
"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def      lowercase__    (       lowerCAmelCase					:       Optional[Any]			,					lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       List[str]			)			->   Dict:






    """simple docstring"""




    UpperCAmelCase          =       OmegaConf.load(lowerCAmelCase			)
    UpperCAmelCase          =       torch.load(lowerCAmelCase			,					map_location='cpu'			)['model']
    UpperCAmelCase          =       list(state_dict.keys()			)

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

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

    UpperCAmelCase          =       config.model.params.first_stage_config.params
    UpperCAmelCase          =       config.model.params.unet_config.params

    UpperCAmelCase          =       VQModel(**lowerCAmelCase			).eval()
    vqvae.load_state_dict(lowerCAmelCase			)

    UpperCAmelCase          =       UNetLDMModel(**lowerCAmelCase			).eval()
    unet.load_state_dict(lowerCAmelCase			)

    UpperCAmelCase          =       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			,					)

    UpperCAmelCase          =       LDMPipeline(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)
    pipeline.save_pretrained(lowerCAmelCase			)


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)





"""simple docstring"""

import math




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Optional[Any]			)			->   List[Any]:






    """simple docstring"""




    if 0 not in (x, y):
        # We use the relation x^y = y*log10(x), where 10 is the base.
        return y * math.logaa(lowerCAmelCase			)
    else:
        if x == 0:  # 0 raised to any number is 0
            return 0
        elif y == 0:
            return 1  # any number raised to 0 is 1
    raise AssertionError('This should never happen'			)


if __name__ == "__main__":  # Main function
   # Read two numbers from input and typecast them to int using map function.
   # Here x is the base and y is the power.
   SCREAMING_SNAKE_CASE_            =    '''Enter the base and the power separated by a comma: '''
   SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    map(int, input(prompt).split(''','''))
   SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    map(int, input(prompt).split(''','''))

   # We find the log of each number, using the function res(), which takes two
   # arguments.
   SCREAMING_SNAKE_CASE_            =    res(xa, ya)
   SCREAMING_SNAKE_CASE_            =    res(xa, ya)

   # We check for the largest number
   if resa > resa:
      print('''Largest number is''', xa, '''^''', ya)
   elif resa > resa:
      print('''Largest number is''', xa, '''^''', ya)
   else:
      print('''Both are equal''')



