import numpy as np
from utils.plog import INFO
from collections import OrderedDict

# for multiple predictions, modify from `learn_add.py`
def flatten(l):
    return [item for sublist in l for item in flatten(sublist)] if isinstance(l, list) else [l]
    
# for multiple predictions, modify from `learn_add.py`
def reform_idx(flatten_pred_res, save_pred_res):
    re = []
    i = 0
    for e in save_pred_res:
        j = 0
        idx = []
        while j < len(e):
            idx.append(flatten_pred_res[i + j])
            j += 1
        re.append(idx)
        i = i + j
    return re

def hamming_dist(A, B):
    B = np.array(B)
    A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B)))
    return np.sum(A != B, axis = 1)

def confidence_dist(A, B):
    B = np.array(B)
    A = np.clip(A, 1e-9, 1)
    A = np.expand_dims(A, axis=0)
    A = A.repeat(axis=0, repeats=(len(B)))
    rows = np.array(range(len(B)))
    rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0]))
    cols = np.array(range(len(B[0])))
    cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B))
    return 1 - np.prod(A[rows, cols, B], axis = 1)


def block_sample(X, Z, Y, sample_num, epoch_idx):
    part_num = len(X) // sample_num
    if part_num == 0:
        part_num = 1
    seg_idx = epoch_idx % part_num
    INFO("seg_idx:", seg_idx, ", part num:", part_num, ", data num:", len(X))
    X = X[sample_num * seg_idx : sample_num * (seg_idx + 1)]
    Z = Z[sample_num * seg_idx : sample_num * (seg_idx + 1)]
    Y = Y[sample_num * seg_idx : sample_num * (seg_idx + 1)]

    return X, Z, Y


def gen_mappings(chars, symbs):
	n_char = len(chars)
	n_symbs = len(symbs)
	if n_char != n_symbs:
		print('Characters and symbols size dosen\'t match.')
		return
	from itertools import permutations
	mappings = []
	# returned mappings
	perms = permutations(symbs)
	for p in perms:
		mappings.append(dict(zip(chars, list(p))))
	return mappings

def mapping_res(original_pred_res, m):
    return [[m[symbol] for symbol in formula] for formula in original_pred_res]

def remapping_res(pred_res, m):
    remapping = {}
    for key, value in m.items():
        remapping[value] = key
    return [[remapping[symbol] for symbol in formula] for formula in pred_res]