from collections import defaultdict
from dataclasses import dataclass
from enum import Enum
import logging
from pathlib import Path
from types import SimpleNamespace
from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple

import datasets

if TYPE_CHECKING:
    import bioc

logger = logging.getLogger(__name__)


BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")


@dataclass
class BigBioConfig(datasets.BuilderConfig):
    """BuilderConfig for BigBio."""

    name: str = None
    version: datasets.Version = None
    description: str = None
    schema: str = None
    subset_id: str = None


class Tasks(Enum):
    NAMED_ENTITY_RECOGNITION = "NER"
    NAMED_ENTITY_DISAMBIGUATION = "NED"
    EVENT_EXTRACTION = "EE"
    RELATION_EXTRACTION = "RE"
    COREFERENCE_RESOLUTION = "COREF"
    QUESTION_ANSWERING = "QA"
    TEXTUAL_ENTAILMENT = "TE"
    SEMANTIC_SIMILARITY = "STS"
    TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
    PARAPHRASING = "PARA"
    TRANSLATION = "TRANSL"
    SUMMARIZATION = "SUM"
    TEXT_CLASSIFICATION = "TXTCLASS"


entailment_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "premise": datasets.Value("string"),
        "hypothesis": datasets.Value("string"),
        "label": datasets.Value("string"),
    }
)

pairs_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "text_1": datasets.Value("string"),
        "text_2": datasets.Value("string"),
        "label": datasets.Value("string"),
    }
)

qa_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "question_id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "question": datasets.Value("string"),
        "type": datasets.Value("string"),
        "choices": [datasets.Value("string")],
        "context": datasets.Value("string"),
        "answer": datasets.Sequence(datasets.Value("string")),
    }
)

text_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "text": datasets.Value("string"),
        "labels": [datasets.Value("string")],
    }
)

text2text_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "text_1": datasets.Value("string"),
        "text_2": datasets.Value("string"),
        "text_1_name": datasets.Value("string"),
        "text_2_name": datasets.Value("string"),
    }
)

kb_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "passages": [
            {
                "id": datasets.Value("string"),
                "type": datasets.Value("string"),
                "text": datasets.Sequence(datasets.Value("string")),
                "offsets": datasets.Sequence([datasets.Value("int32")]),
            }
        ],
        "entities": [
            {
                "id": datasets.Value("string"),
                "type": datasets.Value("string"),
                "text": datasets.Sequence(datasets.Value("string")),
                "offsets": datasets.Sequence([datasets.Value("int32")]),
                "normalized": [
                    {
                        "db_name": datasets.Value("string"),
                        "db_id": datasets.Value("string"),
                    }
                ],
            }
        ],
        "events": [
            {
                "id": datasets.Value("string"),
                "type": datasets.Value("string"),
                # refers to the text_bound_annotation of the trigger
                "trigger": {
                    "text": datasets.Sequence(datasets.Value("string")),
                    "offsets": datasets.Sequence([datasets.Value("int32")]),
                },
                "arguments": [
                    {
                        "role": datasets.Value("string"),
                        "ref_id": datasets.Value("string"),
                    }
                ],
            }
        ],
        "coreferences": [
            {
                "id": datasets.Value("string"),
                "entity_ids": datasets.Sequence(datasets.Value("string")),
            }
        ],
        "relations": [
            {
                "id": datasets.Value("string"),
                "type": datasets.Value("string"),
                "arg1_id": datasets.Value("string"),
                "arg2_id": datasets.Value("string"),
                "normalized": [
                    {
                        "db_name": datasets.Value("string"),
                        "db_id": datasets.Value("string"),
                    }
                ],
            }
        ],
    }
)


TASK_TO_SCHEMA = {
    Tasks.NAMED_ENTITY_RECOGNITION.name: "KB",
    Tasks.NAMED_ENTITY_DISAMBIGUATION.name: "KB",
    Tasks.EVENT_EXTRACTION.name: "KB",
    Tasks.RELATION_EXTRACTION.name: "KB",
    Tasks.COREFERENCE_RESOLUTION.name: "KB",
    Tasks.QUESTION_ANSWERING.name: "QA",
    Tasks.TEXTUAL_ENTAILMENT.name: "TE",
    Tasks.SEMANTIC_SIMILARITY.name: "PAIRS",
    Tasks.TEXT_PAIRS_CLASSIFICATION.name: "PAIRS",
    Tasks.PARAPHRASING.name: "T2T",
    Tasks.TRANSLATION.name: "T2T",
    Tasks.SUMMARIZATION.name: "T2T",
    Tasks.TEXT_CLASSIFICATION.name: "TEXT",
}

SCHEMA_TO_TASKS = defaultdict(set)
for task, schema in TASK_TO_SCHEMA.items():
    SCHEMA_TO_TASKS[schema].add(task)
SCHEMA_TO_TASKS = dict(SCHEMA_TO_TASKS)

VALID_TASKS = set(TASK_TO_SCHEMA.keys())
VALID_SCHEMAS = set(TASK_TO_SCHEMA.values())

