






"""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


_lowerCAmelCase         =							logging.get_logger(__name__)

enable_full_determinism()



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				List[str]    =				UNetaDModel
	_A       :				Union[str, Any]    =				"""sample"""





	@property
	def    lowerCamelCase(self							):
		A_		:						int	    =   4
		A_		:						Union[str, Any]	    =   3
		A_		:						List[Any]	    =   (32, 32)

		A_		:						List[str]	    =   floats_tensor((batch_size, num_channels) + sizes							).to(lowerCAmelCase_							)
		A_		:						Dict	    =   torch.tensor([10]							).to(lowerCAmelCase_							)

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





	@property
	def    lowerCamelCase(self							):
		return (3, 32, 32)





	@property
	def    lowerCamelCase(self							):
		return (3, 32, 32)





	def    lowerCamelCase(self							):
		A_		:						int	    =   {
		    """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_		:						List[Any]	    =   self.dummy_input
		return init_dict, inputs_dict






class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				int    =				UNetaDModel
	_A       :				List[Any]    =				"""sample"""





	@property
	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   4
		A_		:						List[str]	    =   4
		A_		:						Dict	    =   (32, 32)

		A_		:						Dict	    =   floats_tensor((batch_size, num_channels) + sizes							).to(lowerCAmelCase_							)
		A_		:						str	    =   torch.tensor([10]							).to(lowerCAmelCase_							)

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





	@property
	def    lowerCamelCase(self							):
		return (4, 32, 32)





	@property
	def    lowerCamelCase(self							):
		return (4, 32, 32)





	def    lowerCamelCase(self							):
		A_		:						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"""),
		}
		A_		:						List[str]	    =   self.dummy_input
		return init_dict, inputs_dict





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Any	    =   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_							)
		A_		:						List[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    lowerCamelCase(self							):
		A_    ,     A_		:						Dict	    =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     ,     output_loading_info=lowerCAmelCase_							)
		model.to(lowerCAmelCase_							)
		A_		:						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    lowerCamelCase(self							):
		# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
		A_    ,     A_		:						List[str]	    =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     ,     output_loading_info=lowerCAmelCase_							)
		model_accelerate.to(lowerCAmelCase_							)
		model_accelerate.eval()

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

		A_		:						List[str]	    =   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()

		A_    ,     A_		:						int	    =   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()
		A_		:						List[str]	    =   model_normal_load(lowerCAmelCase_     ,     lowerCAmelCase_							)["""sample"""]

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





	def    lowerCamelCase(self							):
		A_		:						int	    =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							)
		model.eval()
		model.to(lowerCAmelCase_							)

		A_		:						Dict	    =   torch.randn(
		    1     ,     model.config.in_channels     ,     model.config.sample_size     ,     model.config.sample_size     ,     generator=torch.manual_seed(0							)     ,     )
		A_		:						Optional[int]	    =   noise.to(lowerCAmelCase_							)
		A_		:						List[Any]	    =   torch.tensor([10] * noise.shape[0]							).to(lowerCAmelCase_							)

		with torch.no_grad():
			A_		:						Union[str, Any]	    =   model(lowerCAmelCase_     ,     lowerCAmelCase_							).sample

		A_		:						Optional[Any]	    =   output[0, -1, -3:, -3:].flatten().cpu()
		# fmt: off
		A_		:						Optional[int]	    =   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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				UNetaDModel
	_A       :				Optional[int]    =				"""sample"""





	@property
	def    lowerCamelCase(self     ,     lowerCAmelCase_=(32, 32)							):
		A_		:						int	    =   4
		A_		:						Any	    =   3

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

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





	@property
	def    lowerCamelCase(self							):
		return (3, 32, 32)





	@property
	def    lowerCamelCase(self							):
		return (3, 32, 32)





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   {
		    """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_		:						int	    =   self.dummy_input
		return init_dict, inputs_dict





	@slow
	def    lowerCamelCase(self							):
		A_    ,     A_		:						str	    =   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_							)
		A_		:						Tuple	    =   self.dummy_input
		A_		:						List[Any]	    =   floats_tensor((4, 3) + (256, 256)							).to(lowerCAmelCase_							)
		A_		:						List[Any]	    =   noise
		A_		:						str	    =   model(**lowerCAmelCase_							)

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





	@slow
	def    lowerCamelCase(self							):
		A_		:						Dict	    =   UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""							)
		model.to(lowerCAmelCase_							)

		A_		:						str	    =   4
		A_		:						str	    =   3
		A_		:						Optional[Any]	    =   (256, 256)

		A_		:						Optional[int]	    =   torch.ones((batch_size, num_channels) + sizes							).to(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   torch.tensor(batch_size * [1e-4]							).to(lowerCAmelCase_							)

		with torch.no_grad():
			A_		:						List[Any]	    =   model(lowerCAmelCase_     ,     lowerCAmelCase_							).sample

		A_		:						Any	    =   output[0, -3:, -3:, -1].flatten().cpu()
		# fmt: off
		A_		:						List[Any]	    =   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							):
		A_		:						Tuple	    =   UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""							)
		model.to(lowerCAmelCase_							)

		A_		:						Dict	    =   4
		A_		:						Tuple	    =   3
		A_		:						Union[str, Any]	    =   (32, 32)

		A_		:						Tuple	    =   torch.ones((batch_size, num_channels) + sizes							).to(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   torch.tensor(batch_size * [1e-4]							).to(lowerCAmelCase_							)

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

		A_		:						Optional[int]	    =   output[0, -3:, -3:, -1].flatten().cpu()
		# fmt: off
		A_		:						Dict	    =   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							):
		# not required for this model
		pass









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

    # extract state_dict for UNetLDM
	A_		:						Tuple	    =   {}
	A_		:						Dict	    =   """model.diffusion_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						Optional[Any]	    =   state_dict[key]

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						List[str]	    =   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__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
							_lowerCAmelCase         =							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)
							_lowerCAmelCase         =							parser.parse_args()

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

