



import importlib

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



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase=False):
					UpperCamelCase_						     =      OmegaConf.load(__UpperCAmelCase)
					if display:
										print(yaml.dump(OmegaConf.to_container(__UpperCAmelCase)))
					return config



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



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase):
					UpperCamelCase_						     =      model.encode(__UpperCAmelCase)
					print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}""")
					UpperCamelCase_						     =      model.decode(__UpperCAmelCase)
					return xrec



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase=False):
					UpperCamelCase_						     =      string.rsplit("."      ,		1)
					if reload:
										UpperCamelCase_						     =      importlib.import_module(__UpperCAmelCase)
										importlib.reload(__UpperCAmelCase)
					return getattr(importlib.import_module(__UpperCAmelCase      ,		package=__UpperCAmelCase)      ,		cls)



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






def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase):
					# load the specified checkpoint
					if ckpt:
										UpperCamelCase_						     =      torch.load(__UpperCAmelCase      ,		map_location="cpu")
										UpperCamelCase_						     =      pl_sd['''global_step''']
										print(f"""loaded model from global step {global_step}.""")
					else:
										UpperCamelCase_						     =      {'''state_dict''': None}
										UpperCamelCase_						     =      None
					UpperCamelCase_						     =      load_model_from_config(config.model      ,		pl_sd["state_dict"]      ,		gpu=__UpperCAmelCase      ,		eval_mode=__UpperCAmelCase)['''model''']
					return model, global_step

"""simple docstring"""






from __future__ import annotations

from math import pi, sqrt



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  tuple:

   if inductance <= 0:
      raise ValueError('''Inductance cannot be 0 or negative''' )

   elif capacitance <= 0:
      raise ValueError('''Capacitance cannot be 0 or negative''' )

   else:
      return (
          "Resonant frequency",
          float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
