# 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.
""" WinoGAViL Loading Script """


import json
import os
import pandas as pd 
import datasets
from huggingface_hub import hf_hub_url

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{bitton2022winogavil,
  title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models},
  author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy},
  journal={arXiv preprint arXiv:2207.12576},
  year={2022}
}
"""

_DESCRIPTION = """\
WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more.
"""

_HOMEPAGE = "https://winogavil.github.io/"

_LICENSE = "https://creativecommons.org/licenses/by/4.0/"

_URL = "https://huggingface.co/datasets/nlphuji/winogavil/blob/main"

class Winogavil(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('winogavil', 'test')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="TEST", version=VERSION, description="winogavil dataset"),
    ]
    IMAGE_EXTENSION = "jpg"

    def _info(self):
        features = datasets.Features(
            {
                "candidates": [datasets.Value("string")],
                #"candidates_images": [datasets.Value("string")], 
                "candidates_images": [datasets.Image()],
                "cue": datasets.Value("string"),
                "associations": [datasets.Value("string")],
                'score_fool_the_ai': datasets.Value("float64"),
                'num_associations': datasets.Value("int64"),
                'num_candidates': datasets.Value("int64"),
                'solvers_jaccard_mean': datasets.Value("float64"),
                'solvers_jaccard_std': datasets.Value("float64"),
                'ID': datasets.Value("int64"),
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        data_dir = dl_manager.download_and_extract({
            "examples_csv": hf_hub_url("datasets/nlphuji/winogavil", filename="winogavil_dataset.csv"),
            "images_dir": hf_hub_url("datasets/nlphuji/winogavil", filename="winogavil_images.zip")
            })
            
        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, examples_csv, images_dir):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.

        df = pd.read_csv(examples_csv)

        # columns_to_serialize = ['candidates', 'associations']
        # for c in columns_to_serialize:
        #   df[c] = df[c].apply(json.loads)
    
        for r_idx, r in df.iterrows():
            r_dict = r.to_dict()
            r_dict['candidates'] = json.loads(r_dict['candidates'])
            candidates_images = [os.path.join(images_dir, "winogavil_images", f"{x}.{self.IMAGE_EXTENSION}") for x in r_dict['candidates']]
            r_dict['candidates_images'] = candidates_images
            r_dict['associations'] = json.loads(r_dict['associations'])
            key = r['ID']
            yield key, r_dict