






'''simple docstring'''







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 						A__			(				*__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												lowerCamelCase__         =  list(__lowerCAmelCase						)
						for i in range(len(__lowerCAmelCase						)						):
												lowerCamelCase__         =  None
						gc.collect()
						if is_xpu_available():
												torch.xpu.empty_cache()
						elif is_npu_available():
												torch.npu.empty_cache()
						else:
												torch.cuda.empty_cache()
						return objects
def 						A__			(				__lowerCAmelCase		:			Exception						):
						lowerCamelCase__         =  [
						    """CUDA out of memory.""",  # CUDA OOM
						    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
						    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
						]
						if isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						) and len(exception.args						) == 1:
												return any(err in exception.args[0] for err in _statements						)
						return False





def 						A__			(				__lowerCAmelCase		:			callable = None   ,    __lowerCAmelCase		:			int = 128						):
						if function is None:
												return functools.partial(__lowerCAmelCase   ,    starting_batch_size=__lowerCAmelCase						)

						lowerCamelCase__         =  starting_batch_size

						def decorator(*__lowerCAmelCase		:			Optional[Any]   ,    **__lowerCAmelCase		:			int						):
												nonlocal batch_size
												gc.collect()
												if is_xpu_available():
																		torch.xpu.empty_cache()
												elif is_npu_available():
																		torch.npu.empty_cache()
												else:
																		torch.cuda.empty_cache()
												lowerCamelCase__         =  list(inspect.signature(__lowerCAmelCase						).parameters.keys()						)
												# Guard against user error
												if len(__lowerCAmelCase						) < (len(__lowerCAmelCase						) + 1):
																		lowerCamelCase__         =  """, """.join([F'''{arg}={value}''' for arg, value in zip(params[1:]   ,    args[1:]						)]						)
																		raise TypeError(
																		    F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
																		    F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''						)
												while True:
																		if batch_size == 0:
																								raise RuntimeError("""No executable batch size found, reached zero."""						)
																		try:
																								return function(__lowerCAmelCase   ,    *__lowerCAmelCase   ,    **__lowerCAmelCase						)
																		except Exception as e:
																								if should_reduce_batch_size(__lowerCAmelCase						):
																														gc.collect()
																														if is_xpu_available():
																																				torch.xpu.empty_cache()
																														elif is_npu_available():
																																				torch.npu.empty_cache()
																														else:
																																				torch.cuda.empty_cache()
																														batch_size //= 2
																								else:
																														raise

						return decorator









'''simple docstring'''







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  OmegaConf.load(__lowerCAmelCase						)
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    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(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

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

						lowerCamelCase__         =  config.model.params.first_stage_config.params
						lowerCamelCase__         =  config.model.params.unet_config.params

						lowerCamelCase__         =  VQModel(**__lowerCAmelCase						).eval()
						vqvae.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  UNetLDMModel(**__lowerCAmelCase						).eval()
						unet.load_state_dict(__lowerCAmelCase						)

						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=__lowerCAmelCase   ,    )

						lowerCamelCase__         =  LDMPipeline(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						pipeline.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
							UpperCamelCase    :      Optional[int]  			=		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)
							UpperCamelCase    :      List[Any]  			=		parser.parse_args()

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

