import importlib

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






def  snake_case  (       snake_case__			:Tuple   ,	snake_case__			:str=False)						->   Union[str, Any]:
       _A        =  OmegaConf.load(snake_case__)
       if display:
              print(yaml.dump(OmegaConf.to_container(snake_case__)))
       return config






def  snake_case  (       snake_case__			:Optional[Any]   ,	snake_case__			:List[str]=None   ,	snake_case__			:Any=None)						->   Optional[Any]:



       if conf_path is None:
              _A        =  """./model_checkpoints/vqgan_only.yaml"""
       _A        =  load_config(snake_case__   ,	display=snake_case__)
       _A        =  VQModel(**config.model.params)
       if ckpt_path is None:
              _A        =  """./model_checkpoints/vqgan_only.pt"""
       _A        =  torch.load(snake_case__   ,	map_location=snake_case__)
       if ".ckpt" in ckpt_path:
              _A        =  sd["""state_dict"""]
       model.load_state_dict(snake_case__   ,	strict=snake_case__)
       model.to(snake_case__)
       del sd
       return model






def  snake_case  (       snake_case__			:Any   ,	snake_case__			:Tuple)						->   int:
       _A   ,  _A   ,  _A        =  model.encode(snake_case__)
       print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
       _A        =  model.decode(snake_case__)
       return xrec






def  snake_case  (       snake_case__			:Any   ,	snake_case__			:Tuple=False)						->   str:
       _A   ,  _A        =  string.rsplit("""."""   ,	1)
       if reload:
              _A        =  importlib.import_module(snake_case__)
              importlib.reload(snake_case__)
       return getattr(importlib.import_module(snake_case__   ,	package=snake_case__)   ,	cls)






def  snake_case  (       snake_case__			:Optional[int])						->   Union[str, Any]:



       if "target" not in config:
              raise KeyError("""Expected key `target` to instantiate.""")
       return get_obj_from_str(config["""target"""])(**config.get("""params"""   ,	{}))






def  snake_case  (       snake_case__			:Tuple   ,	snake_case__			:int   ,	snake_case__			:Union[str, Any]=True   ,	snake_case__			:List[Any]=True)						->   Union[str, Any]:
       _A        =  instantiate_from_config(snake_case__)
       if sd is not None:
              model.load_state_dict(snake_case__)
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}







def  snake_case  (       snake_case__			:Optional[Any]   ,	snake_case__			:Tuple   ,	snake_case__			:Optional[Any]   ,	snake_case__			:Optional[int])						->   List[str]:



       if ckpt:
              _A        =  torch.load(snake_case__   ,	map_location="""cpu""")
              _A        =  pl_sd["""global_step"""]
              print(F'''loaded model from global step {global_step}.''')
       else:
              _A        =  {"""state_dict""": None}
              _A        =  None
       _A        =  load_model_from_config(config.model   ,	pl_sd["""state_dict"""]   ,	gpu=snake_case__   ,	eval_mode=snake_case__)["""model"""]
       return model, global_step





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a  :     str       =  logging.get_logger(__name__)

__a  :     Optional[int]       =  {
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Tuple			 =						'''vivit'''





			def __init__(   self      ,   lowerCAmelCase__=2_24      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=[2, 16, 16]      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu_fast"      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-06      ,   lowerCAmelCase__=True      ,   **lowerCAmelCase__      ,   )     ->					int:

							'''simple docstring'''


							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps

							__lowercase 			=							image_size
							__lowercase 			=							num_frames
							__lowercase 			=							tubelet_size
							__lowercase 			=							num_channels
							__lowercase 			=							qkv_bias

							super().__init__(**lowerCAmelCase__						)