

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__       :   List[str] 			=			logging.get_logger(__name__)

UpperCAmelCase__       :   List[Any] 			=			{
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}







class 				a__				(      A__		,	A__				):







  """simple docstring"""




  UpperCAmelCase__					:       Tuple					="""convnextv2"""


  def __init__(	self			:       Tuple , UpperCAmelCase__			:       Optional[int]=3 , UpperCAmelCase__			:       Any=4 , UpperCAmelCase__			:       int=4 , UpperCAmelCase__			:       Union[str, Any]=None , UpperCAmelCase__			:       List[str]=None , UpperCAmelCase__			:       Optional[Any]="gelu" , UpperCAmelCase__			:       Any=0.02 , UpperCAmelCase__			:       Any=1e-12 , UpperCAmelCase__			:       Tuple=0.0 , UpperCAmelCase__			:       int=2_2_4 , UpperCAmelCase__			:       Any=None , UpperCAmelCase__			:       Optional[int]=None , **UpperCAmelCase__			:       List[str] , )    ->Optional[Any]:
        """simple docstring"""







        super().__init__(**_a						)

        SCREAMING_SNAKE_CASE       :					Optional[int]        =      num_channels
        SCREAMING_SNAKE_CASE       :					Dict        =      patch_size
        SCREAMING_SNAKE_CASE       :					Dict        =      num_stages
        SCREAMING_SNAKE_CASE       :					List[str]        =      [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
        SCREAMING_SNAKE_CASE       :					Dict        =      [3, 3, 9, 3] if depths is None else depths
        SCREAMING_SNAKE_CASE       :					Optional[int]        =      hidden_act
        SCREAMING_SNAKE_CASE       :					List[Any]        =      initializer_range
        SCREAMING_SNAKE_CASE       :					Optional[Any]        =      layer_norm_eps
        SCREAMING_SNAKE_CASE       :					int        =      drop_path_rate
        SCREAMING_SNAKE_CASE       :					Tuple        =      image_size
        SCREAMING_SNAKE_CASE       :					str        =      ["""stem"""] + [f"stage{idx}" for idx in range(1 , len(self.depths						) + 1						)]
        SCREAMING_SNAKE_CASE   ,		SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      get_aligned_output_features_output_indices(
            out_features=_a , out_indices=_a , stage_names=self.stage_names						)







'''simple docstring'''
import importlib

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _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(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """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					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =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        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, 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=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
