



'''simple docstring'''




import importlib

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


def 				lowerCAmelCase_   (	_lowerCamelCase:   str       ,      _lowerCamelCase:   Tuple=False					):
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		OmegaConf.load(_lowerCamelCase					)
		if display:
				print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
		return config


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


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]       ,      _lowerCamelCase:   Tuple					):
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		model.encode(_lowerCamelCase					)
		print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"					)
		__SCREAMING_SNAKE_CASE       :		str           =		model.decode(_lowerCamelCase					)
		return xrec


def 				lowerCAmelCase_   (	_lowerCamelCase:   Union[str, Any]       ,      _lowerCamelCase:   Any=False					):
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Any           =		string.rsplit("""."""       ,      1					)
		if reload:
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		importlib.import_module(_lowerCamelCase					)
				importlib.reload(_lowerCamelCase					)
		return getattr(importlib.import_module(_lowerCamelCase       ,      package=_lowerCamelCase					)       ,      cls					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		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:   List[str]       ,      _lowerCamelCase:   Any       ,      _lowerCamelCase:   str=True       ,      _lowerCamelCase:   Union[str, Any]=True					):
		__SCREAMING_SNAKE_CASE       :		Optional[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 				lowerCAmelCase_   (	_lowerCamelCase:   Any       ,      _lowerCamelCase:   int       ,      _lowerCamelCase:   List[Any]       ,      _lowerCamelCase:   Optional[int]					):
		# load the specified checkpoint
		if ckpt:
				__SCREAMING_SNAKE_CASE       :		int           =		torch.load(_lowerCamelCase       ,      map_location="""cpu"""					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		pl_sd["""global_step"""]
				print(F"loaded model from global step {global_step}."					)
		else:
				__SCREAMING_SNAKE_CASE       :		str           =		{"""state_dict""": None}
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		None
		__SCREAMING_SNAKE_CASE       :		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'''




UpperCamelCase__       :	Optional[Any]    = [
    (10_00, '''M'''),
    (9_00, '''CM'''),
    (5_00, '''D'''),
    (4_00, '''CD'''),
    (1_00, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
		__SCREAMING_SNAKE_CASE       :		Tuple           =		0
		__SCREAMING_SNAKE_CASE       :		str           =		0
		while place < len(_lowerCamelCase					):
				if (place + 1 < len(_lowerCamelCase					)) and (vals[roman[place]] < vals[roman[place + 1]]):
						total += vals[roman[place + 1]] - vals[roman[place]]
						place += 2
				else:
						total += vals[roman[place]]
						place += 1
		return total


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		Any           =		[]
		for arabic, roman in ROMAN:
				((__SCREAMING_SNAKE_CASE)       ,						(__SCREAMING_SNAKE_CASE))       :		str           =		divmod(_lowerCamelCase       ,      _lowerCamelCase					)
				result.append(roman * factor					)
				if number == 0:
						break
		return "".join(_lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()