import os

import time

import random

from enum import Enum

from dataclasses import dataclass, field

from typing import List, Optional, Union, Dict, Tuple

import torch

from torch.utils.data.dataset import Dataset

from filelock import FileLock

import logging

from transformers import (InputFeatures,

                          InputExample,

                          PreTrainedTokenizerBase)

import pandas as pd

from sklearn.metrics import precision_recall_fscore_support

import sys

sys.path.append("../")

sys.path.append('./biobert_re/')

from data_processor import glue_convert_examples_to_features, glue_output_modes, glue_processors

import utils

from ehr import HealthRecord

from annotations import Relation

logger = logging.getLogger(__name__)

@dataclass

class GlueDataTrainingArguments:

    """

    Arguments pertaining to what data we are going to input our model for training and eval.

    Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command

    line.

    """

    task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys())})

    data_dir: str = field(

        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}

    )

    max_seq_length: int = field(

        default=128,

        metadata={

            "help": "The maximum total input sequence length after tokenization. Sequences longer "

            "than this will be truncated, sequences shorter will be padded."

        },

    )

    overwrite_cache: bool = field(

        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}

    )

    def __post_init__(self):

        self.task_name = self.task_name.lower()

class Split(Enum):

    train = "train"

    dev = "dev"

    test = "test"

# noinspection PyTypeChecker

class REDataset(Dataset):

    """

    A class representing a training dataset for Relation Extraction.

    """

    args: GlueDataTrainingArguments

    output_mode: str

    features: List[InputFeatures]

    def __init__(

        self,

        args: GlueDataTrainingArguments,

        tokenizer: PreTrainedTokenizerBase,

        limit_length: Optional[int] = None,

        mode: Union[str, Split] = Split.train,

        cache_dir: Optional[str] = None,

    ):

        self.args = args

        self.processor = glue_processors[args.task_name]()

        self.output_mode = glue_output_modes[args.task_name]

        if isinstance(mode, str):

            try:

                mode = Split[mode]

            except KeyError:

                raise KeyError("mode is not a valid split name")

        # Load data features from cache or dataset file

        cached_features_file = os.path.join(

            cache_dir if cache_dir is not None else args.data_dir,

            "cached_{}_{}_{}_{}".format(

                mode.value,

                tokenizer.__class__.__name__,

                str(args.max_seq_length),

                args.task_name,

            ),

        )

        label_list = self.processor.get_labels()

        self.label_list = label_list

        # Make sure only the first process in distributed training processes the dataset,

        # and the others will use the cache.

        lock_path = cached_features_file + ".lock"

        with FileLock(lock_path):

            if os.path.exists(cached_features_file) and not args.overwrite_cache:

                start = time.time()

                self.features = torch.load(cached_features_file)

                logger.info(f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start)

            else:

                logger.info(f"Creating features from dataset file at {args.data_dir}")

                if mode == Split.dev:

                    examples = self.processor.get_dev_examples(args.data_dir)

                elif mode == Split.test:

                    examples = self.processor.get_test_examples(args.data_dir)

                else:

                    examples = self.processor.get_train_examples(args.data_dir)

                if limit_length is not None:

                    examples = examples[:limit_length]

                self.features = glue_convert_examples_to_features(

                    examples,

                    tokenizer,

                    max_length=args.max_seq_length,

                    label_list=label_list,

                    output_mode=self.output_mode,

                )

                start = time.time()

                torch.save(self.features, cached_features_file)

                logger.info("Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start)

    def __len__(self):

        return len(self.features)

    def __getitem__(self, i) -> InputFeatures:

        return self.features[i]

    def get_labels(self):

        return self.label_list

class RETestDataset(Dataset):

    """

    A class representing a test Dataset for relation extraction.

