# coding=utf-8
# Copyright 2022 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.
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#
#     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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import json
import os
from itertools import chain
from typing import Dict, List, Tuple

import datasets
from datasets import Value
import pandas as pd

from .bigbiohub import pairs_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['English']
_PUBMED = False
_LOCAL = False
_CITATION = """\
@article{wadden2020fact,
  author    = {David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
  title     = {Fact or Fiction: Verifying Scientific Claims},
  year      = {2020},
  address   = {Online},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2020.emnlp-main.609},
  doi       = {10.18653/v1/2020.emnlp-main.609},
  pages     = {7534--7550},
  biburl    = {},
  bibsource = {}
}
"""

_DATASETNAME = "scifact"
_DISPLAYNAME = "SciFact"


_DESCRIPTION_BASE = """\
    SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
    """

_SOURCE_CORPUS_DESCRIPTION = f"""\
    {_DESCRIPTION_BASE} This config has abstracts and document ids.
    """

_SOURCE_CLAIMS_DESCRIPTION = """\
    {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids.
    """

_BIGBIO_PAIRS_RATIONALE_DESCRIPTION = """\
    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("rationale", "not_rationale") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper."
    """

_BIGBIO_PAIRS_LABELPREDICTION_DESCRIPTION = """\
    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("SUPPORT", "NOINFO", "CONTRADICT") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper.
    """

_DESCRIPTION = {
    "scifact_corpus_source": _SOURCE_CORPUS_DESCRIPTION,
    "scifact_claims_source": _SOURCE_CLAIMS_DESCRIPTION,
    "scifact_rationale_bigbio_pairs": _BIGBIO_PAIRS_RATIONALE_DESCRIPTION,
    "scifact_labelprediction_bigbio_pairs": _BIGBIO_PAIRS_LABELPREDICTION_DESCRIPTION,
}

_HOMEPAGE = "https://scifact.apps.allenai.org/"


_LICENSE = 'Creative Commons Attribution Non Commercial 2.0 Generic'

_URLS = {
    _DATASETNAME: "https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz",
}

_SUPPORTED_TASKS = [Tasks.TEXT_PAIRS_CLASSIFICATION]

_SOURCE_VERSION = "1.0.0"

_BIGBIO_VERSION = "1.0.0"


class SciFact(datasets.GeneratorBasedBuilder):
    """
    SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
    """

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="scifact_corpus_source",
            version=SOURCE_VERSION,
            description="scifact source schema for the corpus config",
            schema="source",
            subset_id="scifact_corpus_source",
        ),
        BigBioConfig(
            name="scifact_claims_source",
            version=SOURCE_VERSION,
            description="scifact source schema for the claims config",
            schema="source",
            subset_id="scifact_claims_source",
        ),
        BigBioConfig(
            name="scifact_rationale_bigbio_pairs",
            version=BIGBIO_VERSION,
            description="scifact BigBio text pairs classification schema for rationale task",
            schema="bigbio_pairs",
            subset_id="scifact_rationale_pairs",
        ),
        BigBioConfig(
            name="scifact_labelprediction_bigbio_pairs",
            version=BIGBIO_VERSION,
            description="scifact BigBio text pairs classification schema for label prediction task",
            schema="bigbio_pairs",
            subset_id="scifact_labelprediction_pairs",
        ),
    ]

    DEFAULT_CONFIG_NAME = "scifact_claims_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            # modified from
            # https://huggingface.co/datasets/scifact/blob/main/scifact.py#L50

            if self.config.name == "scifact_corpus_source":
                features = datasets.Features(
                    {
                        "doc_id": Value("int32"),      # The document's S2ORC ID.
                        "title": Value("string"),      # The title.
                        "abstract": [Value("string")], # The abstract, written as a list of sentences.
                        "structured": Value("bool"),   # Indicator for whether this is a structured abstract.
                    }
                )

            elif self.config.name == "scifact_claims_source":
                features = datasets.Features(
                    {
                        "id": Value("int32"),  # An integer claim ID.
                        "claim": Value("string"),  # The text of the claim.
                        "evidences": [
                            {
                                "doc_id": Value("int32"),         # source doc_id for evidence
                                "sentence_ids": [Value("int32")], # sentence ids from doc_id
                                "label": Value("string"),         # SUPPORT or CONTRADICT
                            },
                        ],
                        "cited_doc_ids": [Value("int32")],   # The claim's "cited documents".
                    }
                )

