import argparse
import random
import time
import unicodedata
import sys

import numpy as np


SHINGLE_SIZE = 5  # Known as k
PERMUTATIONS_COUNT = 3


def parse_args(argv: dict = None) -> argparse.Namespace:
    if argv is None:
        argv = sys.argv

    parser = argparse.ArgumentParser(description='Exercise 1')
    parser.add_argument('input', nargs='?', type=argparse.FileType('r'), help='Documents to read.', default=sys.stdin)
    parser.add_argument('similarity', nargs='?', type=float, help='Similarity threshold.', default=0.05)

    return parser.parse_args(argv[1:])


def normalize(doc: str) -> str:
    """
    Remove accents from letters, remove non-ascii letters, keep only letters and digits.
    """
    return ''.join(char for char in unicodedata.normalize(
        'NFKD', doc.casefold().replace('æ', 'ae').replace('œ', 'oe'))
                   if unicodedata.category(char) in ['Lu', 'Ll', 'Nd']
    ).casefold().encode('ascii', 'ignore').decode('ascii')


def compute_shingles(docs: list[str], single_size: int) -> np.ndarray:
    shingle_matrix = np.zeros((2, len(docs)), dtype=bool)
    shingle_id = {}

    for doc_id, doc in enumerate(docs):
        char_shing = [doc[i:i + single_size] for i in range(len(doc) - single_size + 1)]
        for sh in char_shing:
            if sh not in shingle_id:
                shingle_id[sh] = len(shingle_id)
                if shingle_id[sh] >= len(shingle_matrix):
                    # Extend matrix, double its size
                    shingle_matrix = np.append(shingle_matrix, np.zeros(shingle_matrix.shape, dtype=bool), axis=0)

            shingle_matrix[shingle_id[sh], doc_id] = True

    shingle_matrix = shingle_matrix[:len(shingle_id)]

    return shingle_matrix


def min_hash(doc: str, perm: list[str]) -> str:
    for d in perm:
        if d in doc:
            return d


def compute_signature_matrix(shingles: np.ndarray, permutations_count: int) -> np.ndarray:
    shingles_count, docs_count = shingles.shape

    signature_matrix = np.inf * np.ones((permutations_count, docs_count))

    for permutation_id in range(permutations_count):
        permutation = np.random.permutation(shingles_count)

        for doc_id, doc_shingles in enumerate(shingles.T):
            for x in permutation:
                if doc_shingles[x]:
                    signature_matrix[permutation_id, doc_id] = x
                    break

    return signature_matrix


def jaccard_similarity(doc1: set, doc2: set) -> float:
    if not doc1 or not doc2:
        return 0.0

    inter = doc1.intersection(doc2)
    union = doc1.union(doc2)
    return len(inter) / len(union)


def parse(stream, similarity: float) -> None:
    docs = [line.rstrip('\n') for line in stream]
    docs = [normalize(doc) for doc in docs]  # Remove special characters and normalize accents

    shingles = compute_shingles(docs, SHINGLE_SIZE)
    signature = compute_signature_matrix(shingles, PERMUTATIONS_COUNT)

    shingles_sets = [set(x for x in range(len(shingles)) if shingles[x, doc_id]) for doc_id in range(len(docs))]
    for i, doc1 in enumerate(shingles_sets):
        t = time.time()
        for j in range(i + 1, len(docs)):
            doc2 = shingles_sets[j]
            d = jaccard_similarity(doc1, doc2)
            if d >= similarity:
                print(f"{i} {j} {d:.06f}")


def main():
    ns = parse_args()
    parse(ns.input, ns.similarity)