List[str]:
												a__      ,					a__             =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'			,output_loading_info=_A      )
												model.to(_A      )
												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   :str      )					->    Tuple:
												a__      ,					a__             =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'			,output_loading_info=_A      )
												model_accelerate.to(_A      )
												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(_A      )
												a__             =     torch.tensor([10] * noise.shape[0]      ).to(_A      )

												a__             =     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()

												a__      ,					a__             =     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()
												a__             =     model_normal_load(_A			,_A      )['sample']

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



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

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

												with torch.no_grad():
																			a__             =     model(_A			,_A      ).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(_A			,_A			,rtol=1E-3      )      )







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

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

												return {"sample": noise, "timestep": time_step}
					@property
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Tuple:
												return (3, 32, 32)
					@property
					def  lowerCamelCase__(      self   :str      )					->    str:
												return (3, 32, 32)
					def  lowerCamelCase__(      self   :List[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   :List[Any]      )					->    Optional[Any]:
												a__      ,					a__             =     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      )
												a__             =     self.dummy_input
												a__             =     floats_tensor((4, 3) + (2_56, 2_56)      ).to(_A      )
												a__             =     noise
												a__             =     model(**_A      )

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

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

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

												with torch.no_grad():
																			a__             =     model(_A			,_A      ).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(_A			,_A			,rtol=1E-2      )      )
					def  lowerCamelCase__(      self   :Any      )					->    int:
												a__             =     UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'      )
												model.to(_A      )

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

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

												with torch.no_grad():
																			a__             =     model(_A			,_A      ).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(_A			,_A			,rtol=1E-2      )      )



					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[int]:
												pass











from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 	A_       (a_			):
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__()
				self.register_modules(unet=_A							,					scheduler=_A		)







	@torch.no_grad()
	def __call__(    self							,					_A = 1							,					_A = 5_0							,					_A = None							,					_A = "pil"							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.unet.config.sample_size
				UpperCAmelCase   		=				(batch_size, 3, img_size, img_size)

				UpperCAmelCase   		=				self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				UpperCAmelCase   		=				randn_tensor(_A							,					generator=_A							,					device=self.device		) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_A		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							UpperCAmelCase   		=				self.scheduler.schedule[t]
							UpperCAmelCase   		=				self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							UpperCAmelCase		,			UpperCAmelCase   		=				self.scheduler.add_noise_to_input(_A							,					_A							,					generator=_A		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							UpperCAmelCase   		=				(sigma_hat / 2) * model((sample_hat + 1) / 2							,					sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							UpperCAmelCase   		=				self.scheduler.step(_A							,					_A							,					_A							,					_A		)

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										UpperCAmelCase   		=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,					sigma_prev / 2		).sample
										UpperCAmelCase   		=				self.scheduler.step_correct(
										    _A							,					_A							,					_A							,					_A							,					step_output.prev_sample							,					step_output['''derivative''']							,					)
							UpperCAmelCase   		=				step_output.prev_sample

				UpperCAmelCase   		=				(sample / 2 + 0.5).clamp(0							,					1		)
				UpperCAmelCase   		=				sample.cpu().permute(0							,					2							,					3							,					1		).numpy()
				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)



