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"""

 Copyright (c) 2022, salesforce.com, inc.

 All rights reserved.

 SPDX-License-Identifier: BSD-3-Clause

 For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause

"""

import logging
import torch
from omegaconf import OmegaConf
from lavis.common.registry import registry

from lavis.models.base_model import BaseModel

from lavis.models.protein_models.protein_function import Blip2ProteinBase
from lavis.models.protein_models.protein_function_qformer import Blip2ProteinQformer
from lavis.models.protein_models.protein_function_opt import Blip2ProteinMistral

from lavis.processors.base_processor import BaseProcessor


__all__ = [
    "load_model",
    "BaseModel",
    "Blip2ProteinBase",
    "Blip2ProteinQformer",
    "Blip2ProteinMistral"
]


def load_model(name, model_type, is_eval=False, device="cpu", checkpoint=None):
    """

    Load supported models.



    To list all available models and types in registry:

    >>> from lavis.models import model_zoo

    >>> print(model_zoo)



    Args:

        name (str): name of the model.

        model_type (str): type of the model.

        is_eval (bool): whether the model is in eval mode. Default: False.

        device (str): device to use. Default: "cpu".

        checkpoint (str): path or to checkpoint. Default: None.

            Note that expecting the checkpoint to have the same keys in state_dict as the model.



    Returns:

        model (torch.nn.Module): model.

    """

    model = registry.get_model_class(name).from_pretrained(model_type=model_type)

    if checkpoint is not None:
        model.load_checkpoint(checkpoint)

    if is_eval:
        model.eval()

    if device == "cpu":
        model = model.float()

    return model.to(device)


def load_preprocess(config):
    """

    Load preprocessor configs and construct preprocessors.



    If no preprocessor is specified, return BaseProcessor, which does not do any preprocessing.



    Args:

        config (dict): preprocessor configs.



    Returns:

        vis_processors (dict): preprocessors for visual inputs.

        txt_processors (dict): preprocessors for text inputs.



        Key is "train" or "eval" for processors used in training and evaluation respectively.

    """

    def _build_proc_from_cfg(cfg):
        return (
            registry.get_processor_class(cfg.name).from_config(cfg)
            if cfg is not None
            else BaseProcessor()
        )

    vis_processors = dict()
    txt_processors = dict()

    vis_proc_cfg = config.get("vis_processor")
    txt_proc_cfg = config.get("text_processor")

    if vis_proc_cfg is not None:
        vis_train_cfg = vis_proc_cfg.get("train")
        vis_eval_cfg = vis_proc_cfg.get("eval")
    else:
        vis_train_cfg = None
        vis_eval_cfg = None

    vis_processors["train"] = _build_proc_from_cfg(vis_train_cfg)
    vis_processors["eval"] = _build_proc_from_cfg(vis_eval_cfg)

    if txt_proc_cfg is not None:
        txt_train_cfg = txt_proc_cfg.get("train")
        txt_eval_cfg = txt_proc_cfg.get("eval")
    else:
        txt_train_cfg = None
        txt_eval_cfg = None

    txt_processors["train"] = _build_proc_from_cfg(txt_train_cfg)
    txt_processors["eval"] = _build_proc_from_cfg(txt_eval_cfg)

    return vis_processors, txt_processors


def load_model_and_preprocess(name, model_type, is_eval=False, device="cpu"):
    """

    Load model and its related preprocessors.



    List all available models and types in registry:

    >>> from lavis.models import model_zoo

    >>> print(model_zoo)



    Args:

        name (str): name of the model.

        model_type (str): type of the model.

        is_eval (bool): whether the model is in eval mode. Default: False.

        device (str): device to use. Default: "cpu".



    Returns:

        model (torch.nn.Module): model.

        vis_processors (dict): preprocessors for visual inputs.

        txt_processors (dict): preprocessors for text inputs.

    """
    model_cls = registry.get_model_class(name)

    # load model
    model = model_cls.from_pretrained(model_type=model_type)

    if is_eval:
        model.eval()

    # load preprocess
    cfg = OmegaConf.load(model_cls.default_config_path(model_type))
    if cfg is not None:
        preprocess_cfg = cfg.preprocess

        vis_processors, txt_processors = load_preprocess(preprocess_cfg)
    else:
        vis_processors, txt_processors = None, None
        logging.info(
            f"""No default preprocess for model {name} ({model_type}).

                This can happen if the model is not finetuned on downstream datasets,

                or it is not intended for direct use without finetuning.

            """
        )

    if device == "cpu" or device == torch.device("cpu"):
        model = model.float()

    return model.to(device), vis_processors, txt_processors


class ModelZoo:
    """

    A utility class to create string representation of available model architectures and types.



    >>> from lavis.models import model_zoo

    >>> # list all available models

    >>> print(model_zoo)

    >>> # show total number of models

    >>> print(len(model_zoo))

    """

    def __init__(self) -> None:
        self.model_zoo = {
            k: list(v.PRETRAINED_MODEL_CONFIG_DICT.keys())
            for k, v in registry.mapping["model_name_mapping"].items()
        }

    def __str__(self) -> str:
        return (
            "=" * 50
            + "\n"
            + f"{'Architectures':<30} {'Types'}\n"
            + "=" * 50
            + "\n"
            + "\n".join(
                [
                    f"{name:<30} {', '.join(types)}"
                    for name, types in self.model_zoo.items()
                ]
            )
        )

    def __iter__(self):
        return iter(self.model_zoo.items())

    def __len__(self):
        return sum([len(v) for v in self.model_zoo.values()])


model_zoo = ModelZoo()