






"""simple docstring"""







from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)





class 	UpperCamelCase_			(							a__	):



  def __init__(   self			,		snake_case__			,		snake_case__=None			,		snake_case__=None			,		snake_case__=0					)      ->      Optional[int]:



         """simple docstring"""





         UpperCAmelCase						     =      1.0 if scale is None else scale
         UpperCAmelCase						     =      0.0 if loc is None else loc

         super().__init__(lowerCamelCase_			,		[AffineTransform(loc=self.loc			,		scale=self.scale			,		event_dim=lowerCamelCase_					)]					)



  @property
  def 		UpperCamelCase_					(   self					)      ->      Optional[int]:



         """simple docstring"""





         return self.base_dist.mean * self.scale + self.loc



  @property
  def 		UpperCamelCase_					(   self					)      ->      Dict:



         """simple docstring"""





         return self.base_dist.variance * self.scale**2



  @property
  def 		UpperCamelCase_					(   self					)      ->      str:



         """simple docstring"""





         return self.variance.sqrt()





class 	UpperCamelCase_			(							nn.Module	):



  def __init__(   self			,		snake_case__			,		snake_case__			,		snake_case__			,		**snake_case__					)      ->      None:



         """simple docstring"""





         super().__init__(**lowerCamelCase_					)
         UpperCAmelCase						     =      args_dim
         UpperCAmelCase						     =      nn.ModuleList([nn.Linear(lowerCamelCase_			,		lowerCamelCase_					) for dim in args_dim.values()]					)
         UpperCAmelCase						     =      domain_map



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      Tuple[torch.Tensor]:



         """simple docstring"""





         UpperCAmelCase						     =      [proj(lowerCamelCase_					) for proj in self.proj]

         return self.domain_map(*lowerCamelCase_					)





class 	UpperCamelCase_			(							nn.Module	):



  def __init__(   self			,		snake_case__					)      ->      Any:



         """simple docstring"""





         super().__init__()
         UpperCAmelCase						     =      function



  def 		UpperCamelCase_					(   self			,		snake_case__			,		*snake_case__					)      ->      int:



         """simple docstring"""





         return self.function(lowerCamelCase_			,		*lowerCamelCase_					)





class 	UpperCamelCase_			:
  _A							:       type
  _A							:       int
  _A							:       Dict[str, int]



  def __init__(   self			,		snake_case__ = 1					)      ->      None:



         """simple docstring"""





         UpperCAmelCase						     =      dim
         UpperCAmelCase						     =      {k: dim * self.args_dim[k] for k in self.args_dim}



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      Union[str, Any]:



         """simple docstring"""





         if self.dim == 1:
                return self.distribution_class(*lowerCamelCase_					)
         else:
                return Independent(self.distribution_class(*lowerCamelCase_					)			,		1					)



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__ = None			,		snake_case__ = None			,		)      ->      Distribution:



         """simple docstring"""





         UpperCAmelCase						     =      self._base_distribution(lowerCamelCase_					)
         if loc is None and scale is None:
                return distr
         else:
                return AffineTransformed(lowerCamelCase_			,		loc=lowerCamelCase_			,		scale=lowerCamelCase_			,		event_dim=self.event_dim					)



  @property
  def 		UpperCamelCase_					(   self					)      ->      Tuple:



         """simple docstring"""





         return () if self.dim == 1 else (self.dim,)



  @property
  def 		UpperCamelCase_					(   self					)      ->      int:



         """simple docstring"""





         return len(self.event_shape					)



  @property
  def 		UpperCamelCase_					(   self					)      ->      float:



         """simple docstring"""





         return 0.0



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      nn.Module:



         """simple docstring"""





         return ParameterProjection(
             in_features=lowerCamelCase_			,		args_dim=self.args_dim			,		domain_map=LambdaLayer(self.domain_map					)			,		)



  def 		UpperCamelCase_					(   self			,		*snake_case__					)      ->      List[Any]:



         """simple docstring"""





         raise NotImplementedError()



  @staticmethod
  def 		UpperCamelCase_					(   snake_case__					)      ->      torch.Tensor:



         """simple docstring"""





         return (x + torch.sqrt(torch.square(lowerCamelCase_					) + 4.0					)) / 2.0





class 	UpperCamelCase_			(							a__	):
  _A							:       Dict[str, int]					=						{"df": 1, "loc": 1, "scale": 1}
  _A							:       type					=						StudentT



  @classmethod
  def 		UpperCamelCase_					(   cls			,		snake_case__			,		snake_case__			,		snake_case__					)      ->      int:



         """simple docstring"""





         UpperCAmelCase						     =      cls.squareplus(lowerCamelCase_					).clamp_min(torch.finfo(scale.dtype					).eps					)
         UpperCAmelCase						     =      2.0 + cls.squareplus(lowerCamelCase_					)
         return df.squeeze(-1					), loc.squeeze(-1					), scale.squeeze(-1					)





class 	UpperCamelCase_			(							a__	):
  _A							:       Dict[str, int]					=						{"loc": 1, "scale": 1}
  _A							:       type					=						Normal



  @classmethod
  def 		UpperCamelCase_					(   cls			,		snake_case__			,		snake_case__					)      ->      Dict:



         """simple docstring"""





         UpperCAmelCase						     =      cls.squareplus(lowerCamelCase_					).clamp_min(torch.finfo(scale.dtype					).eps					)
         return loc.squeeze(-1					), scale.squeeze(-1					)





class 	UpperCamelCase_			(							a__	):
  _A							:       Dict[str, int]					=						{"total_count": 1, "logits": 1}
  _A							:       type					=						NegativeBinomial



  @classmethod
  def 		UpperCamelCase_					(   cls			,		snake_case__			,		snake_case__					)      ->      int:



         """simple docstring"""





         UpperCAmelCase						     =      cls.squareplus(lowerCamelCase_					)
         return total_count.squeeze(-1					), logits.squeeze(-1					)



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      Distribution:



         """simple docstring"""





         UpperCAmelCase     ,							UpperCAmelCase						     =      distr_args
         if self.dim == 1:
                return self.distribution_class(total_count=lowerCamelCase_			,		logits=lowerCamelCase_					)
         else:
                return Independent(self.distribution_class(total_count=lowerCamelCase_			,		logits=lowerCamelCase_					)			,		1					)



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__ = None			,		snake_case__ = None					)      ->      Distribution:



         """simple docstring"""





         UpperCAmelCase     ,							UpperCAmelCase						     =      distr_args

         if scale is not None:
                # See scaling property of Gamma.
                logits += scale.log()

         return self._base_distribution((total_count, logits)					)




'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       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(A			):
																					lowerCAmelCase__			  =      state_dict[key]

    # extract state_dict for UNetLDM
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''model.diffusion_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							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=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


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

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