




import importlib

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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=False	):




	"""simple docstring"""






	A_  :				int	   =		OmegaConf.load(__UpperCamelCase	)
	if display:
		print(yaml.dump(OmegaConf.to_container(__UpperCamelCase	)	)	)
	return config

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=None  ,      _UpperCAmelCase=None	):




	"""simple docstring"""






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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Union[str, Any]	   =		model.encode(__UpperCamelCase	)
	print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""	)
	A_  :				int	   =		model.decode(__UpperCamelCase	)
	return xrec

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=False	):




	"""simple docstring"""






	A_  :				Tuple	   =		string.rsplit('''.'''  ,      1	)
	if reload:
		A_  :				Tuple	   =		importlib.import_module(__UpperCamelCase	)
		importlib.reload(__UpperCamelCase	)
	return getattr(importlib.import_module(__UpperCamelCase  ,      package=__UpperCamelCase	)  ,      cls	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=True  ,      _UpperCAmelCase=True	):




	"""simple docstring"""






	A_  :				int	   =		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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	if ckpt:
		A_  :				int	   =		torch.load(__UpperCamelCase  ,      map_location='''cpu'''	)
		A_  :				List[str]	   =		pl_sd["""global_step"""]
		print(f"""loaded model from global step {global_step}."""	)
	else:
		A_  :				int	   =		{"""state_dict""": None}
		A_  :				Any	   =		None
	A_  :				Dict	   =		load_model_from_config(config.model  ,      pl_sd['''state_dict''']  ,      gpu=__UpperCamelCase  ,      eval_mode=__UpperCamelCase	)["""model"""]
	return model, global_step











"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase  :			Optional[Any]        =       {
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			int        =       ['Mask2FormerImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			Tuple        =       [
		    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Mask2FormerForUniversalSegmentation',
		    'Mask2FormerModel',
		    'Mask2FormerPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_maskaformer import MaskaFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_maskaformer import (
				    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaskaFormerForUniversalSegmentation,
				    MaskaFormerModel,
				    MaskaFormerPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase  :			Optional[Any]        =       _LazyModule(__name__, globals()['__file__'], _import_structure)





