



'''simple docstring'''





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase=False      ):



    """simple docstring"""





    lowerCAmelCase__			:  Dict											= OmegaConf.load(lowerCamelCase__      )
    if display:
        print(yaml.dump(OmegaConf.to_container(lowerCamelCase__      )      )      )
    return config
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase=None							,   UpperCamelCase=None      ):



    """simple docstring"""





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



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= model.encode(lowerCamelCase__      )
    print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""      )
    lowerCAmelCase__			:  str											= model.decode(lowerCamelCase__      )
    return xrec
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase=False      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= string.rsplit("""."""							,   1      )
    if reload:
        lowerCAmelCase__			:  List[Any]											= importlib.import_module(lowerCamelCase__      )
        importlib.reload(lowerCamelCase__      )
    return getattr(importlib.import_module(lowerCamelCase__							,   package=lowerCamelCase__      )							,   cls      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=True							,   UpperCamelCase=True      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[Any]											= 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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    if ckpt:
        lowerCAmelCase__			:  Optional[int]											= torch.load(lowerCamelCase__							,   map_location="""cpu"""      )
        lowerCAmelCase__			:  Tuple											= 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__			:  Union[str, Any]											= load_model_from_config(config.model							,   pl_sd["""state_dict"""]							,   gpu=lowerCamelCase__							,   eval_mode=lowerCamelCase__      )["""model"""]
    return model, global_step







'''simple docstring'''





import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase = True							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = False							,   UpperCamelCase = 100							,   UpperCamelCase = 0.01							,   UpperCamelCase = 1							,   ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= False
    lowerCAmelCase__			:  Optional[Any]											= search_prob
    lowerCAmelCase__			:  Tuple											= start_temperate
    lowerCAmelCase__			:  Optional[Any]											= []
    lowerCAmelCase__			:  str											= 0
    lowerCAmelCase__			:  Tuple											= None

    while not search_end:
        lowerCAmelCase__			:  List[str]											= current_state.score()
        if best_state is None or current_score > best_state.score():
            lowerCAmelCase__			:  List[str]											= current_state
        scores.append(UpperCamelCase      )
        iterations += 1
        lowerCAmelCase__			:  Dict											= None
        lowerCAmelCase__			:  List[Any]											= current_state.get_neighbors()
        while (
            next_state is None and neighbors
        ):  # till we do not find a neighbor that we can move to
            lowerCAmelCase__			:  Tuple											= random.randint(0							,   len(UpperCamelCase      ) - 1      )  # picking a random neighbor
            lowerCAmelCase__			:  str											= neighbors.pop(UpperCamelCase      )
            lowerCAmelCase__			:  Optional[Any]											= picked_neighbor.score() - current_score

            if (
                picked_neighbor.x > max_x
                or picked_neighbor.x < min_x
                or picked_neighbor.y > max_y
                or picked_neighbor.y < min_y
            ):
                continue  # neighbor outside our bounds

            if not find_max:
                lowerCAmelCase__			:  Union[str, Any]											= change * -1  # in case we are finding minimum
            if change > 0:  # improves the solution
                lowerCAmelCase__			:  Tuple											= picked_neighbor
            else:
                lowerCAmelCase__			:  List[Any]											= (math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    lowerCAmelCase__			:  List[Any]											= picked_neighbor
        lowerCAmelCase__			:  Optional[int]											= current_temp - (current_temp * rate_of_decrease)

        if current_temp < threshold_temp or next_state is None:
            # temperature below threshold, or could not find a suitable neighbor
            lowerCAmelCase__			:  str											= True
        else:
            lowerCAmelCase__			:  Optional[int]											= next_state

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(UpperCamelCase      )							,   UpperCamelCase      )
        plt.xlabel("""Iterations"""      )
        plt.ylabel("""Function values"""      )
        plt.show()
    return best_state


if __name__ == "__main__":
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





     return (x**2) + (y**2)

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(
     prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
     F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(
     prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
     F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





     return (3 * x**2) - (6 * y)

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(prob, find_max=False, visualization=True)
 print(
     '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
     F"""{local_min.score()}"""
 )

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(prob, find_max=True, visualization=True)




 print(
     '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
     F"""{local_min.score()}"""
 )


