
import importlib

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



def 						lowercase_      (       A__			,		A__=False		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= OmegaConf.load(A__		)
    if display:
        print(yaml.dump(OmegaConf.to_container(A__		)		)		)
    return config



def 						lowercase_      (       A__			,		A__=None			,		A__=None		)							->							str:
    """simple docstring"""

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



def 						lowercase_      (       A__			,		A__		)							->							Any:
    """simple docstring"""

    snake_case					,							snake_case					,							snake_case								= model.encode(A__		)
    print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'		)
    snake_case								= model.decode(A__		)
    return xrec



def 						lowercase_      (       A__			,		A__=False		)							->							List[str]:
    """simple docstring"""

    snake_case					,							snake_case								= string.rsplit("."			,		1		)
    if reload:
        snake_case								= importlib.import_module(A__		)
        importlib.reload(A__		)
    return getattr(importlib.import_module(A__			,		package=A__		)			,		cls		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """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 						lowercase_      (       A__			,		A__			,		A__=True			,		A__=True		)							->							Dict:
    """simple docstring"""

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





def 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							str:
    """simple docstring"""

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



from __future__ import annotations



def 						lowercase_      (       A__			,		A__			,		A__		)							->							int | float:
    """simple docstring"""

    if len(A__		) == 0:
        raise ValueError("find_max() arg is an empty sequence"		)
    if (
        left >= len(A__		)
        or left < -len(A__		)
        or right >= len(A__		)
        or right < -len(A__		)
    ):
        raise IndexError("list index out of range"		)
    if left == right:
        return nums[left]
    snake_case								= (left + right) >> 1  # the middle
    snake_case								= find_max(A__			,		A__			,		A__		)  # find max in range[left, mid]
    snake_case								= find_max(A__			,		mid + 1			,		A__		)  # find max in range[mid + 1, right]

    return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

