"""Metrics to assess performance on sequence labeling task given prediction
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from collections import defaultdict

import numpy as np
import json


def get_entities(seq, suffix=False):
    """Gets entities from sequence.

    Args:
        seq (list): sequence of labels.

    Returns:
        list: list of (chunk_type, chunk_start, chunk_end).

    Example:
        >>> from seqeval.metrics.sequence_labeling import get_entities
        >>> seq = ['B-PER', 'I-PER', 'O', 'B-LOC']
        >>> get_entities(seq)
        [('PER', 0, 1), ('LOC', 3, 3)]
    """
    # for nested list
    if any(isinstance(s, list) for s in seq):
        seq = [item for sublist in seq for item in sublist + ['O']]

    prev_tag = 'O'
    prev_type = ''
    begin_offset = 0
    chunks = []
    for i, chunk in enumerate(seq + ['O']):
        if suffix:
            tag = chunk[-1]
            type_ = chunk.split('-')[0]
        else:
            tag = chunk[0]
            type_ = chunk.split('-')[-1]

        if end_of_chunk(prev_tag, tag, prev_type, type_):
            chunks.append((prev_type, begin_offset, i-1))
        if start_of_chunk(prev_tag, tag, prev_type, type_):
            begin_offset = i
        prev_tag = tag
        prev_type = type_

    return chunks


def end_of_chunk(prev_tag, tag, prev_type, type_):
    """Checks if a chunk ended between the previous and current word.

    Args:
        prev_tag: previous chunk tag.
        tag: current chunk tag.
        prev_type: previous type.
        type_: current type.

    Returns:
        chunk_end: boolean.
    """
    chunk_end = False

    if prev_tag == 'E': chunk_end = True
    if prev_tag == 'S': chunk_end = True

    if prev_tag == 'B' and tag == 'B': chunk_end = True
    if prev_tag == 'B' and tag == 'S': chunk_end = True
    if prev_tag == 'B' and tag == 'O': chunk_end = True
    if prev_tag == 'I' and tag == 'B': chunk_end = True
    if prev_tag == 'I' and tag == 'S': chunk_end = True
    if prev_tag == 'I' and tag == 'O': chunk_end = True

    if prev_tag != 'O' and prev_tag != '.' and prev_type != type_:
        chunk_end = True

    return chunk_end


def start_of_chunk(prev_tag, tag, prev_type, type_):
    """Checks if a chunk started between the previous and current word.

    Args:
        prev_tag: previous chunk tag.
        tag: current chunk tag.
        prev_type: previous type.
        type_: current type.

    Returns:
        chunk_start: boolean.
    """
    chunk_start = False

    if tag == 'B': chunk_start = True
    if tag == 'S': chunk_start = True

    if prev_tag == 'E' and tag == 'E': chunk_start = True
    if prev_tag == 'E' and tag == 'I': chunk_start = True
    if prev_tag == 'S' and tag == 'E': chunk_start = True
    if prev_tag == 'S' and tag == 'I': chunk_start = True
    if prev_tag == 'O' and tag == 'E': chunk_start = True
    if prev_tag == 'O' and tag == 'I': chunk_start = True

    if tag != 'O' and tag != '.' and prev_type != type_:
        chunk_start = True

    return chunk_start


def f1_score(y_true, y_pred, average='micro', suffix=False, criteria = 'exact'):
    """Compute the F1 score.

    The F1 score can be interpreted as a weighted average of the precision and
    recall, where an F1 score reaches its best value at 1 and worst score at 0.
    The relative contribution of precision and recall to the F1 score are
    equal. The formula for the F1 score is::

        F1 = 2 * (precision * recall) / (precision + recall)

    Args:
        y_true : 2d array. Ground truth (correct) target values.
        y_pred : 2d array. Estimated targets as returned by a tagger.

    Returns:
        score : float.

    Example:
        >>> from seqeval.metrics import f1_score
        >>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> f1_score(y_true, y_pred)
        0.50
    """
    true_entities = set(get_entities(y_true, suffix))
    pred_entities = set(get_entities(y_pred, suffix))

    # nb_correct = len(true_entities & pred_entities)
    nb_correct = process_entities_micro(true_entities, pred_entities, criteria = criteria)
    nb_pred = len(pred_entities)
    nb_true = len(true_entities)

    p = nb_correct / nb_pred if nb_pred > 0 else 0
    r = nb_correct / nb_true if nb_true > 0 else 0
    score = 2 * p * r / (p + r) if p + r > 0 else 0

    return score


def accuracy_score(y_true, y_pred):
    """Accuracy classification score.

