



import importlib

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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=False    ):
			A							:					Union[str, Any]              =						OmegaConf.load(_lowerCamelCase    )
			if display:
						print(yaml.dump(OmegaConf.to_container(_lowerCamelCase    )    )    )
			return config




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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A       , A       , A							:					List[str]              =						model.encode(_lowerCamelCase    )
			print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""    )
			A							:					List[Any]              =						model.decode(_lowerCamelCase    )
			return xrec




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=False    ):
			A       , A							:					str              =						string.rsplit("." ,    1    )
			if reload:
						A							:					Union[str, Any]              =						importlib.import_module(_lowerCamelCase    )
						importlib.reload(_lowerCamelCase    )
			return getattr(importlib.import_module(_lowerCamelCase ,    package=_lowerCamelCase    ) ,    cls    )




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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase=True ,    _lowerCamelCase=True    ):
			A							:					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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			# load the specified checkpoint
			if ckpt:
						A							:					List[str]              =						torch.load(_lowerCamelCase ,    map_location="cpu"    )
						A							:					str              =						pl_sd["global_step"]
						print(f"""loaded model from global step {global_step}."""    )
			else:
						A							:					int              =						{"state_dict": None}
						A							:					List[str]              =						None
			A							:					Optional[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_torch_available


__SCREAMING_SNAKE_CASE           =  {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """InstructBlipQFormerModel""",
							    """InstructBlipPreTrainedModel""",
							    """InstructBlipForConditionalGeneration""",
							    """InstructBlipVisionModel""",
							]

if TYPE_CHECKING:
							from .configuration_instructblip import (
							    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    InstructBlipConfig,
							    InstructBlipQFormerConfig,
							    InstructBlipVisionConfig,
							)
							from .processing_instructblip import InstructBlipProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_instructblip import (
														    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    InstructBlipForConditionalGeneration,
														    InstructBlipPreTrainedModel,
														    InstructBlipQFormerModel,
														    InstructBlipVisionModel,
														)

else:
							import sys

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