import re
import string
import unicodedata
from collections import Counter

from dsp.utils.utils import print_message


def EM(prediction, answers_list):
    assert type(answers_list) == list

    return max(em_score(prediction, ans) for ans in answers_list)


def F1(prediction, answers_list):
    assert type(answers_list) == list

    return max(f1_score(prediction, ans) for ans in answers_list)


def HotPotF1(prediction, answers_list):
    assert type(answers_list) == list

    return max(hotpot_f1_score(prediction, ans) for ans in answers_list)


def nF1(history, prediction, answers_list, return_recall=False):
    assert type(answers_list) == list

    return max(novel_f1_score(history, prediction, ans, return_recall=return_recall) for ans in answers_list)


def normalize_text(s):
    s = unicodedata.normalize('NFD', s)

    def remove_articles(text):
        return re.sub(r'\b(a|an|the)\b', ' ', text)

    def white_space_fix(text):
        return ' '.join(text.split())

    def remove_punc(text):
        exclude = set(string.punctuation)
        return ''.join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s))))


def em_score(prediction, ground_truth):
    return normalize_text(prediction) == normalize_text(ground_truth)


# See: https://github.com/hotpotqa/hotpot/blob/master/hotpot_evaluate_v1.py
# See: https://rajpurkar.github.io/SQuAD-explorer/ under Evaluation Script
# See: QReCC's

def f1_score(prediction, ground_truth):
    prediction_tokens = normalize_text(prediction).split()
    ground_truth_tokens = normalize_text(ground_truth).split()

    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
    num_same = sum(common.values())

    if len(prediction_tokens) == len(ground_truth_tokens) == 0:
        # Unlike most tasks, QReCC and SQuAD-2.0 assign 1.0 in this edge case. We don't for uniformity.
        print_message(
            "\n#> F1 Metric: Rare edge case of len(prediction_tokens) == len(ground_truth_tokens) == 0.\n")

    if num_same == 0:
        return 0

    precision = 1.0 * num_same / len(prediction_tokens)
    recall = 1.0 * num_same / len(ground_truth_tokens)
    f1 = (2 * precision * recall) / (precision + recall)

    return f1


def hotpot_f1_score(prediction, ground_truth):
    normalized_prediction = normalize_text(prediction)
    normalized_ground_truth = normalize_text(ground_truth)

    if normalized_prediction in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
        return 0
    if normalized_ground_truth in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
        return 0

    prediction_tokens = normalized_prediction.split()
    ground_truth_tokens = normalized_ground_truth.split()
    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
    num_same = sum(common.values())
    if num_same == 0:
        return 0
    precision = 1.0 * num_same / len(prediction_tokens)
    recall = 1.0 * num_same / len(ground_truth_tokens)
    f1 = (2 * precision * recall) / (precision + recall)
    return f1


def precision_score(prediction, ground_truth):
    prediction_tokens = normalize_text(prediction).split()
    ground_truth_tokens = normalize_text(ground_truth).split()

    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
    num_same = sum(common.values())

    if len(prediction_tokens) == len(ground_truth_tokens) == 0:
        # Unlike most tasks, QReCC and SQuAD-2.0 assign 1.0 in this edge case. We don't for uniformity.
        print_message(
            "\n#> F1 Metric: Rare edge case of len(prediction_tokens) == len(ground_truth_tokens) == 0.\n")

    if num_same == 0:
        return 0

    precision = 1.0 * num_same / len(prediction_tokens)

    return precision


# Source: https://gist.github.com/sebleier/554280
stopwords = ["i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself",
             "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself",
             "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these",
             "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do",
             "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while",
             "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before",
             "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again",
             "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each",
             "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than",
             "too", "very", "s", "t", "can", "will", "just", "don", "should", "now"]


def novel_f1_score(history, prediction, ground_truth, return_recall=False):
    history_tokens = normalize_text(history).split()
    prediction_tokens = normalize_text(prediction).split()
    ground_truth_tokens = normalize_text(ground_truth).split()

    history_tokens = set(history_tokens + stopwords)

    prediction_tokens = [
        t for t in prediction_tokens if t not in history_tokens]
    ground_truth_tokens = [
        t for t in ground_truth_tokens if t not in history_tokens]

    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
    num_same = sum(common.values())
    if num_same == 0:
        return 0

    precision = 1.0 * num_same / len(prediction_tokens)
    recall = 1.0 * num_same / len(ground_truth_tokens)
    f1 = (2 * precision * recall) / (precision + recall)

    if return_recall:
        return recall

    return f1