



import importlib

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       Tuple=False	)->  List[str]:
    A__		 =					OmegaConf.load(__snake_case	)
    if display:
        print(yaml.dump(OmegaConf.to_container(__snake_case	)	)	)
    return config



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       Dict=None ,       UpperCamelCase_				:       Tuple=None	)->  Optional[int]:


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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int ,       UpperCamelCase_				:       Optional[Any]	)->  List[str]:
    A__		 =					model.encode(__snake_case	)
    print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"	)
    A__		 =					model.decode(__snake_case	)
    return xrec



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int ,       UpperCamelCase_				:       Dict=False	)->  Tuple:
    A__		 =					string.rsplit('''.''' ,       1	)
    if reload:
        A__		 =					importlib.import_module(__snake_case	)
        importlib.reload(__snake_case	)
    return getattr(importlib.import_module(__snake_case ,       package=__snake_case	) ,       cls	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any]	)->  Tuple:


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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Tuple ,       UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       Optional[Any]=True ,       UpperCamelCase_				:       Any=True	)->  int:
    A__		 =					instantiate_from_config(__snake_case	)
    if sd is not None:
        model.load_state_dict(__snake_case	)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       Tuple ,       UpperCamelCase_				:       Union[str, Any] ,       UpperCamelCase_				:       Optional[Any]	)->  Tuple:


    if ckpt:
        A__		 =					torch.load(__snake_case ,       map_location='''cpu'''	)
        A__		 =					pl_sd["global_step"]
        print(f"loaded model from global step {global_step}."	)
    else:
        A__		 =					{"state_dict": None}
        A__		 =					None
    A__		 =					load_model_from_config(config.model ,       pl_sd['''state_dict'''] ,       gpu=__snake_case ,       eval_mode=__snake_case	)["model"]
    return model, global_step





import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier



def 		lowerCAmelCase__    (					UpperCamelCase_				:       dict	)->  tuple:
    return (data["data"], data["target"])



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray ,       UpperCamelCase_				:       np.ndarray	)->  XGBClassifier:
    A__		 =					XGBClassifier()
    classifier.fit(UpperCamelCase_ ,       UpperCamelCase_	)
    return classifier



def 		lowerCAmelCase__    (					)->  None:
    A__		 =					load_iris()
    A__ ,	A__		 =					data_handling(UpperCamelCase_	)
    A__ ,	A__ ,	A__ ,	A__		 =					train_test_split(
        UpperCamelCase_ ,       UpperCamelCase_ ,       test_size=0.25	)

    A__		 =					iris['''target_names''']

    # Create an XGBoost Classifier from the training data
    A__		 =					xgboost(UpperCamelCase_ ,       UpperCamelCase_	)

    # Display the confusion matrix of the classifier with both training and test sets
    ConfusionMatrixDisplay.from_estimator(
        UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       display_labels=UpperCamelCase_ ,       cmap='''Blues''' ,       normalize='''true''' ,       )
    plt.title('''Normalized Confusion Matrix - IRIS Dataset'''	)
    plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()
