# Copyright 2020 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os
from pathlib import Path

import datasets
from datasets.tasks import ImageClassification
import numpy as np

_CITATION = """\
@article{FeiFei2004LearningGV,
  title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories},
  author={Li Fei-Fei and Rob Fergus and Pietro Perona},
  journal={Computer Vision and Pattern Recognition Workshop},
  year={2004},
}
"""

_DESCRIPTION = """\
Pictures of objects belonging to 101 categories. 
About 40 to 800 images per category. 
Most categories have about 50 images. 
Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc'Aurelio Ranzato. 
The size of each image is roughly 300 x 200 pixels. 
"""

_HOMEPAGE = "https://data.caltech.edu/records/20086"

_LICENSE = "CC BY 4.0"

# _DATA_URL = "brand_new_data/caltech-101.zip"
_DATA_URL = "brand_new_data/caltech-101/101_ObjectCategories.tar.gz"

_NAMES = [
    "accordion",
    "airplanes",
    "anchor",
    "ant",
    "background_google",
    "barrel",
    "bass",
    "beaver",
    "binocular",
    "bonsai",
    "brain",
    "brontosaurus",
    "buddha",
    "butterfly",
    "camera",
    "cannon",
    "car_side",
    "ceiling_fan",
    "cellphone",
    "chair",
    "chandelier",
    "cougar_body",
    "cougar_face",
    "crab",
    "crayfish",
    "crocodile",
    "crocodile_head",
    "cup",
    "dalmatian",
    "dollar_bill",
    "dolphin",
    "dragonfly",
    "electric_guitar",
    "elephant",
    "emu",
    "euphonium",
    "ewer",
    "faces",
    "faces_easy",
    "ferry",
    "flamingo",
    "flamingo_head",
    "garfield",
    "gerenuk",
    "gramophone",
    "grand_piano",
    "hawksbill",
    "headphone",
    "hedgehog",
    "helicopter",
    "ibis",
    "inline_skate",
    "joshua_tree",
    "kangaroo",
    "ketch",
    "lamp",
    "laptop",
    "leopards",
    "llama",
    "lobster",
    "lotus",
    "mandolin",
    "mayfly",
    "menorah",
    "metronome",
    "minaret",
    "motorbikes",
    "nautilus",
    "octopus",
    "okapi",
    "pagoda",
    "panda",
    "pigeon",
    "pizza",
    "platypus",
    "pyramid",
    "revolver",
    "rhino",
    "rooster",
    "saxophone",
    "schooner",
    "scissors",
    "scorpion",
    "sea_horse",
    "snoopy",
    "soccer_ball",
    "stapler",
    "starfish",
    "stegosaurus",
    "stop_sign",
    "strawberry",
    "sunflower",
    "tick",
    "trilobite",
    "umbrella",
    "watch",
    "water_lilly",
    "wheelchair",
    "wild_cat",
    "windsor_chair",
    "wrench",
    "yin_yang",
]

_TRAIN_POINTS_PER_CLASS = 30


class Caltech101(datasets.GeneratorBasedBuilder):
    """Caltech 101 dataset."""

    VERSION = datasets.Version("1.0.0")

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

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_DATA_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir,  # TODO: change accordingly
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir,  # TODO: change accordingly
                    "split": "test",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # Same stratagy as the one proposed in TF datasets 
        is_train_split = (split == "train")
        data_dir = Path(filepath) / "101_ObjectCategories"
        # Sets random seed so the random partitioning of files is the same when
        # called for the train and test splits.
        numpy_original_state = np.random.get_state()
        np.random.seed(1234)

        for class_dir in data_dir.iterdir():
            fnames = [image_path for image_path in class_dir.iterdir() if image_path.name.endswith(".jpg")]
            assert [image_path for image_path in class_dir.iterdir() if not image_path.name.endswith(".jpg")] == []
            # _TRAIN_POINTS_PER_CLASS datapoints are sampled for the train split,
            # the others constitute the test split.
            if _TRAIN_POINTS_PER_CLASS > len(fnames):
                raise ValueError("Fewer than {} ({}) points in class {}".format(
                    _TRAIN_POINTS_PER_CLASS, len(fnames), class_dir.name))
            train_fnames = np.random.choice(
                fnames, _TRAIN_POINTS_PER_CLASS, replace=False)
            test_fnames = set(fnames).difference(train_fnames)
            fnames_to_emit = train_fnames if is_train_split else test_fnames

            for image_file in fnames_to_emit:
                record = {
                    "image": str(image_file),
                    "label": class_dir.name.lower(),
                }
                yield "%s/%s" % (class_dir.name.lower(), image_file), record
        # Resets the seeds to their previous states.
        np.random.set_state(numpy_original_state)