


'''simple docstring'''
import importlib

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





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





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





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      int:
    UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						Dict						=							model.encode(lowerCAmelCase__		)
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""		)
    UpperCAmelCase__     :						str						=							model.decode(lowerCAmelCase__		)
    return xrec





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=False		)       ->      Optional[int]:
    UpperCAmelCase__		,		UpperCAmelCase__     :						int						=							string.rsplit('''.'''    ,   1		)
    if reload:
        UpperCAmelCase__     :						Tuple						=							importlib.import_module(lowerCAmelCase__		)
        importlib.reload(lowerCAmelCase__		)
    return getattr(importlib.import_module(lowerCAmelCase__    ,   package=lowerCAmelCase__		)    ,   cls		)





def    a__			(   lowerCAmelCase__		)       ->      List[str]:
    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]:
    UpperCAmelCase__     :						Tuple						=							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__		)       ->      Optional[Any]:
    # load the specified checkpoint
    if ckpt:
        UpperCAmelCase__     :						Tuple						=							torch.load(lowerCAmelCase__    ,   map_location='''cpu'''		)
        UpperCAmelCase__     :						Dict						=							pl_sd['''global_step''']
        print(F"""loaded model from global step {global_step}."""		)
    else:
        UpperCAmelCase__     :						Optional[int]						=							{'''state_dict''': None}
        UpperCAmelCase__     :						str						=							None
    UpperCAmelCase__     :						Tuple						=							load_model_from_config(config.model    ,   pl_sd['''state_dict''']    ,   gpu=lowerCAmelCase__    ,   eval_mode=lowerCAmelCase__		)['''model''']
    return model, global_step




import unittest

from knapsack import greedy_knapsack as kp




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
							__UpperCAmelCase			:							str	    =	[2, 4, 6, 8, 1_0, 1_2]
							__UpperCAmelCase			:							List[Any]	    =	1_0_0
							self.assertEqual(kp.calc_profit(lowercase__				,     lowercase__				,     lowercase__)				,     2_1_0)


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Weight can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Profit can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(
							    lowercase__				,     '''The length of profit and weight must be same.''')


if __name__ == "__main__":
				unittest.main()

