
from __future__ import absolute_import, division, print_function
import csv
import logging
import os
import sys
import numpy as np
from io import open
import pandas as pd
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score, accuracy_score

logger = logging.getLogger(__name__)


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
              #  if sys.version_info[0] == 2:
              #      line = list(unicode(cell, 'utf-8') for cell in line)
                lines.append(line)
            return lines

    def read_pandas_tsv(self, input_file):
        return pd.read_csv(input_file, sep = "\t", encoding = "utf8")
    
class genericProcessor(DataProcessor):

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
    
    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):            
            if i == 0:
                continue
            if len(line) < 2:
                continue
            
            guid = i
            text_a = line[1]
            label = line[0]            
            if len(text_a) > 5 and len(label.split(" ")) == 1:
                examples.append({"GUID":guid,"text_a":text_a,"text_b":text_a,"label":label})
        return pd.DataFrame(examples)


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def simple_accuracy(preds, labels):
    return accuracy_score(preds, labels) #(preds == labels).mean()


def acc_and_f1(preds, labels, average=None):
    if len(np.unique(labels)) > 2:
        average = "weighted"
    else:
        average = "binary"
    acc = simple_accuracy(preds, labels)
    f1 = f1_score(y_true=labels, y_pred=preds,average=average)
    return {
        "acc": acc,
        "f1": f1,
        "acc_and_f1": (acc + f1) / 2,
    }


def pearson_and_spearman(preds, labels):
    pearson_corr = pearsonr(preds, labels)[0]
    spearman_corr = spearmanr(preds, labels)[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }

def compute_metrics(task_name, preds, labels):
    assert len(preds) == len(labels)
    return acc_and_f1(preds, labels)
    
data_processors = {
    "bbc":genericProcessor,
    "insults":genericProcessor,
    "hatespeech":genericProcessor
}

task_names = ["bbc","insults","hatespeech"]

folder_names = task_names

dataset_folders = dict(zip(task_names,folder_names))
