






"""simple docstring"""






import importlib

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



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__=False					):
				A__							=							OmegaConf.load(SCREAMING_SNAKE_CASE__					)
				if display:
								print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE__					)					)					)
				return config



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__=None							,lowerCAmelCase__=None					):


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



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__					):
				A__							=							model.encode(SCREAMING_SNAKE_CASE__					)
				print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
				A__							=							model.decode(SCREAMING_SNAKE_CASE__					)
				return xrec



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__=False					):
				A__							=							string.rsplit('.'							,1					)
				if reload:
								A__							=							importlib.import_module(SCREAMING_SNAKE_CASE__					)
								importlib.reload(SCREAMING_SNAKE_CASE__					)
				return getattr(importlib.import_module(SCREAMING_SNAKE_CASE__							,package=SCREAMING_SNAKE_CASE__					)							,cls					)



def 			__lowerCamelCase       (       lowerCAmelCase__					):


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



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=True							,lowerCAmelCase__=True					):
				A__							=							instantiate_from_config(SCREAMING_SNAKE_CASE__					)
				if sd is not None:
								model.load_state_dict(SCREAMING_SNAKE_CASE__					)
				if gpu:
								model.cuda()
				if eval_mode:
								model.eval()
				return {"model": model}







def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__					):


				if ckpt:
								A__							=							torch.load(SCREAMING_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=SCREAMING_SNAKE_CASE__							,eval_mode=SCREAMING_SNAKE_CASE__					)['''model''']
				return model, global_step




'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	*UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''  ,	UpperCamelCase__  ,	)
				super().__init__(*UpperCamelCase__  ,	**UpperCamelCase__      )


