





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


A_												=     logging.get_logger(__name__)

A_												=     {
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "swinv2"

					A__				 =    {
					    "num_attention_heads": "num_heads",
					    "num_hidden_layers": "num_layers",
					}





					def __init__( self	,  _lowerCAmelCase=224	,  _lowerCAmelCase=4	,  _lowerCAmelCase=3	,  _lowerCAmelCase=96	,  _lowerCAmelCase=[2, 2, 6, 2]	,  _lowerCAmelCase=[3, 6, 12, 24]	,  _lowerCAmelCase=7	,  _lowerCAmelCase=4.0	,  _lowerCAmelCase=True	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase="gelu"	,  _lowerCAmelCase=False	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=1E-5	,  _lowerCAmelCase=32	,  **_lowerCAmelCase	,  ):
										super().__init__(**_lowerCAmelCase	)

										lowerCamelCase__       =       image_size
										lowerCamelCase__       =       patch_size
										lowerCamelCase__       =       num_channels
										lowerCamelCase__       =       embed_dim
										lowerCamelCase__       =       depths
										lowerCamelCase__       =       len(_lowerCAmelCase	)
										lowerCamelCase__       =       num_heads
										lowerCamelCase__       =       window_size
										lowerCamelCase__       =       mlp_ratio
										lowerCamelCase__       =       qkv_bias
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       drop_path_rate
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       use_absolute_embeddings
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       encoder_stride
										# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										lowerCamelCase__       =       int(embed_dim * 2 ** (len(_lowerCAmelCase	) - 1)	)
										lowerCamelCase__       =       (0, 0, 0, 0)








import importlib

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





def    __UpperCamelCase      (    a,     a=False) ->Optional[Any]:
					lowerCamelCase__       =       OmegaConf.load(a)
					if display:
										print(yaml.dump(OmegaConf.to_container(a)))
					return config





def    __UpperCamelCase      (    a,     a=None,     a=None) ->List[Any]:
					if conf_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.yaml"
					lowerCamelCase__       =       load_config(a,     display=a)
					lowerCamelCase__       =       VQModel(**config.model.params)
					if ckpt_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.pt"
					lowerCamelCase__       =       torch.load(a,     map_location=a)
					if ".ckpt" in ckpt_path:
										lowerCamelCase__       =       sd["state_dict"]
					model.load_state_dict(a,     strict=a)
					model.to(a)
					del sd
					return model





def    __UpperCamelCase      (    a,     a) ->int:
					lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       model.encode(a)
					print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
					lowerCamelCase__       =       model.decode(a)
					return xrec





def    __UpperCamelCase      (    a,     a=False) ->Dict:
					lowerCamelCase__    ,		lowerCamelCase__       =       string.rsplit(".",     1)
					if reload:
										lowerCamelCase__       =       importlib.import_module(a)
										importlib.reload(a)
					return getattr(importlib.import_module(a,     package=a),     cls)





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





def    __UpperCamelCase      (    a,     a,     a=True,     a=True) ->Optional[Any]:
					lowerCamelCase__       =       instantiate_from_config(a)
					if sd is not None:
										model.load_state_dict(a)
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def    __UpperCamelCase      (    a,     a,     a,     a) ->Dict:
					# load the specified checkpoint
					if ckpt:
										lowerCamelCase__       =       torch.load(a,     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=a,     eval_mode=a)["model"]
					return model, global_step

