import importlib

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




def       lowerCAmelCase_     (		lowercase:							int  ,					lowercase:							Optional[Any]=False		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= OmegaConf.load(_lowerCamelCase		)
 if display:
  print(yaml.dump(OmegaConf.to_container(_lowerCamelCase		)		)		)
 return config




def       lowerCAmelCase_     (		lowercase:							Tuple  ,					lowercase:							Tuple=None  ,					lowercase:							Union[str, Any]=None		)    ->				List[Any]:







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




def       lowerCAmelCase_     (		lowercase:							Optional[Any]  ,					lowercase:							Optional[Any]		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= model.encode(_lowerCamelCase		)
 print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""		)
 _UpperCamelCase:     List[Any]							= model.decode(_lowerCamelCase		)
 return xrec




def       lowerCAmelCase_     (		lowercase:							Optional[Any]  ,					lowercase:							Optional[int]=False		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     str							= string.rsplit('''.'''  ,					1		)
 if reload:
  _UpperCamelCase:     Union[str, Any]							= importlib.import_module(_lowerCamelCase		)
  importlib.reload(_lowerCamelCase		)
 return getattr(importlib.import_module(_lowerCamelCase  ,					package=_lowerCamelCase		)  ,					cls		)




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Any:







 '''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_     (		lowercase:							Any  ,					lowercase:							Union[str, Any]  ,					lowercase:							Any=True  ,					lowercase:							str=True		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     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_     (		lowercase:							Tuple  ,					lowercase:							Optional[int]  ,					lowercase:							str  ,					lowercase:							Optional[Any]		)    ->				Union[str, Any]:







 '''simple docstring'''
 # load the specified checkpoint
 if ckpt:
  _UpperCamelCase:     List[str]							= torch.load(_lowerCamelCase  ,					map_location='''cpu'''		)
  _UpperCamelCase:     str							= pl_sd["global_step"]
  print(F"""loaded model from global step {global_step}."""		)
 else:
  _UpperCamelCase:     int							= {"state_dict": None}
  _UpperCamelCase:     List[str]							= None
 _UpperCamelCase:     Optional[Any]							= load_model_from_config(config.model  ,					pl_sd['''state_dict''']  ,					gpu=_lowerCamelCase  ,					eval_mode=_lowerCamelCase		)["model"]
 return model, global_step





from __future__ import annotations




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  Dict=None							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= data
    _UpperCamelCase:     int							= None



   def __repr__(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     int							= []
    _UpperCamelCase:     str							= self
    while temp:
     string_rep.append(f"""{temp.data}"""							)
     _UpperCamelCase:     Optional[Any]							= temp.next
    return "->".join(_lowercase							)




def       lowerCAmelCase_     (		lowercase:							list		)    ->				Optional[int]:







 '''simple docstring'''
 if not elements_list:
  raise Exception('''The Elements List is empty'''		)

 _UpperCamelCase:     Optional[Any]							= Node(elements_list[0]		)
 for i in range(1  ,					len(lowercase		)		):
  _UpperCamelCase:     Union[str, Any]							= Node(elements_list[i]		)
  _UpperCamelCase:     Optional[int]							= current.next
 return head




def       lowerCAmelCase_     (		lowercase:							Node		)    ->				None:







 '''simple docstring'''
 if head_node is not None and isinstance(lowercase  ,					lowercase		):
  print_reverse(head_node.next		)
  print(head_node.data		)




def       lowerCAmelCase_     (		)    ->				Tuple:







 '''simple docstring'''
 from doctest import testmod

 testmod()

 _UpperCamelCase:     Any							= make_linked_list([14, 52, 14, 12, 43]		)
 print('''Linked List:'''		)
 print(lowercase		)
 print('''Elements in Reverse:'''		)
 print_reverse(lowercase		)


if __name__ == "__main__":
  main()