    """

    def __init__(self, test_ehr, tokenizer, max_seq_len, label_list):

        self.re_text_list, self.relation_list = generate_re_test_file(test_ehr)

        if not self.re_text_list:

            self.features = []

        else:

            examples = []

            for (i, text) in enumerate(self.re_text_list):

                guid = "%s" % i

                examples.append(InputExample(guid=guid, text_a=text, text_b=None, label=None))

            self.features = glue_convert_examples_to_features(examples, tokenizer,

                                                              max_length=max_seq_len,

                                                              label_list=label_list)

    def __len__(self):

        return len(self.features)

    def __getitem__(self, i) -> InputFeatures:

        return self.features[i]

def replace_ent_label(text, ent_type, start_idx, end_idx):

    label = '@'+ent_type+'$'

    return text[:start_idx]+label+text[end_idx:]

def write_file(file, index, sentence, label, sep, is_test, is_label):

    if is_test and is_label:  # test_original - test with labels

        file.write('{}{}{}{}{}'.format(index, sep, sentence, sep, label))

    elif is_test and not is_label:  # test - test with no labels

        file.write('{}{}{}'.format(index, sep, sentence))

    else:  # train

        file.write('{}{}{}'.format(sentence, sep, label))

    file.write('\n')

def get_char_split_points(record, max_len):

    char_split_points = []

    split_points = record.get_split_points(max_len=max_len)

    for pt in split_points[:-1]:

        char_split_points.append(record.get_char_idx(pt)[1])

    if len(char_split_points) == 1:

        return char_split_points

    else:

        return char_split_points[1:]

def replace_entity_text(split_text, ent1, ent2, split_offset):

    # Remove split offset

    ent1_start = ent1.range[0] - split_offset

    ent1_end = ent1.range[1] - split_offset

    ent2_start = ent2.range[0] - split_offset

    ent2_end = ent2.range[1] - split_offset

    # If entity 1 is present before entity 2

    if ent1_end < ent2_end:

        # Insert entity 2 and then entity 1

        modified_text = replace_ent_label(split_text, ent2.name, ent2_start, ent2_end)

        modified_text = replace_ent_label(modified_text, ent1.name, ent1_start, ent1_end)

    # If entity 1 is present after entity 2

    else:

        # Insert entity 1 and then entity 2

        modified_text = replace_ent_label(split_text, ent1.name, ent1_start, ent1_end)

        modified_text = replace_ent_label(modified_text, ent2.name, ent2_start, ent2_end)

    return modified_text

def generate_re_input_files(ehr_records: List[HealthRecord], filename: str,

                            ade_records: List[Dict] = None, max_len: int = 128,

                            is_test=False, is_label=True, is_predict=False, sep: str = '\t'):

    random.seed(0)

    index = 0

    index_rel_label_map = []

    with open(filename, 'w') as file:

        # Write headers

        write_file(file, 'index', 'sentence', 'label', sep, is_test, is_label)

        # Preprocess EHR records

        for record in ehr_records:

            text = record.text

            entities = record.get_entities()

            if is_predict:

                true_relations = None

            else:

                true_relations = record.get_relations()

            # get character split points

            char_split_points = get_char_split_points(record, max_len)

            start = 0

            end = char_split_points[0]

            for i in range(len(char_split_points)):

                # Obtain only entities within the split text

                range_entities = {ent_id: ent for ent_id, ent in

                                  filter(lambda item: int(item[1][0]) >= start and int(item[1][1]) <= end,

                                         entities.items())}

                # Get all possible relations within the split text

                possible_relations = utils.map_entities(range_entities, true_relations)

                for rel, label in possible_relations:

                    if label == 0 and rel.name != "ADE-Drug":

                        if random.random() > 0.25:

                            continue

                    split_text = text[start:end]

                    split_offset = start

                    ent1 = rel.get_entities()[0]

                    ent2 = rel.get_entities()[1]

                    # Check if both entities are within split text

                    if ent1.range[0] >= start and ent1.range[1] < end and \

                            ent2.range[0] >= start and ent2.range[1] < end:

                        modified_text = replace_entity_text(split_text, ent1, ent2, split_offset)

                        # Replace un-required characters with space

                        final_text = modified_text.replace('\n', ' ').replace('\t', ' ')

                        write_file(file, index, final_text, label, sep, is_test, is_label)

                        if is_predict:

                            index_rel_label_map.append({'relation': rel})

                        else:

                            index_rel_label_map.append({'label': label, 'relation': rel})