SCHEMA_TO_FEATURES = {
    "KB": kb_features,
    "QA": qa_features,
    "TE": entailment_features,
    "T2T": text2text_features,
    "TEXT": text_features,
    "PAIRS": pairs_features,
}


def get_texts_and_offsets_from_bioc_ann(ann: "bioc.BioCAnnotation") -> Tuple:

    offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations]

    text = ann.text

    if len(offsets) > 1:
        i = 0
        texts = []
        for start, end in offsets:
            chunk_len = end - start
            texts.append(text[i : chunk_len + i])
            i += chunk_len
            while i < len(text) and text[i] == " ":
                i += 1
    else:
        texts = [text]

    return offsets, texts


def remove_prefix(a: str, prefix: str) -> str:
    if a.startswith(prefix):
        a = a[len(prefix) :]
    return a


def parse_brat_file(
    txt_file: Path,
    annotation_file_suffixes: List[str] = None,
    parse_notes: bool = False,
) -> Dict:
    """
    Parse a brat file into the schema defined below.
    `txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
    Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
    e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
    Will include annotator notes, when `parse_notes == True`.
    brat_features = datasets.Features(
        {
            "id": datasets.Value("string"),
            "document_id": datasets.Value("string"),
            "text": datasets.Value("string"),
            "text_bound_annotations": [  # T line in brat, e.g. type or event trigger
                {
                    "offsets": datasets.Sequence([datasets.Value("int32")]),
                    "text": datasets.Sequence(datasets.Value("string")),
                    "type": datasets.Value("string"),
                    "id": datasets.Value("string"),
                }
            ],
            "events": [  # E line in brat
                {
                    "trigger": datasets.Value(
                        "string"
                    ),  # refers to the text_bound_annotation of the trigger,
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "arguments": datasets.Sequence(
                        {
                            "role": datasets.Value("string"),
                            "ref_id": datasets.Value("string"),
                        }
                    ),
                }
            ],
            "relations": [  # R line in brat
                {
                    "id": datasets.Value("string"),
                    "head": {
                        "ref_id": datasets.Value("string"),
                        "role": datasets.Value("string"),
                    },
                    "tail": {
                        "ref_id": datasets.Value("string"),
                        "role": datasets.Value("string"),
                    },
                    "type": datasets.Value("string"),
                }
            ],
            "equivalences": [  # Equiv line in brat
                {
                    "id": datasets.Value("string"),
                    "ref_ids": datasets.Sequence(datasets.Value("string")),
                }
            ],
            "attributes": [  # M or A lines in brat
                {
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "ref_id": datasets.Value("string"),
                    "value": datasets.Value("string"),
                }
            ],
            "normalizations": [  # N lines in brat
                {
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "ref_id": datasets.Value("string"),
                    "resource_name": datasets.Value(
                        "string"
                    ),  # Name of the resource, e.g. "Wikipedia"
                    "cuid": datasets.Value(
                        "string"
                    ),  # ID in the resource, e.g. 534366
                    "text": datasets.Value(
                        "string"
                    ),  # Human readable description/name of the entity, e.g. "Barack Obama"
                }
            ],
            ### OPTIONAL: Only included when `parse_notes == True`
            "notes": [  # # lines in brat
                {
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "ref_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                }
            ],
        },
        )
    """

    example = {}
    example["document_id"] = txt_file.with_suffix("").name
    with txt_file.open() as f:
        example["text"] = f.read()

    # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
    # for event extraction
    if annotation_file_suffixes is None:
        annotation_file_suffixes = [".a1", ".a2", ".ann"]

    if len(annotation_file_suffixes) == 0:
        raise AssertionError(
            "At least one suffix for the to-be-read annotation files should be given!"
        )

    ann_lines = []
    for suffix in annotation_file_suffixes:
        annotation_file = txt_file.with_suffix(suffix)
        try:
            with annotation_file.open() as f:
                ann_lines.extend(f.readlines())
        except Exception:
            continue

    example["text_bound_annotations"] = []
    example["events"] = []
    example["relations"] = []
    example["equivalences"] = []
    example["attributes"] = []
    example["normalizations"] = []

    if parse_notes:
        example["notes"] = []

    for line in ann_lines:
        line = line.strip()
        if not line:
            continue

        if line.startswith("T"):  # Text bound
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["type"] = fields[1].split()[0]
            ann["offsets"] = []
            span_str = remove_prefix(fields[1], (ann["type"] + " "))
            text = fields[2]
            for span in span_str.split(";"):
                start, end = span.split()
                ann["offsets"].append([int(start), int(end)])