    In multilabel classification, this function computes subset accuracy:
    the set of labels predicted for a sample must *exactly* match the
    corresponding set of labels in y_true.

    Args:
        y_true : 2d array. Ground truth (correct) target values.
        y_pred : 2d array. Estimated targets as returned by a tagger.

    Returns:
        score : float.

    Example:
        >>> from seqeval.metrics import accuracy_score
        >>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> accuracy_score(y_true, y_pred)
        0.80
    """
    if any(isinstance(s, list) for s in y_true):
        y_true = [item for sublist in y_true for item in sublist]
        y_pred = [item for sublist in y_pred for item in sublist]

    nb_correct = sum(y_t==y_p for y_t, y_p in zip(y_true, y_pred))
    nb_true = len(y_true)

    score = nb_correct / nb_true

    return score


def precision_score(y_true, y_pred, average='micro', suffix=False, criteria = 'exact'):
    """Compute the precision.

    The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
    true positives and ``fp`` the number of false positives. The precision is
    intuitively the ability of the classifier not to label as positive a sample.

    The best value is 1 and the worst value is 0.

    Args:
        y_true : 2d array. Ground truth (correct) target values.
        y_pred : 2d array. Estimated targets as returned by a tagger.

    Returns:
        score : float.

    Example:
        >>> from seqeval.metrics import precision_score
        >>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> precision_score(y_true, y_pred)
        0.50
    """
    true_entities = set(get_entities(y_true, suffix))
    pred_entities = set(get_entities(y_pred, suffix))

    # nb_correct = len(true_entities & pred_entities)
    nb_correct = process_entities_micro(true_entities, pred_entities, criteria = criteria)
    nb_pred = len(pred_entities)

    score = nb_correct / nb_pred if nb_pred > 0 else 0

    return score


def recall_score(y_true, y_pred, average='micro', suffix=False, criteria = 'exact'):
    """Compute the recall.

    The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
    true positives and ``fn`` the number of false negatives. The recall is
    intuitively the ability of the classifier to find all the positive samples.

    The best value is 1 and the worst value is 0.

    Args:
        y_true : 2d array. Ground truth (correct) target values.
        y_pred : 2d array. Estimated targets as returned by a tagger.

    Returns:
        score : float.

    Example:
        >>> from seqeval.metrics import recall_score
        >>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> recall_score(y_true, y_pred)
        0.50
    """
    true_entities = set(get_entities(y_true, suffix))
    pred_entities = set(get_entities(y_pred, suffix))

    # nb_correct = len(true_entities & pred_entities)
    nb_correct = process_entities_micro(true_entities, pred_entities, criteria = criteria)
    nb_true = len(true_entities)

    score = nb_correct / nb_true if nb_true > 0 else 0

    return score


def performance_measure(y_true, y_pred):
    """
    Compute the performance metrics: TP, FP, FN, TN

    Args:
        y_true : 2d array. Ground truth (correct) target values.
        y_pred : 2d array. Estimated targets as returned by a tagger.

    Returns:
        performance_dict : dict

    Example:
        >>> from seqeval.metrics import performance_measure
        >>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'O', 'B-ORG'], ['B-PER', 'I-PER', 'O']]
        >>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> performance_measure(y_true, y_pred)
        (3, 3, 1, 4)
    """
    performace_dict = dict()
    if any(isinstance(s, list) for s in y_true):
        y_true = [item for sublist in y_true for item in sublist]
        y_pred = [item for sublist in y_pred for item in sublist]
    performace_dict['TP'] = sum(y_t == y_p for y_t, y_p in zip(y_true, y_pred)
                                if ((y_t != 'O') or (y_p != 'O')))
    performace_dict['FP'] = sum(y_t != y_p for y_t, y_p in zip(y_true, y_pred))
    performace_dict['FN'] = sum(((y_t != 'O') and (y_p == 'O'))
                                for y_t, y_p in zip(y_true, y_pred))
    performace_dict['TN'] = sum((y_t == y_p == 'O')
                                for y_t, y_p in zip(y_true, y_pred))

    return performace_dict

def process_entities_macro(true_entities, pred_entities, criteria = 'exact'):

    if criteria not in ['exact', 'relaxed']:
        err_msg = ("Expected criteria to either 'exact' or 'relaxed'. Got: {}").format(criteria)
        raise ValueError(err_msg)
    if criteria == 'exact':
        nb_correct = len(true_entities & pred_entities)
    else:
        nb_correct = 0
        for t_e in true_entities:
            for p_e in pred_entities:
                t_indices = set(range(t_e[0],t_e[1]+1))
                p_indices = set(range(p_e[0],p_e[1]+1))
                if bool(t_indices.intersection(p_indices)):
                    nb_correct += 1
                    break

