

'''simple docstring'''






import doctest
from collections import deque

import numpy as np






class      lowercase_      :




  """simple docstring"""




  def __init__( self  :	Optional[Any]     ):
   __lowercase				    =					[2, 1, 2, -1]
   __lowercase				    =					[1, 2, 3, 4]






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					len(self.first_signal     )
   __lowercase				    =					len(self.second_signal     )

   __lowercase				    =					max(lowercase__   ,lowercase__     )

   # create a zero matrix of max_length x max_length
   __lowercase				    =					[[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__     ):
    __lowercase				    =					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
   __lowercase				    =					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()






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

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

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

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

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

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

			A__	  =     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			,						)

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


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

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




