import os
# import sys
from functools import partial
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from torch import Tensor, nn
from torchvision import models, transforms
import pandas as pd


class ModelInterface:
    def __init__(self, config):
        # TODO: doc string
        # TODO: default values for config.get(...)
        self.device = torch.device(
            f"cuda:{config.get('gpu_kernel')}" if torch.cuda.is_available() else "cpu"
        )
        normalization = (const["NORM_MEAN"], const["NORM_SD"])
        # TODO: config is changed by transform['normalize'] = normalization
        transform = config.get("transform_surface")
        transform["normalize"] = normalization
        self.transform_surface = transform
        transform = config.get("transform_road_type")
        transform["normalize"] = normalization
        self.transform_road_type = transform
        self.model_root = Path(config.get("model_root"))
        self.models = config.get("models")
        self.hf_model_repo = config.get("hf_model_repo")

    @staticmethod
    def custom_crop(img, crop_style=None):
        im_width, im_height = img.size
        if crop_style == const["CROP_LOWER_MIDDLE_HALF"]:
            top = im_height / 2
            left = im_width / 4
            height = im_height / 2
            width = im_width / 2
        elif crop_style == const["CROP_LOWER_HALF"]:
            top = im_height / 2
            left = 0
            height = im_height / 2
            width = im_width
        else:  # None, or not valid
            return img

        cropped_img = transforms.functional.crop(img, top, left, height, width)
        return cropped_img

    def transform(
        self,
        resize=None,
        crop=None,
        to_tensor=True,
        normalize=None,
    ):
        """
        Create a PyTorch image transformation function based on specified parameters.

        Parameters:
            - resize (tuple or None): Target size for resizing, e.g. (height, width).
            - crop (string): crop style e.g. 'lower_middle_third'
            - to_tensor (bool): Converts the PIL Image (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
            - normalize (tuple of lists [r, g, b] or None): Mean and standard deviation for normalization.

        Returns:
            PyTorch image transformation function.
        """
        transform_list = []

        if crop is not None:
            transform_list.append(
                transforms.Lambda(partial(self.custom_crop, crop_style=crop))
            )

        if resize is not None:
            if isinstance(resize, int):
                resize = (resize, resize)
            transform_list.append(transforms.Resize(resize))

        if to_tensor:
            transform_list.append(transforms.ToTensor())

        if normalize is not None:
            transform_list.append(transforms.Normalize(*normalize))

        composed_transform = transforms.Compose(transform_list)
        return composed_transform

    def preprocessing(self, img_data_raw, transform):
        transform = self.transform(**transform)
        img_data = torch.stack([transform(img) for img in img_data_raw])
        return img_data

    def load_model(self, model):
        model_path = self.model_root / model
        # load model data from hugging face if not locally available
        if not os.path.exists(model_path):
            print(
                f"Model file not found at {model_path}. Downloading from Hugging Face..."
            )
            try:
                os.makedirs(self.model_root, exist_ok=True)
                model_path = hf_hub_download(
                    repo_id=self.hf_model_repo, filename=model, local_dir=self.model_root
                )
                print(f"Model file downloaded to {model_path}.")
            except Exception as e:
                print(f"An unexpected error occurred while downloading the model: {e}")
                return None, {}, False
            
        model_state = torch.load(model_path, map_location=self.device)
        model_name = model_state["model_name"]
        is_regression = model_state["is_regression"]
        class_to_idx = model_state["class_to_idx"]
        num_classes = 1 if is_regression else len(class_to_idx.items())
        model_state_dict = model_state["model_state_dict"]
        model_cls = model_mapping[model_name]
        model = model_cls(num_classes=num_classes)
        model.load_state_dict(model_state_dict)

        return model, class_to_idx, is_regression

    def predict(self, model, data):
        model.to(self.device)
        model.eval()

        image_batch = data.to(self.device)

        with torch.no_grad():
            batch_outputs = model(image_batch)
            # batch_classes, batch_values = model.get_class_and_value(batch_outputs)
            batch_values = model.get_class_probabilities(batch_outputs)

        return batch_values
    
    @staticmethod
    def predict_value_to_class(batch_values, class_to_idx, ids, level=""):
        columns = ["id", "level", "value", "class"]
        batch_size = list(batch_values.shape)
        if len(batch_size) < 2:
            batch_size = [batch_size[0], 1]
        df = pd.DataFrame(columns=columns, index=range(batch_size[0] * batch_size[1]))
        idx_to_class = {i: cls for cls, i in class_to_idx.items()}
        