    return nb_correct

def process_entities_micro(true_entities, pred_entities, criteria = 'exact'):

    if criteria not in ['exact', 'relaxed']:
        err_msg = ("Expected criteria to either 'exact' or 'relaxed'. Got: {}").format(criteria)
        raise ValueError(err_msg)
    if criteria == 'exact':
        nb_correct = len(true_entities & pred_entities)
    else:
        nb_correct = 0
        for t_e in true_entities:
            for p_e in pred_entities:
                if t_e[0]==p_e[0]:
                    t_indices = set(range(t_e[1],t_e[2]+1))
                    p_indices = set(range(p_e[1],p_e[2]+1))
                    if bool(t_indices.intersection(p_indices)):
                        nb_correct += 1
                        break

    return nb_correct

def classification_report(y_true, y_pred, digits=2, suffix=False, criteria = 'exact'):
    """Build a text report showing the main classification metrics.

    Args:
        y_true : 2d array. Ground truth (correct) target values.
        y_pred : 2d array. Estimated targets as returned by a classifier.
        digits : int. Number of digits for formatting output floating point values.

    Returns:
        report : string. Text summary of the precision, recall, F1 score for each class.

    Examples:
        >>> from seqeval.metrics import classification_report
        >>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
        >>> print(classification_report(y_true, y_pred))
                     precision    recall  f1-score   support
        <BLANKLINE>
               MISC       0.00      0.00      0.00         1
                PER       1.00      1.00      1.00         1
        <BLANKLINE>
          micro avg       0.50      0.50      0.50         2
          macro avg       0.50      0.50      0.50         2
        <BLANKLINE>
    """
    true_entities = set(get_entities(y_true, suffix))
    pred_entities = set(get_entities(y_pred, suffix))

    name_width = 0
    d1 = defaultdict(set)
    d2 = defaultdict(set)
    for e in true_entities:
        d1[e[0]].add((e[1], e[2]))
        name_width = max(name_width, len(e[0]))
    for e in pred_entities:
        d2[e[0]].add((e[1], e[2]))

    last_line_heading = 'weighted avg'
    width = max(name_width, len(last_line_heading), digits)

    headers = ["precision", "recall", "f1-score", "support", "predicted"]
    head_fmt = u'{:>{width}s} ' + u' {:>9}' * len(headers)
    report = head_fmt.format(u'', *headers, width=width)
    report += u'\n\n'

    row_fmt = u'{:>{width}s} ' + u' {:>9.{digits}f}' * 3 + u' {:>9}' * 2 + '\n'

    ps, rs, f1s, s, prd = [], [], [], [], []
    for type_name, true_entities in d1.items():
        pred_entities = d2[type_name]
        # nb_correct = len(true_entities & pred_entities)
        nb_correct = process_entities_macro(true_entities, pred_entities, criteria = criteria)
        nb_pred = len(pred_entities)
        nb_true = len(true_entities)

        p = nb_correct / nb_pred if nb_pred > 0 else 0
        r = nb_correct / nb_true if nb_true > 0 else 0
        f1 = 2 * p * r / (p + r) if p + r > 0 else 0

        report += row_fmt.format(*[type_name, p, r, f1, nb_true, nb_pred], width=width, digits=digits)

        ps.append(p)
        rs.append(r)
        f1s.append(f1)
        s.append(nb_true)
        prd.append(nb_pred)

    report += u'\n'
    
    # with open('ytrue.json','w') as ytrue:
    #     json.dump(y_true,ytrue)
        # for yt in y_true:
        #     ytrue.write(str(yt)+'\n')
            
    # with open('ypred.json','w') as ypred:
    #     json.dump(y_pred,ypred)
        # for yp in y_pred:
        #     ypred.write(str(yp)+'\n')
            
    # compute averages
    report += row_fmt.format('micro avg',
                             precision_score(y_true, y_pred, suffix=suffix, criteria = criteria),
                             recall_score(y_true, y_pred, suffix=suffix, criteria = criteria),
                             f1_score(y_true, y_pred, suffix=suffix, criteria = criteria),
                             np.sum(s),
                             np.sum(prd),
                             width=width, digits=digits)
    report += row_fmt.format('macro avg',
                             np.average(ps),
                             np.average(rs),
                             np.average(f1s),
                             np.sum(s),
                             np.sum(prd),
                             width=width, digits=digits)
    report += row_fmt.format(last_line_heading,
                             np.average(ps, weights=s),
                             np.average(rs, weights=s),
                             np.average(f1s, weights=s),
                             np.sum(s),                          
                             np.sum(prd),
                             width=width, digits=digits)

    return report, np.average(f1s)
