





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel




def  __UpperCAmelCase(   lowercase_			,     lowercase_=False   ):
       _lowerCamelCase				:  Union[str, Any]						=		OmegaConf.load(lowercase_   )
       if display:
              print(yaml.dump(OmegaConf.to_container(lowercase_   )   )   )
       return config




def  __UpperCAmelCase(   lowercase_			,     lowercase_=None			,     lowercase_=None   ):
       if conf_path is None:
              _lowerCamelCase				:  str						=		'''./model_checkpoints/vqgan_only.yaml'''
       _lowerCamelCase				:  Optional[int]						=		load_config(lowercase_			,     display=lowercase_   )
       _lowerCamelCase				:  Tuple						=		VQModel(**config.model.params   )
       if ckpt_path is None:
              _lowerCamelCase				:  List[Any]						=		'''./model_checkpoints/vqgan_only.pt'''
       _lowerCamelCase				:  List[Any]						=		torch.load(lowercase_			,     map_location=lowercase_   )
       if ".ckpt" in ckpt_path:
              _lowerCamelCase				:  List[str]						=		sd['''state_dict''']
       model.load_state_dict(lowercase_			,     strict=lowercase_   )
       model.to(lowercase_   )
       del sd
       return model




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase				:  List[Any]						=		model.encode(lowercase_   )
       print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
       _lowerCamelCase				:  Dict						=		model.decode(lowercase_   )
       return xrec




def  __UpperCAmelCase(   lowercase_			,     lowercase_=False   ):
       _lowerCamelCase,	_lowerCamelCase				:  Optional[Any]						=		string.rsplit('''.'''			,     1   )
       if reload:
              _lowerCamelCase				:  Optional[int]						=		importlib.import_module(lowercase_   )
              importlib.reload(lowercase_   )
       return getattr(importlib.import_module(lowercase_			,     package=lowercase_   )			,     cls   )




def  __UpperCAmelCase(   lowercase_   ):
       if "target" not in config:
              raise KeyError('''Expected key `target` to instantiate.'''   )
       return get_obj_from_str(config['''target''']   )(**config.get('''params'''			,     {}   )   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_=True			,     lowercase_=True   ):
       _lowerCamelCase				:  int						=		instantiate_from_config(lowercase_   )
       if sd is not None:
              model.load_state_dict(lowercase_   )
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   ):
       # load the specified checkpoint
       if ckpt:
              _lowerCamelCase				:  List[str]						=		torch.load(lowercase_			,     map_location='''cpu'''   )
              _lowerCamelCase				:  Dict						=		pl_sd['''global_step''']
              print(F"""loaded model from global step {global_step}."""   )
       else:
              _lowerCamelCase				:  Optional[int]						=		{'''state_dict''': None}
              _lowerCamelCase				:  Optional[int]						=		None
       _lowerCamelCase				:  str						=		load_model_from_config(config.model			,     pl_sd['''state_dict''']			,     gpu=lowercase_			,     eval_mode=lowercase_   )['''model''']
       return model, global_step








import doctest
from collections import deque

import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		[2, 1, 2, -1]
             _lowerCamelCase				:  Optional[int]						=		[1, 2, 3, 4]



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		len(self.first_signal)
             _lowerCamelCase				:  str						=		len(self.second_signal)

             _lowerCamelCase				:  str						=		max(a__   ,				a__)

             # create a zero matrix of max_length x max_length
             _lowerCamelCase				:  Optional[Any]						=		[[0] * max_length for i in range(a__)]

             # 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(a__):
                    _lowerCamelCase				:  Optional[Any]						=		deque(self.second_signal)
                    rotated_signal.rotate(a__)
                    for j, item in enumerate(a__):
                           matrix[i][j] += item

        # multiply the matrix with the first signal
             _lowerCamelCase				:  List[Any]						=		np.matmul(np.transpose(a__)   ,				np.transpose(self.first_signal))

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


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

