




import importlib

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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=False )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							OmegaConf.load(lowerCAmelCase )
		if display:
				print(yaml.dump(OmegaConf.to_container(lowerCAmelCase ) ) )
		return config


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=None    ,						lowerCAmelCase=None )->	List[Any]:
		if conf_path is None:
				SCREAMING_SNAKE_CASE_					      =							'./model_checkpoints/vqgan_only.yaml'
		SCREAMING_SNAKE_CASE_					      =							load_config(lowerCAmelCase    ,						display=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							VQModel(**config.model.params )
		if ckpt_path is None:
				SCREAMING_SNAKE_CASE_					      =							'./model_checkpoints/vqgan_only.pt'
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location=lowerCAmelCase )
		if ".ckpt" in ckpt_path:
				SCREAMING_SNAKE_CASE_					      =							sd['state_dict']
		model.load_state_dict(lowerCAmelCase    ,						strict=lowerCAmelCase )
		model.to(lowerCAmelCase )
		del sd
		return model


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							model.encode(lowerCAmelCase )
		print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''' )
		SCREAMING_SNAKE_CASE_					      =							model.decode(lowerCAmelCase )
		return xrec


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=False )->	List[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							string.rsplit('.'    ,						1 )
		if reload:
				SCREAMING_SNAKE_CASE_					      =							importlib.import_module(lowerCAmelCase )
				importlib.reload(lowerCAmelCase )
		return getattr(importlib.import_module(lowerCAmelCase    ,						package=lowerCAmelCase )    ,						cls )


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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=True    ,						lowerCAmelCase=True )->	Optional[int]:
		SCREAMING_SNAKE_CASE_					      =							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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	List[str]:
		# load the specified checkpoint
		if ckpt:
				SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
				SCREAMING_SNAKE_CASE_					      =							pl_sd['global_step']
				print(F'''loaded model from global step {global_step}.''' )
		else:
				SCREAMING_SNAKE_CASE_					      =							{'state_dict': None}
				SCREAMING_SNAKE_CASE_					      =							None
		SCREAMING_SNAKE_CASE_					      =							load_model_from_config(config.model    ,						pl_sd['state_dict']    ,						gpu=lowerCAmelCase    ,						eval_mode=lowerCAmelCase )['model']
		return model, global_step





import functools


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# Validation
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ) or not all(isinstance(lowerCAmelCase    ,						lowerCAmelCase ) for day in days ):
				raise ValueError('The parameter days should be a list of integers' )

		if len(lowerCAmelCase ) != 3 or not all(isinstance(lowerCAmelCase    ,						lowerCAmelCase ) for cost in costs ):
				raise ValueError('The parameter costs should be a list of three integers' )

		if len(lowerCAmelCase ) == 0:
				return 0

		if min(lowerCAmelCase ) <= 0:
				raise ValueError('All days elements should be greater than 0' )

		if max(lowerCAmelCase ) >= 366:
				raise ValueError('All days elements should be less than 366' )

		SCREAMING_SNAKE_CASE_					      =							set(lowerCAmelCase )

		@functools.cache
		def dynamic_programming(lowerCAmelCase ) -> int:
				if index > 365:
						return 0

				if index not in days_set:
						return dynamic_programming(index + 1 )

				return min(
				    costs[0] + dynamic_programming(index + 1 )    ,						costs[1] + dynamic_programming(index + 7 )    ,						costs[2] + dynamic_programming(index + 30 )    ,						)

		return dynamic_programming(1 )


if __name__ == "__main__":
						import doctest

						doctest.testmod()