






import importlib

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					List[Any]=False		)  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						OmegaConf.load(UpperCamelCase_		)
						if display:
												print(yaml.dump(OmegaConf.to_container(UpperCamelCase_		)		)		)
						return config







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Optional[int]=None    ,       UpperCamelCase_       :					Union[str, Any]=None		)  ->   Optional[Any]:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					List[Any]		)  ->   Union[str, Any]:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					List[Any]=False		)  ->   Optional[int]:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]		)  ->   int:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					Optional[int]=True    ,       UpperCamelCase_       :					Union[str, Any]=True		)  ->   Dict:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Any		)  ->   int:

						"""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













from collections import deque

from .hash_table import HashTable


class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def __init__(	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												super().__init__(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						deque([]   ) if self.values[key] is None else self.values[key]
												self.values[key].appendleft(_UpperCamelCase   )
												lowerCAmelCase__    						=						self.values[key]






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return (
												    sum(self.charge_factor - len(_UpperCamelCase   ) for slot in self.values   )
												    / self.size_table
												    * self.charge_factor
												)







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if not (
												    len(self.values[key]   ) == self.charge_factor and self.values.count(_UpperCamelCase   ) == 0
												):
																		return key
												return super()._collision_resolution(_UpperCamelCase     ,						_UpperCamelCase   )