        if batch_size[1] == 1:
            batch_classes = [
                idx_to_class[
                    min(
                        max(idx.item(), min(list(class_to_idx.values()))),
                        max(list(class_to_idx.values())),
                    )
                ]
                for idx in batch_values.round().int()
            ]
            i = 0
            for id, value, cls in zip(ids, batch_values, batch_classes):
                df.iloc[i] = [id, level, value.item(), cls]
                i += 1    
        else:
            batch_classes = [idx_to_class[idx.item()] for idx in torch.argmax(batch_values, dim=1)]
            i = 0
            for id, values in zip(ids, batch_values):
                for idx, value in enumerate(values.tolist()):
                    df.iloc[i] = [id, level, value, idx_to_class[idx]]
                    i += 1
            
        return df, batch_classes

    def batch_classifications(self, img_data_raw, img_ids=None):
        # default image ids
        if img_ids is None:
            img_ids = range(len(img_data_raw))

        df = pd.DataFrame()

        # road type
        level = "road_type"
        model_file = self.models.get(level)
        if model_file is not None:
            model, class_to_idx, _ = self.load_model(model=model_file)
            if model is None:
                print(f"Road type model '{model_file}' is not found.\n"
                      + "Road type prediction is skipped.")
            else:
                data = self.preprocessing(img_data_raw, self.transform_road_type)
                values = self.predict(model, data)
                df_tmp, _ = self.predict_value_to_class(
                    values,
                    class_to_idx,
                    img_ids,
                    level,
                    )
                df = pd.concat([df, df_tmp], ignore_index=True)

        # surface type
        level = "surface_type"
        model_file = self.models.get(level)
        if model_file is not None:
            model, class_to_idx, _ = self.load_model(model=model_file)
            if model is None:
                print(f"Surface type model '{model_file}' is not found.\n"
                      + "Surface type prediction is skipped.")
            else:
                data = self.preprocessing(img_data_raw, self.transform_surface)
                values = self.predict(model, data)
                df_tmp, classes = self.predict_value_to_class(
                    values,
                    class_to_idx,
                    img_ids,
                    level,
                    )
                df = pd.concat([df, df_tmp], ignore_index=True)

                # surface quality
                level = "surface_quality"
                sub_models = self.models.get(level)
                if sub_models is not None:
                    surface_indices = {}
                    for i, surface_type in enumerate(classes):
                        if surface_type not in surface_indices:
                            surface_indices[surface_type] = []
                        surface_indices[surface_type].append(i)

                    for surface_type, indices in surface_indices.items():
                        model_file = sub_models.get(surface_type)
                        if model_file is not None:
                            model, class_to_idx, _ = self.load_model(model=model_file)
                            if model is None:
                                print(f"Quality model '{model_file}' is not found.\n"
                                    + f"Quality prediction is skipped for surface '{surface_type}'.")
                            else:
                                values = self.predict(model, data[indices])
                                df_tmp, _ = self.predict_value_to_class(
                                    values,
                                    class_to_idx,
                                    [img_ids[i] for i in indices],
                                    level,
                                    )
                                df = pd.concat([df, df_tmp], ignore_index=True)

        return df


class CustomEfficientNetV2SLinear(nn.Module):
    def __init__(self, num_classes, avg_pool=1):
        super(CustomEfficientNetV2SLinear, self).__init__()

        model = models.efficientnet_v2_s(weights="IMAGENET1K_V1")
        # adapt output layer
        in_features = model.classifier[-1].in_features * (avg_pool * avg_pool)
        fc = nn.Linear(in_features, num_classes, bias=True)
        model.classifier[-1] = fc

        self.features = model.features
        self.avgpool = nn.AdaptiveAvgPool2d(avg_pool)
        self.classifier = model.classifier
        if num_classes == 1:
            self.criterion = nn.MSELoss
            self.is_regression = True
        else:
            self.criterion = nn.CrossEntropyLoss
            self.is_regression = False

    def get_class_probabilities(self, x):
        if self.is_regression:
            x = x.flatten()
        else:
            x = nn.functional.softmax(x, dim=1)
        return x

    def forward(self, x: Tensor) -> Tensor:
        x = self.features(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)

        x = self.classifier(x)

        return x

    # def get_optimizer_layers(self):
    #     return self.classifier


# Model settings
const = {
    "EFFNET_LINEAR": "efficientNetV2SLinear",
    "CROP_LOWER_MIDDLE_HALF": "lower_middle_half",
    "CROP_LOWER_HALF": "lower_half",
    "NORM_MEAN": [0.42834484577178955, 0.4461250305175781, 0.4350937306880951],
    "NORM_SD": [0.22991590201854706, 0.23555299639701843, 0.26348039507865906],
}

model_mapping = {
    const["EFFNET_LINEAR"]: CustomEfficientNetV2SLinear,
}