






import importlib

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


def   lowerCamelCase__			(     lowercase						,     lowercase=False				):

		"""simple docstring"""






		SCREAMING_SNAKE_CASE       :      Dict							=    OmegaConf.load(_lowerCamelCase				)
		if display:
				print(yaml.dump(OmegaConf.to_container(_lowerCamelCase				)				)				)
		return config


def   lowerCamelCase__			(     lowercase						,     lowercase=None						,     lowercase=None				):

		"""simple docstring"""






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


def   lowerCamelCase__			(     lowercase						,     lowercase				):

		"""simple docstring"""






		SCREAMING_SNAKE_CASE       :      Tuple							=    model.encode(_lowerCamelCase				)
		print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''				)
		SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model.decode(_lowerCamelCase				)
		return xrec


def   lowerCamelCase__			(     lowercase						,     lowercase=False				):

		"""simple docstring"""






		SCREAMING_SNAKE_CASE       :      Optional[Any]							=    string.rsplit("."						,     1				)
		if reload:
				SCREAMING_SNAKE_CASE       :      Optional[Any]							=    importlib.import_module(_lowerCamelCase				)
				importlib.reload(_lowerCamelCase				)
		return getattr(importlib.import_module(_lowerCamelCase						,     package=_lowerCamelCase				)						,     cls				)


def   lowerCamelCase__			(     lowercase				):

		"""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   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=True						,     lowercase=True				):

		"""simple docstring"""






		SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    instantiate_from_config(_lowerCamelCase				)
		if sd is not None:
				model.load_state_dict(_lowerCamelCase				)
		if gpu:
				model.cuda()
		if eval_mode:
				model.eval()
		return {"model": model}


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase				):

		"""simple docstring"""






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














from typing import TYPE_CHECKING

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


snake_case    				=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SwinForImageClassification""",
       """SwinForMaskedImageModeling""",
       """SwinModel""",
       """SwinPreTrainedModel""",
       """SwinBackbone""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFSwinForImageClassification""",
       """TFSwinForMaskedImageModeling""",
       """TFSwinModel""",
       """TFSwinPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






