#%%
import pandas as pd
import numpy as np
import os
from tqdm import tqdm
import timm
import torchvision.transforms as T
# from albumentations.pytorch import ToTensorV2

from PIL import Image
import torch
import torch.nn as nn
import json 
# from transformers import AutoImageProcessor
# from create_model import HieraForImageClassification
#%%
# %%


SZ = 224
LABELS = json.load(open("./labels_class_map_rev.json"))
ORIGINAL_LABELS = json.load(open("./original_mapping.json"))
def is_gpu_available():
    """Check if the python package `onnxruntime-gpu` is installed."""
    return torch.cuda.is_available()

# VALID_AUG = A.Compose([
#     A.SmallestMaxSize(max_size=SZ + 16, p=1.0),
#     A.CenterCrop(height=SZ, width=SZ, p=1.0),
#     A.Normalize(),
#     ToTensorV2(),
# ])


def get_corn_model(model_name, pretrained=True, **kwargs):
    model = timm.create_model(model_name, pretrained=pretrained, **kwargs)
    model = nn.Sequential(
        model,
        nn.Dropout(0.15),
        nn.Linear(model.num_classes, model.num_classes * 2) ,
        nn.Linear(model.num_classes * 2, len(LABELS))
    
    )
    return model

class PytorchWorker:

    def __init__(self):
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        def _load_model():
            print("Setting up Pytorch Model")
            
            print(f"Using devide: {self.device}")
            model = get_corn_model("vit_base_patch16_224", pretrained=False)
            model_ckpt = torch.load("./NB_EXP_V2_008/vit_base_patch16_224_224_bs32_ep16_lr6e05_wd0.05_mixup_cutmix_CV_0.pth", map_location=self.device)
            model.load_state_dict(model_ckpt)
            return model.to(self.device)
        
        self.transforms = T.Compose([T.Resize((SZ, SZ)),
                                            T.ToTensor(),
                                            T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
        self.model = _load_model()

    def predict_image(self, image: np.ndarray) -> list():
        """Run inference using ONNX runtime.
        :param image: Input image as numpy array.
        :return: A list with logits and confidences.
        """
        image_data = self.transforms(image).unsqueeze(0).to(self.device)
        outputs = self.model(image_data)
        logits = outputs
        return logits.tolist()


def make_submission(test_metadata, model_path, model_name, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
    """Make submission with given """

    model = PytorchWorker()
    predictions = []

    for _, row in tqdm(test_metadata.iterrows(), total=len(test_metadata)):
        image_path = os.path.join(images_root_path, row.filename)
        # image_path = row.filename

        image = Image.open(image_path).convert("RGB")

        output = model.predict_image(image)
        string_label_dup = LABELS.get(str(np.argmax(output)), 'Acanthophis antarcticus')
        prediction_class = ORIGINAL_LABELS.get(string_label_dup, 1)
        predictions.append(prediction_class)
    print(predictions)

    test_metadata["class_id"] = predictions

    user_pred_df = test_metadata.drop_duplicates("observation_id", keep="first")
    user_pred_df[["observation_id", "class_id"]].to_csv(output_csv_path, index=None)

#%%
if __name__ == "__main__":

    import zipfile

    with zipfile.ZipFile("/tmp/data/private_testset.zip", 'r') as zip_ref:
        zip_ref.extractall("/tmp/data")

    MODEL_PATH = "pytorch_model.bin"
    MODEL_NAME = "swinv2_tiny_window16_256.ms_in1k"

    metadata_file_path = "./SnakeCLEF2024_TestMetadata.csv"
    test_metadata = pd.read_csv(metadata_file_path)

    # test_metadata = pd.DataFrame()
    # test_metadata['filename'] = ['../sample.png', '../sample copy.png', '../sample copy 2.png']
    # test_metadata['observation_id'] = [1, 2, 3]
    make_submission(
        test_metadata=test_metadata,
        model_path=MODEL_PATH,
        model_name=MODEL_NAME
    )

# #%%
# import requests
# image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
# # %%
# image = VALID_AUG(image=np.array(image))['image']
# # %%
# model= PytorchWorker()
# # %%
# output = model.predict_image(image.unsqueeze(dim =0 ))
# # %%
# output
# # %%
# import numpy as np 
# np.argmax(output)
# %%
# df = pd.DataFrame() 
# df["filename"] = ['sample.png']
# # %%
# make_submission(
#         test_metadata=df,
#         model_path="MODEL_PATH",
#         model_name="MODEL_NAME"
#     )
# %%

# %%