




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


__A						:							Optional[int]							=  logging.get_logger(__name__)

enable_full_determinism()




class 			UpperCAmelCase_      (   A		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     UNetaDModel
							a__						  =     '''sample'''





							@property
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					4
										SCREAMING_SNAKE_CASE           =					3
										SCREAMING_SNAKE_CASE           =					(32, 32)

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

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





							@property
							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										return (3, 32, 32)





							@property
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Dict:
										return (3, 32, 32)







							def    _UpperCAmelCase     (					self       :	str			)			->					List[Any]:
										SCREAMING_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,
										}
										SCREAMING_SNAKE_CASE           =					self.dummy_input
										return init_dict, inputs_dict





class 			UpperCAmelCase_      (   A		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     UNetaDModel
							a__						  =     '''sample'''





							@property
							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					4
										SCREAMING_SNAKE_CASE           =					4
										SCREAMING_SNAKE_CASE           =					(32, 32)

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

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





							@property
							def    _UpperCAmelCase     (					self       :	str			)			->					int:
										return (4, 32, 32)





							@property
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Tuple:
										return (4, 32, 32)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					str:
										SCREAMING_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"""),
										}
										SCREAMING_SNAKE_CASE           =					self.dummy_input
										return init_dict, inputs_dict





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

										SCREAMING_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			)       ,     )
										SCREAMING_SNAKE_CASE           =					noise.to(a			)
										SCREAMING_SNAKE_CASE           =					torch.tensor([10] * noise.shape[0]			).to(a			)

										SCREAMING_SNAKE_CASE           =					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()

										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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()
										SCREAMING_SNAKE_CASE           =					model_normal_load(a       ,     a			)["""sample"""]

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







							def    _UpperCAmelCase     (					self       :	Dict			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""			)
										model.eval()
										model.to(a			)

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

										with torch.no_grad():
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample

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





class 			UpperCAmelCase_      (   A		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     UNetaDModel
							a__						  =     '''sample'''





							@property
							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Dict=(32, 32)			)			->					str:
										SCREAMING_SNAKE_CASE           =					4
										SCREAMING_SNAKE_CASE           =					3

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

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





							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Dict:
										return (3, 32, 32)





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										return (3, 32, 32)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[str]:
										SCREAMING_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""",
										    ],
										}
										SCREAMING_SNAKE_CASE           =					self.dummy_input
										return init_dict, inputs_dict





							@slow
							def    _UpperCAmelCase     (					self       :	Dict			)			->					str:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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			)
										SCREAMING_SNAKE_CASE           =					self.dummy_input
										SCREAMING_SNAKE_CASE           =					floats_tensor((4, 3) + (256, 256)			).to(a			)
										SCREAMING_SNAKE_CASE           =					noise
										SCREAMING_SNAKE_CASE           =					model(**a			)

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





							@slow
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					str:
										SCREAMING_SNAKE_CASE           =					UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""			)
										model.to(a			)

										SCREAMING_SNAKE_CASE           =					4
										SCREAMING_SNAKE_CASE           =					3
										SCREAMING_SNAKE_CASE           =					(256, 256)

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

										with torch.no_grad():
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample

										SCREAMING_SNAKE_CASE           =					output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										SCREAMING_SNAKE_CASE           =					torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608]			)
										# fmt: on

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





							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										SCREAMING_SNAKE_CASE           =					UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""			)
										model.to(a			)

										SCREAMING_SNAKE_CASE           =					4
										SCREAMING_SNAKE_CASE           =					3
										SCREAMING_SNAKE_CASE           =					(32, 32)

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

										with torch.no_grad():
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample

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







							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										# not required for this model
										pass












import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__A						:							Tuple							=  argparse.ArgumentParser()

		parser.add_argument(
		    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
		)
		parser.add_argument(
		    """--original_config_file""",
		    type=str,
		    required=True,
		    help="""The YAML config file corresponding to the original architecture.""",
		)
		parser.add_argument(
		    """--num_in_channels""",
		    default=None,
		    type=int,
		    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
		)
		parser.add_argument(
		    """--image_size""",
		    default=5_1_2,
		    type=int,
		    help=(
		        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
		        """ Base. Use 768 for Stable Diffusion v2."""
		    ),
		)
		parser.add_argument(
		    """--extract_ema""",
		    action="""store_true""",
		    help=(
		        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
		        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
		        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
		    ),
		)
		parser.add_argument(
		    """--upcast_attention""",
		    action="""store_true""",
		    help=(
		        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
		        """ diffusion 2.1."""
		    ),
		)
		parser.add_argument(
		    """--from_safetensors""",
		    action="""store_true""",
		    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
		)
		parser.add_argument(
		    """--to_safetensors""",
		    action="""store_true""",
		    help="""Whether to store pipeline in safetensors format or not.""",
		)
		parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
		parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
		def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
					'''simple docstring'''
					if string == "True":
								return True
					elif string == "False":
								return False
					else:
								raise ValueError(f"""could not parse string as bool {string}"""   )

		parser.add_argument(
		    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
		)

		parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

		__A						:							Union[str, Any]							=  parser.parse_args()

		__A						:							Any							=  download_controlnet_from_original_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    extract_ema=args.extract_ema,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    use_linear_projection=args.use_linear_projection,
		    cross_attention_dim=args.cross_attention_dim,
		)




		controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