                        index += 1

                start = end

                if i != len(char_split_points)-1:

                    end = char_split_points[i+1]

                else:

                    end = len(text)+1

        # Preprocess ADE records

        if ade_records is not None:

            for record in ade_records:

                entities = record['entities']

                true_relations = record['relations']

                possible_relations = utils.map_entities(entities, true_relations)

                for rel, label in possible_relations:

                    if label == 1 and random.random() > 0.5:

                        continue

                    new_tokens = record['tokens'].copy()

                    for ent in rel.get_entities():

                        ent_type = ent.name

                        start_tok = ent.range[0]

                        end_tok = ent.range[1]+1

                        for i in range(start_tok, end_tok):

                            new_tokens[i] = '@'+ent_type+'$'

                    """Remove consecutive repeating entities.

                    Eg. this is @ADE$ @ADE$ @ADE$ for @Drug$ @Drug$ -> this is @ADE$ for @Drug$"""

                    final_tokens = [new_tokens[i] for i in range(len(new_tokens))\

                                    if (i == 0) or new_tokens[i] != new_tokens[i-1]]

                    final_text = " ".join(final_tokens)

                    write_file(file, index, final_text, label, sep, is_test, is_label)

                    index_rel_label_map.append({'label': label, 'relation': rel})

                    index += 1

    filename, ext = filename.split('.')

    utils.save_pickle(filename+'_rel.pkl', index_rel_label_map)

def get_eval_results(answer_path, output_path):

    """

    Get evaluation metrics for predictions

    Parameters

    ------------

    answer_path : test.tsv file. Tab-separated.

                  One example per a line. True labels at the 3rd column.

    output_path : test_predictions.txt. Model generated predictions.

    """

    testdf = pd.read_csv(answer_path, sep="\t", index_col=0)

    preddf = pd.read_csv(output_path, sep="\t", header=None)

    pred = [preddf.iloc[i].tolist() for i in preddf.index]

    pred_class = [int(v[1]) for v in pred[1:]]

    p, r, f, s = precision_recall_fscore_support(y_pred=pred_class, y_true=testdf["label"])

    results = dict()

    results["f1 score"] = f[1]

    results["recall"] = r[1]

    results["precision"] = p[1]

    results["specificity"] = r[0]

    return results

def generate_re_test_file(ehr_record: HealthRecord,

                          max_len: int = 128) -> Tuple[List[str], List[Relation]]:

    """

    Generates test file for Relation Extraction.

    Parameters

    -----------

    ehr_record : HealthRecord

        The EHR record with entities set.

    max_len : int

        The maximum length of sequence.

    Returns

    --------

    Tuple[List[str], List[Relation]]

        List of sequences with entity replaced by it's tag.

        And a list of relation objects representing relation in those sequences.

    """

    random.seed(0)

    re_text_list = []

    relation_list = []

    text = ehr_record.text

    entities = ehr_record.get_entities()

    if isinstance(entities, dict):

        entities = list(entities.values())

    # get character split points

    char_split_points = get_char_split_points(ehr_record, max_len)

    start = 0

    end = char_split_points[0]

    for i in range(len(char_split_points)):

        # Obtain only entities within the split text

        range_entities = [ent for ent in filter(lambda item: int(item[0]) >= start and int(item[1]) <= end,

                                                entities)]

        # Get all possible relations within the split text

        possible_relations = utils.map_entities(range_entities)

        for rel, label in possible_relations:

            split_text = text[start:end]

            split_offset = start

            ent1 = rel.get_entities()[0]

            ent2 = rel.get_entities()[1]

            # Check if both entities are within split text

            if ent1[0] >= start and ent1[1] < end and \

                    ent2[0] >= start and ent2[1] < end:

                modified_text = replace_entity_text(split_text, ent1, ent2, split_offset)

                # Replace un-required characters with space

                final_text = modified_text.replace('\n', ' ').replace('\t', ' ')

                re_text_list.append(final_text)

                relation_list.append(rel)

        start = end

        if i != len(char_split_points)-1:

            end = char_split_points[i+1]

        else:

            end = len(text)+1

    assert len(re_text_list) == len(relation_list)

    return re_text_list, relation_list