from pathlib import Path

import datasets
import numpy as np
import pandas as pd
import PIL.Image
import PIL.ImageOps

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {generated-usa-passeports-dataset},
author = {TrainingDataPro},
year = {2023}
}
"""

_DESCRIPTION = """\
The dataset consists of selfies of people and videos of them wearing a printed
2d mask with their face. The dataset solves tasks in the field of anti-spoofing
and it is useful for buisness and safety systems.
The dataset includes: **attacks** - videos of people wearing printed portraits
of themselves with cut-out eyes.
"""
_NAME = 'generated-usa-passeports-dataset'

_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"

_LICENSE = "cc-by-nc-nd-4.0"

_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"


def exif_transpose(img):
    if not img:
        return img

    exif_orientation_tag = 274

    # Check for EXIF data (only present on some files)
    if hasattr(img, "_getexif") and isinstance(
            img._getexif(), dict) and exif_orientation_tag in img._getexif():
        exif_data = img._getexif()
        orientation = exif_data[exif_orientation_tag]

        # Handle EXIF Orientation
        if orientation == 1:
            # Normal image - nothing to do!
            pass
        elif orientation == 2:
            # Mirrored left to right
            img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)
        elif orientation == 3:
            # Rotated 180 degrees
            img = img.rotate(180)
        elif orientation == 4:
            # Mirrored top to bottom
            img = img.rotate(180).transpose(PIL.Image.FLIP_LEFT_RIGHT)
        elif orientation == 5:
            # Mirrored along top-left diagonal
            img = img.rotate(-90,
                             expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT)
        elif orientation == 6:
            # Rotated 90 degrees
            img = img.rotate(-90, expand=True)
        elif orientation == 7:
            # Mirrored along top-right diagonal
            img = img.rotate(90,
                             expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT)
        elif orientation == 8:
            # Rotated 270 degrees
            img = img.rotate(90, expand=True)

    return img


def load_image_file(file, mode='RGB'):
    # Load the image with PIL
    img = PIL.Image.open(file)

    if hasattr(PIL.ImageOps, 'exif_transpose'):
        # Very recent versions of PIL can do exit transpose internally
        img = PIL.ImageOps.exif_transpose(img)
    else:
        # Otherwise, do the exif transpose ourselves
        img = exif_transpose(img)

    img = img.convert(mode)

    return np.array(img)


class GeneratedUsaPasseportsDataset(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                'original': datasets.Image(),
                'us_pass_augmentated_1': datasets.Image(),
                'us_pass_augmentated_2': datasets.Image(),
                'us_pass_augmentated_3': datasets.Image()
            }),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE)

    def _split_generators(self, dl_manager):
        original = dl_manager.download_and_extract(f"{_DATA}original.zip")
        augmentation = dl_manager.download_and_extract(
            f"{_DATA}augmentation.zip")
        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
        # original = dl_manager.iter_files(original)
        # augmentation = dl_manager.iter_files(augmentation)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={
                                        "original": original,
                                        'augmentation': augmentation,
                                        'annotations': annotations
                                    }),
        ]

    def _generate_examples(self, original, augmentation, annotations):
        annotations_df = pd.read_csv(annotations, sep=';')

        for row in annotations_df.itertuples():
            yield row[0], {
                'original':
                    load_image_file(f'{original}/{row[1]}'),
                'us_pass_augmentated_1':
                    load_image_file(f'{augmentation}/{row[2]}'),
                'us_pass_augmentated_2':
                    load_image_file(f'{augmentation}/{row[3]}'),
                'us_pass_augmentated_3':
                    load_image_file(f'{augmentation}/{row[4]}')
            }

        # for idx, (image_path, (attack_path, attack)) in enumerate(
        #         zip(sorted(images), sorted(attacks, key=lambda x: x[0]))):
        #     image_name = Path(image_path).name
        #     yield idx, {
        #         "photo":
        #             load_image_file(image_path),
        #         "attack":
        #             attack_path,
        #         # annotations_df.loc[annotations_df['photo'].str.lower() ==
        #         #                    image_name.lower()]['attack'].values[0],
        #         'phone':
        #             annotations_df.loc[annotations_df['photo'].str.lower() ==
        #                                image_name.lower()]['phone'].values[0],
        #         'gender':
        #             annotations_df.loc[annotations_df['photo'].str.lower() ==
        #                                image_name.lower()]['gender'].values[0],
        #         'age':
        #             annotations_df.loc[annotations_df['photo'].str.lower() ==
        #                                image_name.lower()]['age'].values[0],
        #         'country':
        #             annotations_df.loc[annotations_df['photo'].str.lower() ==
        #                                image_name.lower()]
        #             ['country'].values[0],
        #     }