import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Dict	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      OmegaConf.load(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      torch.load(__UpperCamelCase ,     map_location="""cpu"""	)["""model"""]
  SCREAMING_SNAKE_CASE__										=      list(state_dict.keys()	)

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

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

  SCREAMING_SNAKE_CASE__										=      config.model.params.first_stage_config.params
  SCREAMING_SNAKE_CASE__										=      config.model.params.unet_config.params

  SCREAMING_SNAKE_CASE__										=      VQModel(**__UpperCamelCase	).eval()
  vqvae.load_state_dict(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      UNetLDMModel(**__UpperCamelCase	).eval()
  unet.load_state_dict(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      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=__UpperCamelCase ,     )

  SCREAMING_SNAKE_CASE__										=      LDMPipeline(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
  pipeline.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: Optional[Any]							   =		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)
   __lowerCamelCase			: str							   =		parser.parse_args()

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

import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[0] * max_length for i in range(_lowercase      )]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
       self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
       self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

        # multiply the matrix with the first signal
     SCREAMING_SNAKE_CASE__										=      np.matmul(np.transpose(_lowercase      )       ,	np.transpose(self.first_signal      )      )

     # rounding-off to two decimal places
     return [round(_lowercase       ,	2      ) for i in final_signal]


if __name__ == "__main__":
   doctest.testmod()
