
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     int:
						_lowercase					       =					OmegaConf.load(_lowerCAmelCase       )
						_lowercase					       =					torch.load(_lowerCAmelCase		,   map_location="""cpu"""       )["""model"""]
						_lowercase					       =					list(state_dict.keys()       )

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

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

						_lowercase					       =					config.model.params.first_stage_config.params
						_lowercase					       =					config.model.params.unet_config.params

						_lowercase					       =					VQModel(**_lowerCAmelCase       ).eval()
						vqvae.load_state_dict(_lowerCAmelCase       )

						_lowercase					       =					UNetLDMModel(**_lowerCAmelCase       ).eval()
						unet.load_state_dict(_lowerCAmelCase       )

						_lowercase					       =					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		,   )

						_lowercase					       =					LDMPipeline(_lowerCAmelCase		,   _lowerCAmelCase		,   _lowerCAmelCase       )
						pipeline.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
		A		: str								=     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		: List[str]								=     parser.parse_args()

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




'''simple docstring'''





__a  			=						"Alexander Joslin"

import operator as op

from .stack import Stack





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       str       		= {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

      snake_case__    :       Stack[int]       		= Stack()
      snake_case__    :       Stack[str]       		= Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(_lowerCAmelCase						)						)
            elif i in operators:
                  # RULE 2
                  operator_stack.push(_lowerCAmelCase						)
            elif i == ")":
                  # RULE 4
                  snake_case__    :       Union[str, Any]       		= operator_stack.peek()
                  operator_stack.pop()
                  snake_case__    :       Any       		= operand_stack.peek()
                  operand_stack.pop()
                  snake_case__    :       str       		= operand_stack.peek()
                  operand_stack.pop()

                  snake_case__    :       Optional[Any]       		= operators[opr](_lowerCAmelCase	,  _lowerCAmelCase						)
                  operand_stack.push(_lowerCAmelCase						)

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
       __a  			=						"(5 + ((4 * 2) * (2 + 3)))"
       # answer = 45
       print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")
