import json
import os

import datasets

_CITATION = """\@inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
}
"""

_DESCRIPTION = """\
ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. 
"""

_URLs = {
    "totto": {
        "data": "https://storage.googleapis.com/totto/totto_data.zip",
        "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/totto.zip",
    },
}


class Mlsum(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="totto",
            version=datasets.Version("1.0.0"),
            description=f"GEM benchmark: struct2text task",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features = datasets.Features(
                {
                    "gem_id": datasets.Value("string"),
                    "gem_parent_id": datasets.Value("string"),
                    "totto_id": datasets.Value("int32"),
                    "table_page_title": datasets.Value("string"),
                    "table_webpage_url": datasets.Value("string"),
                    "table_section_title": datasets.Value("string"),
                    "table_section_text": datasets.Value("string"),
                    "table": [
                        [
                            {
                                "column_span": datasets.Value("int32"),
                                "is_header": datasets.Value("bool"),
                                "row_span": datasets.Value("int32"),
                                "value": datasets.Value("string"),
                            }
                        ]
                    ],
                    "highlighted_cells": [[datasets.Value("int32")]],
                    "example_id": datasets.Value("string"),
                    "sentence_annotations": [
                        {
                            "original_sentence": datasets.Value("string"),
                            "sentence_after_deletion": datasets.Value("string"),
                            "sentence_after_ambiguity": datasets.Value("string"),
                            "final_sentence": datasets.Value("string"),
                        }
                    ],
                    "overlap_subset": datasets.Value("string"),
                    "target": datasets.Value("string"),  # single target for train
                    "references": [datasets.Value("string")],
                },
            ),
            supervised_keys=None,
            homepage="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
        challenge_sets = [
                ("challenge_train_sample", "train_totto_RandomSample500.json"),
                ("challenge_validation_sample", "validation_totto_RandomSample500.json"),
                ("challenge_test_scramble", "test_totto_ScrambleInputStructure500.json"),
            ]

        return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": os.path.join(dl_dir["data"], "totto_data/totto_train_data.jsonl"),
                        "split": "train",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "filepath": os.path.join(dl_dir["data"], "totto_data/totto_dev_data.jsonl"),
                        "split": "validation",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "filepath": os.path.join(dl_dir["data"], "totto_data/unlabeled_totto_test_data.jsonl"),
                        "split": "test",
                    },
                ),
            ] + [
                datasets.SplitGenerator(
                    name=challenge_split,
                    gen_kwargs={
                        "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename),
                        "split": challenge_split,
                    },
                )
                for challenge_split, filename in challenge_sets
            ]

    def _generate_examples(self, filepath, split, filepaths=None, lang=None):
        """Yields examples."""
        if "challenge" in split:
            exples = json.load(open(filepath, encoding="utf-8"))
            if isinstance(exples, dict):
                assert len(exples) == 1, "multiple entries found"
                exples = list(exples.values())[0]
            for id_, exple in enumerate(exples):
                if len(exple) == 0:
                    continue
                exple["gem_parent_id"] = exple["gem_id"]
                exple["gem_id"] = f"{self.config.name}-{split}-{id_}"
                yield id_, exple
        else:
            with open(filepath, "r", encoding="utf-8") as json_file:
                json_list = list(json_file)
                id_ = -1
                i = -1
                for json_str in json_list:
                    result = json.loads(json_str)
                    if split == "train":
                        i += 1
                        for sentence in result["sentence_annotations"]:
                            id_ += 1
                            response = {
                                "gem_id": f"{self.config.name}-{split}-{id_}",
                                "gem_parent_id": f"{self.config.name}-{split}-{id_}",
                                "totto_id": i,
                                "table_page_title": result["table_page_title"],
                                "table_webpage_url": result["table_webpage_url"],
                                "table_section_title": result["table_section_title"],
                                "table_section_text": result["table_section_text"],
                                "table": result["table"],
                                "highlighted_cells": result["highlighted_cells"],
                                "example_id": str(result["example_id"]),
                                "overlap_subset": "none",
                                "sentence_annotations": [sentence],
                                "references": [],
                                "target": sentence["final_sentence"],
                            }
                            yield id_, response
                    else:
                        id_ += 1
                        response = {
                            "gem_id": f"{self.config.name}-{split}-{id_}",
                            "gem_parent_id": f"{self.config.name}-{split}-{id_}",
                            "totto_id": id_,
                            "table_page_title": result["table_page_title"],
                            "table_webpage_url": result["table_webpage_url"],
                            "table_section_title": result["table_section_title"],
                            "table_section_text": result["table_section_text"],
                            "table": result["table"],
                            "highlighted_cells": result["highlighted_cells"],
                            "example_id": str(result["example_id"]),
                            "overlap_subset": str(result["overlap_subset"]),
                        }
                        response["sentence_annotations"] = [] if split == "test" else result["sentence_annotations"]
                        response["references"] = [
                            sentence["final_sentence"] for sentence in response["sentence_annotations"]
                        ]
                        response["target"] = response["references"][0] if len(response["references"]) > 0 else ""
                        yield id_, response