


import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
	from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm







def 			_lowerCamelCase(      lowercase__      )		->      List[str]:

		'''simple docstring'''







		if is_torch_version('<'  ,					'2.0.0'      ) or not hasattr(_lowerCAmelCase  ,					'_dynamo'      ):
				return False
		return isinstance(_lowerCAmelCase  ,					torch._dynamo.eval_frame.OptimizedModule      )







def 			_lowerCamelCase(      lowercase__  ,					lowercase__ = True      )		->      Union[str, Any]:

		'''simple docstring'''







		__lowercase=     (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

		__lowercase=     is_compiled_module(_lowerCAmelCase      )
		if is_compiled:
				__lowercase=     model
				__lowercase=     model._orig_mod

		if is_deepspeed_available():
				options += (DeepSpeedEngine,)

		while isinstance(_lowerCAmelCase  ,					_lowerCAmelCase      ):
				__lowercase=     model.module

		if not keep_fpaa_wrapper:
				__lowercase=     getattr(_lowerCAmelCase  ,					'forward'      )
				__lowercase=     model.__dict__.pop('_original_forward'  ,					_lowerCAmelCase      )
				if original_forward is not None:
						while hasattr(_lowerCAmelCase  ,					'__wrapped__'      ):
								__lowercase=     forward.__wrapped__
								if forward == original_forward:
										break
						__lowercase=     forward
				if getattr(_lowerCAmelCase  ,					'_converted_to_transformer_engine'  ,					_lowerCAmelCase      ):
						convert_model(_lowerCAmelCase  ,					to_transformer_engine=_lowerCAmelCase      )

		if is_compiled:
				__lowercase=     model
				__lowercase=     compiled_model

		return model







def 			_lowerCamelCase(      )		->      Any:

		'''simple docstring'''







		PartialState().wait_for_everyone()







def 			_lowerCamelCase(      lowercase__  ,					lowercase__      )		->      str:

		'''simple docstring'''







		if PartialState().distributed_type == DistributedType.TPU:
				xm.save(_lowerCAmelCase  ,					_lowerCAmelCase      )
		elif PartialState().local_process_index == 0:
				torch.save(_lowerCAmelCase  ,					_lowerCAmelCase      )







@contextmanager
def 			_lowerCamelCase(      **lowercase__      )		->      Tuple:

		'''simple docstring'''







		for key, value in kwargs.items():
				__lowercase=     str(_lowerCAmelCase      )

		yield

		for key in kwargs:
				if key.upper() in os.environ:
						del os.environ[key.upper()]







def 			_lowerCamelCase(      lowercase__      )		->      Any:

		'''simple docstring'''







		if not hasattr(_lowerCAmelCase  ,					'__qualname__'      ) and not hasattr(_lowerCAmelCase  ,					'__name__'      ):
				__lowercase=     getattr(_lowerCAmelCase  ,					'__class__'  ,					_lowerCAmelCase      )
		if hasattr(_lowerCAmelCase  ,					'__qualname__'      ):
				return obj.__qualname__
		if hasattr(_lowerCAmelCase  ,					'__name__'      ):
				return obj.__name__
		return str(_lowerCAmelCase      )







def 			_lowerCamelCase(      lowercase__  ,					lowercase__      )		->      Union[str, Any]:

		'''simple docstring'''







		for key, value in source.items():
				if isinstance(_lowerCAmelCase  ,					_lowerCAmelCase      ):
						__lowercase=     destination.setdefault(_lowerCAmelCase  ,					{}      )
						merge_dicts(_lowerCAmelCase  ,					_lowerCAmelCase      )
				else:
						__lowercase=     value

		return destination







def 			_lowerCamelCase(      lowercase__ = None      )		->      bool:

		'''simple docstring'''







		if port is None:
				__lowercase=     2_9_5_0_0
		with socket.socket(socket.AF_INET  ,					socket.SOCK_STREAM      ) as s:
				return s.connect_ex(('localhost', port)      ) == 0











'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

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

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

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= 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	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


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)