            # Heuristically split text of discontiguous entities into chunks
            ann["text"] = []
            if len(ann["offsets"]) > 1:
                i = 0
                for start, end in ann["offsets"]:
                    chunk_len = end - start
                    ann["text"].append(text[i : chunk_len + i])
                    i += chunk_len
                    while i < len(text) and text[i] == " ":
                        i += 1
            else:
                ann["text"] = [text]

            example["text_bound_annotations"].append(ann)

        elif line.startswith("E"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]

            ann["type"], ann["trigger"] = fields[1].split()[0].split(":")

            ann["arguments"] = []
            for role_ref_id in fields[1].split()[1:]:
                argument = {
                    "role": (role_ref_id.split(":"))[0],
                    "ref_id": (role_ref_id.split(":"))[1],
                }
                ann["arguments"].append(argument)

            example["events"].append(ann)

        elif line.startswith("R"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["type"] = fields[1].split()[0]

            ann["head"] = {
                "role": fields[1].split()[1].split(":")[0],
                "ref_id": fields[1].split()[1].split(":")[1],
            }
            ann["tail"] = {
                "role": fields[1].split()[2].split(":")[0],
                "ref_id": fields[1].split()[2].split(":")[1],
            }

            example["relations"].append(ann)

        # '*' seems to be the legacy way to mark equivalences,
        # but I couldn't find any info on the current way
        # this might have to be adapted dependent on the brat version
        # of the annotation
        elif line.startswith("*"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["ref_ids"] = fields[1].split()[1:]

            example["equivalences"].append(ann)

        elif line.startswith("A") or line.startswith("M"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]

            info = fields[1].split()
            ann["type"] = info[0]
            ann["ref_id"] = info[1]

            if len(info) > 2:
                ann["value"] = info[2]
            else:
                ann["value"] = ""

            example["attributes"].append(ann)

        elif line.startswith("N"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["text"] = fields[2]

            info = fields[1].split()

            ann["type"] = info[0]
            ann["ref_id"] = info[1]
            ann["resource_name"] = info[2].split(":")[0]
            ann["cuid"] = info[2].split(":")[1]
            example["normalizations"].append(ann)

        elif parse_notes and line.startswith("#"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL

            info = fields[1].split()

            ann["type"] = info[0]
            ann["ref_id"] = info[1]
            example["notes"].append(ann)

    return example


def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict:
    """
    Transform a brat parse (conforming to the standard brat schema) obtained with
    `parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py)
    :param brat_parse:
    """

    unified_example = {}

    # Prefix all ids with document id to ensure global uniqueness,
    # because brat ids are only unique within their document
    id_prefix = brat_parse["document_id"] + "_"

    # identical
    unified_example["document_id"] = brat_parse["document_id"]
    unified_example["passages"] = [
        {
            "id": id_prefix + "_text",
            "type": "abstract",
            "text": [brat_parse["text"]],
            "offsets": [[0, len(brat_parse["text"])]],
        }
    ]

    # get normalizations
    ref_id_to_normalizations = defaultdict(list)
    for normalization in brat_parse["normalizations"]:
        ref_id_to_normalizations[normalization["ref_id"]].append(
            {
                "db_name": normalization["resource_name"],
                "db_id": normalization["cuid"],
            }
        )

    # separate entities and event triggers
    unified_example["events"] = []
    non_event_ann = brat_parse["text_bound_annotations"].copy()
    for event in brat_parse["events"]:
        event = event.copy()
        event["id"] = id_prefix + event["id"]
        trigger = next(
            tr
            for tr in brat_parse["text_bound_annotations"]
            if tr["id"] == event["trigger"]
        )
        if trigger in non_event_ann:
            non_event_ann.remove(trigger)
        event["trigger"] = {
            "text": trigger["text"].copy(),
            "offsets": trigger["offsets"].copy(),
        }
        for argument in event["arguments"]:
            argument["ref_id"] = id_prefix + argument["ref_id"]

        unified_example["events"].append(event)

    unified_example["entities"] = []
    anno_ids = [ref_id["id"] for ref_id in non_event_ann]
    for ann in non_event_ann:
        entity_ann = ann.copy()
        entity_ann["id"] = id_prefix + entity_ann["id"]
        entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]]
        unified_example["entities"].append(entity_ann)

    # massage relations
    unified_example["relations"] = []
    skipped_relations = set()
    for ann in brat_parse["relations"]:
        if (
            ann["head"]["ref_id"] not in anno_ids
            or ann["tail"]["ref_id"] not in anno_ids
        ):
            skipped_relations.add(ann["id"])
            continue
        unified_example["relations"].append(
            {
                "arg1_id": id_prefix + ann["head"]["ref_id"],
                "arg2_id": id_prefix + ann["tail"]["ref_id"],
                "id": id_prefix + ann["id"],
                "type": ann["type"],
                "normalized": [],
            }
        )
    if len(skipped_relations) > 0:
        example_id = brat_parse["document_id"]
        logger.info(
            f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities."
            f" Skip (for now): "
            f"{list(skipped_relations)}"
        )

    # get coreferences
    unified_example["coreferences"] = []
    for i, ann in enumerate(brat_parse["equivalences"], start=1):
        is_entity_cluster = True
        for ref_id in ann["ref_ids"]:
            if not ref_id.startswith("T"):  # not textbound -> no entity
                is_entity_cluster = False
            elif ref_id not in anno_ids:  # event trigger -> no entity
                is_entity_cluster = False
        if is_entity_cluster:
            entity_ids = [id_prefix + i for i in ann["ref_ids"]]
            unified_example["coreferences"].append(
                {"id": id_prefix + str(i), "entity_ids": entity_ids}
            )
    return unified_example