


"""simple docstring"""


import importlib

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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=False      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			OmegaConf.load(_UpperCamelCase      )
			if display:
						print(yaml.dump(OmegaConf.to_container(_UpperCamelCase      )      )      )
			return config


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=None    , _UpperCamelCase=None      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=False      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=True    , _UpperCamelCase=True      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if ckpt:
						__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )
						__lowerCAmelCase				    =			pl_sd["global_step"]
						print(f"loaded model from global step {global_step}."      )
			else:
						__lowerCAmelCase				    =			{"state_dict": None}
						__lowerCAmelCase				    =			None
			__lowerCAmelCase				    =			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

import typing
from collections import Counter




def     snake_case_					(     lowerCAmelCase_		)->		typing.Counter[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						typing.Counter[int]			     = Counter()
       for base in range(1       ,   max_perimeter + 1		):
              for perpendicular in range(lowerCAmelCase_       ,   max_perimeter + 1		):
                     _UpperCAmelCase  :						List[str]			     = (base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(lowerCAmelCase_		):
                            _UpperCAmelCase  :						Optional[Any]			     = int(base + perpendicular + hypotenuse		)
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets




def     snake_case_					(     lowerCAmelCase_ = 1000		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = pythagorean_triple(lowerCAmelCase_		)
       return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
   print(f"""Perimeter {solution()} has maximum solutions""")



