




"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : str      ,       snake_case__   : Union[str, Any] )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				OmegaConf.load(snake_case__ )
				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )["""model"""]
				lowerCAmelCase  =				list(state_dict.keys() )

				# extract state_dict for VQVAE
				lowerCAmelCase  =				{}
				lowerCAmelCase  =				"""first_stage_model."""
				for key in keys:
								if key.startswith(snake_case__ ):
												lowerCAmelCase  =				state_dict[key]

    # extract state_dict for UNetLDM
				lowerCAmelCase  =				{}
				lowerCAmelCase  =				"""model.diffusion_model."""
				for key in keys:
								if key.startswith(snake_case__ ):
												lowerCAmelCase  =				state_dict[key]

				lowerCAmelCase  =				config.model.params.first_stage_config.params
				lowerCAmelCase  =				config.model.params.unet_config.params

				lowerCAmelCase  =				VQModel(**snake_case__ ).eval()
				vqvae.load_state_dict(snake_case__ )

				lowerCAmelCase  =				UNetLDMModel(**snake_case__ ).eval()
				unet.load_state_dict(snake_case__ )

				lowerCAmelCase  =				DDIMScheduler(
				    timesteps=config.model.params.timesteps      ,       beta_schedule="""scaled_linear"""      ,       beta_start=config.model.params.linear_start      ,       beta_end=config.model.params.linear_end      ,       clip_sample=snake_case__      ,       )

				lowerCAmelCase  =				LDMPipeline(snake_case__      ,       snake_case__      ,       snake_case__ )
				pipeline.save_pretrained(snake_case__ )


if __name__ == "__main__":
				a      =							argparse.ArgumentParser()
				parser.add_argument('--checkpoint_path', type=str, required=True)
				parser.add_argument('--config_path', type=str, required=True)
				parser.add_argument('--output_path', type=str, required=True)
				a      =							parser.parse_args()

				convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)











"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				UNetaDModel(
										    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=3						,						out_channels=3						,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,						)
										return model




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.dummy_uncond_unet
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""						,						return_dict=lowerCAmelCase					)[0]

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										lowerCAmelCase  =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCAmelCase  =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2






@slow
@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""google/ncsnpp-celebahq-256"""
										lowerCAmelCase  =				UNetaDModel.from_pretrained(lowerCAmelCase					)
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=20						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										lowerCAmelCase  =				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2