            else:
                raise NotImplementedError(
                    f"{self.config.name} config not implemented"
                )

        elif self.config.schema == "bigbio_pairs":
            features = pairs_features

        else:
            raise NotImplementedError(f"{self.config.schema} schema not implemented")

        return datasets.DatasetInfo(
            description=_DESCRIPTION[self.config.name],
            features=features,
            homepage=_HOMEPAGE,
            license=str(_LICENSE),
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
        urls = _URLS[_DATASETNAME]
        self.config.data_dir = dl_manager.download_and_extract(urls)

        if self.config.name == "scifact_corpus_source":
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": os.path.join(
                            self.config.data_dir, "data", "corpus.jsonl"
                        ),
                        "split": "train",
                    },
                ),
            ]

        # the test split is only returned in source schema
        # this is b/c it only has claims with no cited docs or evidence
        # the bigbio implementation of this dataset requires
        # cited docs or evidence to construct samples
        elif self.config.name == "scifact_claims_source":
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": os.path.join(
                            self.config.data_dir, "data", "claims_train.jsonl"
                        ),
                        "split": "train",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "filepath": os.path.join(
                            self.config.data_dir, "data", "claims_dev.jsonl"
                        ),
                        "split": "dev",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "filepath": os.path.join(
                            self.config.data_dir, "data", "claims_test.jsonl"
                        ),
                        "split": "test",
                    },
                ),
            ]

        elif self.config.name in [
            "scifact_rationale_bigbio_pairs",
            "scifact_labelprediction_bigbio_pairs",
        ]:
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": os.path.join(
                            self.config.data_dir, "data", "claims_train.jsonl"
                        ),
                        "split": "train",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "filepath": os.path.join(
                            self.config.data_dir, "data", "claims_dev.jsonl"
                        ),
                        "split": "dev",
                    },
                ),
            ]


    def _source_generate_examples(self, filepath, split) -> Tuple[str, Dict[str, str]]:

        # here we just read corpus.jsonl and return the abstracts
        if self.config.name == "scifact_corpus_source":
            with open(filepath) as fp:
                for id_, row in enumerate(fp.readlines()):
                    data = json.loads(row)
                    yield id_, {
                        "doc_id": int(data["doc_id"]),
                        "title": data["title"],
                        "abstract": data["abstract"],
                        "structured": data["structured"],
                    }

        # here we are reading one of claims_(train|dev|test).jsonl
        elif self.config.name == "scifact_claims_source":

            # claims_test.jsonl only has "id" and "claim" keys
            # claims_train.jsonl and claims_dev.jsonl sometimes have evidence
            with open(filepath) as fp:
                for id_, row in enumerate(fp.readlines()):
                    data = json.loads(row)
                    evidences_dict = data.get("evidence", {})
                    evidences_list = []
                    for doc_id, sent_lbl_list in evidences_dict.items():
                        for sent_lbl_dict in sent_lbl_list:
                            evidence = {
                                "doc_id": doc_id,
                                "sentence_ids": sent_lbl_dict["sentences"],
                                "label": sent_lbl_dict["label"],
                            }
                            evidences_list.append(evidence)

                    yield id_, {
                        "id": data["id"],
                        "claim": data["claim"],
                        "evidences": evidences_list,
                        "cited_doc_ids": data.get("cited_doc_ids", []),
                    }


    def _bigbio_generate_examples(self, filepath, split) -> Tuple[str, Dict[str, str]]:
        """
        Here we always create one sample per sentence group.
        Any sentence group in an evidence attribute will have
        a label in {"rationale"} for the rationale task or
        in {"SUPPORT", "CONTRADICT"} for the labelprediction task.
        All other sentences will have either a "not_rationale"
        label or a "NOINFO" label depending on the task.
        """

        # read corpus (one row per abstract)
        corpus_file_path = os.path.join(self.config.data_dir, "data", "corpus.jsonl")
        df_corpus = pd.read_json(corpus_file_path, lines=True)

