import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def  snake_case						(__lowercase ,      __lowercase=False						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   OmegaConf.load(__lowercase						)
						if display:
												print(yaml.dump(OmegaConf.to_container(__lowercase						)						)						)
						return config
def  snake_case						(__lowercase ,      __lowercase=None ,      __lowercase=None						)	->	Tuple:

						'''simple docstring'''



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

						'''simple docstring'''



						_snake_case    ,_snake_case    ,_snake_case						:					Any    =   model.encode(__lowercase						)
						print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""						)
						_snake_case						:					str    =   model.decode(__lowercase						)
						return xrec
def  snake_case						(__lowercase ,      __lowercase=False						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case    ,_snake_case						:					Dict    =   string.rsplit("." ,      1						)
						if reload:
												_snake_case						:					Optional[int]    =   importlib.import_module(__lowercase						)
												importlib.reload(__lowercase						)
						return getattr(importlib.import_module(__lowercase ,      package=__lowercase						) ,      cls						)
def  snake_case						(__lowercase						)	->	Dict:

						'''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  snake_case						(__lowercase ,      __lowercase ,      __lowercase=True ,      __lowercase=True						)	->	Optional[int]:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   instantiate_from_config(__lowercase						)
						if sd is not None:
												model.load_state_dict(__lowercase						)
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}

def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



						if ckpt:
												_snake_case						:					str    =   torch.load(__lowercase ,      map_location="cpu"						)
												_snake_case						:					int    =   pl_sd["global_step"]
												print(F"""loaded model from global step {global_step}."""						)
						else:
												_snake_case						:					Optional[int]    =   {"state_dict": None}
												_snake_case						:					List[Any]    =   None
						_snake_case						:					List[str]    =   load_model_from_config(config.model ,      pl_sd["state_dict"] ,      gpu=__lowercase ,      eval_mode=__lowercase						)["model"]
						return model, global_step
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'M-CLIP'



							def __init__(						self				,    lowercase_=1_024				,    lowercase_=768				,    **lowercase_    ):
													_snake_case						:					str    =   transformerDimSize
													_snake_case						:					Union[str, Any]    =   imageDimSize
													super().__init__(**lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		MCLIPConfig



							def __init__(						self				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    *lowercase_				,    **lowercase_    )
													_snake_case						:					List[Any]    =   XLMRobertaModel(lowercase_    )
													_snake_case						:					int    =   torch.nn.Linear(
													    in_features=config.transformerDimensions				,    out_features=config.numDims    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.transformer(input_ids=lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Tuple    =   (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
													return self.LinearTransformation(lowercase_    ), embs