# coding=utf-8
# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Dataset class for Food-101 dataset."""

import datasets
from datasets.tasks import ImageClassification


_BASE_URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"

_METADATA_URLS = {
    "train": "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/train.txt",
    "test": "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/test.txt",
}

_HOMEPAGE = "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/"

_DESCRIPTION = (
    "This dataset consists of 101 food categories, with 101'000 images. For "
    "each class, 250 manually reviewed test images are provided as well as 750"
    " training images. On purpose, the training images were not cleaned, and "
    "thus still contain some amount of noise. This comes mostly in the form of"
    " intense colors and sometimes wrong labels. All images were rescaled to "
    "have a maximum side length of 512 pixels."
)

_CITATION = """\
 @inproceedings{bossard14,
  title = {Food-101 -- Mining Discriminative Components with Random Forests},
  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
  booktitle = {European Conference on Computer Vision},
  year = {2014}
}
"""

_LICENSE = """\
LICENSE AGREEMENT
=================
 - The Food-101 data set consists of images from Foodspotting [1] which are not
   property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond
   scientific fair use must be negociated with the respective picture owners
   according to the Foodspotting terms of use [2].

[1] http://www.foodspotting.com/
[2] http://www.foodspotting.com/terms/
"""

_NAMES = [
    "apple_pie",
    "baby_back_ribs",
    "baklava",
    "beef_carpaccio",
    "beef_tartare",
    "beet_salad",
    "beignets",
    "bibimbap",
    "bread_pudding",
    "breakfast_burrito",
    "bruschetta",
    "caesar_salad",
    "cannoli",
    "caprese_salad",
    "carrot_cake",
    "ceviche",
    "cheesecake",
    "cheese_plate",
    "chicken_curry",
    "chicken_quesadilla",
    "chicken_wings",
    "chocolate_cake",
    "chocolate_mousse",
    "churros",
    "clam_chowder",
    "club_sandwich",
    "crab_cakes",
    "creme_brulee",
    "croque_madame",
    "cup_cakes",
    "deviled_eggs",
    "donuts",
    "dumplings",
    "edamame",
    "eggs_benedict",
    "escargots",
    "falafel",
    "filet_mignon",
    "fish_and_chips",
    "foie_gras",
    "french_fries",
    "french_onion_soup",
    "french_toast",
    "fried_calamari",
    "fried_rice",
    "frozen_yogurt",
    "garlic_bread",
    "gnocchi",
    "greek_salad",
    "grilled_cheese_sandwich",
    "grilled_salmon",
    "guacamole",
    "gyoza",
    "hamburger",
    "hot_and_sour_soup",
    "hot_dog",
    "huevos_rancheros",
    "hummus",
    "ice_cream",
    "lasagna",
    "lobster_bisque",
    "lobster_roll_sandwich",
    "macaroni_and_cheese",
    "macarons",
    "miso_soup",
    "mussels",
    "nachos",
    "omelette",
    "onion_rings",
    "oysters",
    "pad_thai",
    "paella",
    "pancakes",
    "panna_cotta",
    "peking_duck",
    "pho",
    "pizza",
    "pork_chop",
    "poutine",
    "prime_rib",
    "pulled_pork_sandwich",
    "ramen",
    "ravioli",
    "red_velvet_cake",
    "risotto",
    "samosa",
    "sashimi",
    "scallops",
    "seaweed_salad",
    "shrimp_and_grits",
    "spaghetti_bolognese",
    "spaghetti_carbonara",
    "spring_rolls",
    "steak",
    "strawberry_shortcake",
    "sushi",
    "tacos",
    "takoyaki",
    "tiramisu",
    "tuna_tartare",
    "waffles",
]

_IMAGES_DIR = "food-101/images/"


class Food101(datasets.GeneratorBasedBuilder):
    """Food-101 Images dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(names=_NAMES),
                }
            ),
            supervised_keys=("image", "label"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            task_templates=[ImageClassification(image_column="image", label_column="label", labels=_NAMES)],
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download(_BASE_URL)
        split_metadata_paths = dl_manager.download(_METADATA_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "metadata_path": split_metadata_paths["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "metadata_path": split_metadata_paths["test"],
                },
            ),
        ]

    def _generate_examples(self, images, metadata_path):
        """Generate images and labels for splits."""
        with open(metadata_path, encoding="utf-8") as f:
            files_to_keep = set(f.read().split("\n"))
        for file_path, file_obj in images:
            if file_path.startswith(_IMAGES_DIR):
                if file_path[len(_IMAGES_DIR) : -len(".jpg")] in files_to_keep:
                    label = file_path.split("/")[2]
                    yield file_path, {
                        "image": {"path": file_path, "bytes": file_obj.read()},
                        "label": label,
                    }