import numpy
import torch
from typing import NamedTuple

from towhee.operator.base import NNOperator
from towhee.utils.pil_utils import to_pil
from towhee.types.image import Image as towheeImage

from torch import nn

from timm.data.transforms_factory import create_transform
from timm.data import resolve_data_config
from timm.models.factory import create_model

import warnings
warnings.filterwarnings('ignore')

class TimmImage(NNOperator):
    """
    Pytorch image embedding operator that uses the Pytorch Image Model (timm) collection.
    Args:
        model_name (`str`):
            Which model to use for the embeddings.
    """
    def __init__(self, model_name: str, num_classes: int = 1000) -> None:
        super().__init__()
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.model = create_model(model_name, pretrained=True, num_classes=num_classes)
        self.model.to(self.device)
        self.model.eval()
        config = resolve_data_config({}, model=self.model)
        self.tfms = create_transform(**config)

    def __call__(self, image: 'towheeImage') -> NamedTuple('Outputs', [('vec', numpy.ndarray)]):
        img = self.tfms(to_pil(image)).unsqueeze(0)
        img = img.to(self.device)
        features = self.model.forward_features(img)
        if features.dim() == 4:
            global_pool = nn.AdaptiveAvgPool2d(1)
            features = global_pool(features)

        features = features.to('cpu')
        feature_vector = features.flatten().detach().numpy()
        Outputs = NamedTuple('Outputs', [('vec', numpy.ndarray)])
        return Outputs(feature_vector)