        # create one row per sentence and create sentence index
        df_sents = df_corpus.explode('abstract')
        df_sents = df_sents.rename(columns={"abstract": "sentence"})
        df_sents['sent_num'] = df_sents.groupby('doc_id').transform('cumcount')
        df_sents['doc_sent_id'] = df_sents.apply(lambda x: f"{x['doc_id']}-{x['sent_num']}", axis=1)

        # read claims
        df_claims = pd.read_json(filepath, lines=True)


        # join claims to corpus
        for _, claim_row in df_claims.iterrows():

            evidence = claim_row['evidence']
            cited_doc_ids = set(claim_row['cited_doc_ids'])
            evidence_doc_ids = set([int(doc_id) for doc_id in evidence.keys()])

            # assert all evidence doc IDs are in cited_doc_ids
            assert len(evidence_doc_ids - cited_doc_ids) == 0

            # this will have all abstract sentences from cited docs
            df_claim_sents = df_sents[df_sents['doc_id'].isin(cited_doc_ids)]

            # create all sentence samples as NOINFO then fix
            noinfo_samples = {}
            for _, row in df_claim_sents.iterrows():
                sample = {
                    "claim": claim_row["claim"],
                    "claim_id": claim_row["id"],
                    "doc_id": row['doc_id'],
                    "sentence_ids": (row['sent_num'],),
                    "doc_sent_ids": (row['doc_sent_id'],),
                    "span": row['sentence'].strip(),
                    "label": "NOINFO",
                }
                noinfo_samples[sample["doc_sent_ids"]] = sample

            # create evidence samples and remove from noinfo samples as we go
            evidence_samples = []
            for doc_id_str, sent_lbl_list in evidence.items():
                doc_id = int(doc_id_str)

                for sent_lbl_dict in sent_lbl_list:
                    sent_ids = sent_lbl_dict['sentences']
                    doc_sent_ids = [f"{doc_id}-{sent_id}" for sent_id in sent_ids]
                    df_evi = df_claim_sents[df_claim_sents['doc_sent_id'].isin(doc_sent_ids)]

                    sample = {
                        "claim": claim_row["claim"],
                        "claim_id": claim_row["id"],
                        "doc_id": doc_id,
                        "sentence_ids": tuple(sent_ids),
                        "doc_sent_ids": tuple(doc_sent_ids),
                        "span": " ".join([el.strip() for el in df_evi["sentence"].values]),
                        "label": sent_lbl_dict["label"],
                    }
                    evidence_samples.append(sample)
                    for doc_sent_id in doc_sent_ids:
                        del noinfo_samples[(doc_sent_id,)]

            # combine all sample and put back in sentence order
            all_samples = evidence_samples + list(noinfo_samples.values())
            all_samples = sorted(all_samples, key=lambda x: (x['doc_id'], x['sentence_ids'][0]))

            # add a unique ID
            for _id, sample in enumerate(all_samples):
                sample["id"] = f"{_id}-{sample['claim_id']}-{sample['doc_id']}-{sample['sentence_ids'][0]}"

            RATIONALE_LABEL_MAP = {
                "SUPPORT": "rationale",
                "CONTRADICT": "rationale",
                "NOINFO": "not_rationale",
            }

            if self.config.name == "scifact_rationale_bigbio_pairs":
                for sample in all_samples:
                    yield sample['id'], {
                        "id": sample["id"],
                        "document_id": sample["doc_id"],
                        "text_1": sample["claim"],
                        "text_2": sample["span"],
                        "label": RATIONALE_LABEL_MAP[sample['label']],
                    }

            elif self.config.name == "scifact_labelprediction_bigbio_pairs":
                for sample in all_samples:
                    yield sample['id'], {
                        "id": sample["id"],
                        "document_id": sample["doc_id"],
                        "text_1": sample["claim"],
                        "text_2": sample["span"],
                        "label": sample['label'],
                    }

    def _generate_examples(self, filepath, split) -> Tuple[int, dict]:

        if "source" in self.config.name:
            for sample in self._source_generate_examples(filepath, split):
                yield sample

        elif "bigbio" in self.config.name:
            for sample in self._bigbio_generate_examples(filepath, split):
                yield sample