import csv
import json
import os
from PIL import Image

import pandas as pd
from huggingface_hub import hf_hub_download, snapshot_download
import datasets
import cv2

# _CITATION = """\
# @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# """

_DESCRIPTION = """\
Image Quality Assessment Dataset consisting of 25 reference images, 17 different distortions and 4 intensities per distortion. 
In total there are 1700 (reference, distortion, MOS) tuples.
"""

# _HOMEPAGE = ""

# _LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URLS = {
#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
# }

# _REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata" # Stolen from imagenette.py
_REPO = "https://huggingface.co/datasets/Jorgvt/TID2008/resolve/main"

class TID2008(datasets.GeneratorBasedBuilder):
    """TID2008 Image Quality Dataset"""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "reference": datasets.Image(),
                "distorted": datasets.Image(),
                "mos": datasets.Value("float")
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            # supervised_keys=("reference", "distorted", "mos"),
            # homepage=_HOMEPAGE,
            # license=_LICENSE,
            # citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_path = dl_manager.download("image_pairs_mos.csv")
        data = pd.read_csv(data_path, index_col=0)

        # kk = dl_manager.download("distorted_images")
        # print(kk)

        root_path = "/".join(data_path.split("/")[:-1])
        # reference_path = dl_manager.download("reference_images")
        # distorted_path = dl_manager.download("distorted_images")
        
        reference_paths = data["Reference"].apply(lambda x: os.path.join("reference_images", x))
        distorted_paths = data["Distorted"].apply(lambda x: os.path.join("distorted_images", x))

        reference_paths = dl_manager.download(reference_paths)
        distorted_paths = dl_manager.download(distorted_paths)
        
        # dl_manager.download(data["Reference"])

        # data["Reference"] = data["Reference"].apply(lambda x: os.path.join(reference_path, x))
        # data["Distorted"] = data["Distorted"].apply(lambda x: os.path.join(distorted_path, x))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    # "reference": data["Reference"],
                    # "distorted": data["Distorted"],
                    "reference": reference_paths,
                    "distorted": distorted_paths,
                    "mos": data["MOS"],
                    "split": "train",
                },
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, reference, distorted, mos, split):
        for key, (ref, dist, m) in enumerate(zip(reference, distorted, mos)):
            yield key, {
                "reference": ref,
                "distorted": dist,
                "mos": m,
            }
        # with open(filepath, encoding="utf-8") as f:
        #     for key, row in enumerate(f):
        #         data = json.loads(row)
        #         if self.config.name == "first_domain":
        #             # Yields examples as (key, example) tuples
        #             yield key, {
        #                 "sentence": data["sentence"],
        #                 "option1": data["option1"],
        #                 "answer": "" if split == "test" else data["answer"],
        #             }
        #         else:
        #             yield key, {
        #                 "sentence": data["sentence"],
        #                 "option2": data["option2"],
        #                 "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
        #             }