





import importlib

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				Tuple			      =	OmegaConf.load(_lowerCAmelCase     )
   if display:
      print(yaml.dump(OmegaConf.to_container(_lowerCAmelCase     )     )     )
   return config



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=None  ,_lowerCAmelCase=None     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_      ,				A_      ,				A_     :				List[Any]			      =	model.encode(_lowerCAmelCase     )
   print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""     )
   A_     :				Union[str, Any]			      =	model.decode(_lowerCAmelCase     )
   return xrec



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_      ,				A_     :				int			      =	string.rsplit("""."""  ,1     )
   if reload:
      A_     :				Dict			      =	importlib.import_module(_lowerCAmelCase     )
      importlib.reload(_lowerCAmelCase     )
   return getattr(importlib.import_module(_lowerCAmelCase  ,package=_lowerCAmelCase     )  ,cls     )



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=True  ,_lowerCAmelCase=True     ):
   '''simple docstring'''


   A_     :				Tuple			      =	instantiate_from_config(_lowerCAmelCase     )
   if sd is not None:
      model.load_state_dict(_lowerCAmelCase     )
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}







def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   if ckpt:
      A_     :				str			      =	torch.load(_lowerCAmelCase  ,map_location="""cpu"""     )
      A_     :				Tuple			      =	pl_sd["""global_step"""]
      print(f"""loaded model from global step {global_step}."""     )
   else:
      A_     :				int			      =	{"""state_dict""": None}
      A_     :				List[str]			      =	None
   A_     :				Tuple			      =	load_model_from_config(config.model  ,pl_sd["""state_dict"""]  ,gpu=_lowerCAmelCase  ,eval_mode=_lowerCAmelCase     )["""model"""]
   return model, global_step













from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,):
   '''simple docstring'''


   A_      ,				A_     :				int			      =	coefficient_matrix.shape
   A_      ,				A_     :				Tuple			      =	constant_matrix.shape

   if rowsa != colsa:
      A_     :				int			      =	f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if colsa != 1:
      A_     :				List[Any]			      =	f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if rowsa != rowsa:
      A_     :				str			      =	(
          """Coefficient and constant matrices dimensions must be nxn and nx1 but """
          f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
      )
      raise ValueError(_lowerCAmelCase     )

   if len(_lowerCAmelCase     ) != rowsa:
      A_     :				Any			      =	(
          """Number of initial values must be equal to number of rows in coefficient """
          f"""matrix but received {len(_lowerCAmelCase     )} and {rowsa}"""
      )
      raise ValueError(_lowerCAmelCase     )

   if iterations <= 0:
      raise ValueError("""Iterations must be at least 1"""     )

   A_     :				NDArray[floataa]			      =	np.concatenate(
       (coefficient_matrix, constant_matrix)  ,axis=1     )

   A_      ,				A_     :				str			      =	table.shape

   strictly_diagonally_dominant(_lowerCAmelCase     )

   # Iterates the whole matrix for given number of times
   for _ in range(_lowerCAmelCase     ):
      A_     :				Union[str, Any]			      =	[]
      for row in range(_lowerCAmelCase     ):
         A_     :				str			      =	0
         for col in range(_lowerCAmelCase     ):
            if col == row:
               A_     :				Optional[Any]			      =	table[row][col]
            elif col == cols - 1:
               A_     :				List[str]			      =	table[row][col]
            else:
               temp += (-1) * table[row][col] * init_val[col]
         A_     :				str			      =	(temp + val) / denom
         new_val.append(_lowerCAmelCase     )
      A_     :				List[str]			      =	new_val

   return [float(_lowerCAmelCase     ) for i in new_val]



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_      ,				A_     :				str			      =	table.shape

   A_     :				Any			      =	True

   for i in range(0  ,_lowerCAmelCase     ):
      A_     :				Optional[Any]			      =	0
      for j in range(0  ,cols - 1     ):
         if i == j:
            continue
         else:
            total += table[i][j]

      if table[i][i] <= total:
         raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""     )

   return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
      import doctest

      doctest.testmod()






