# Path: data_generator.py
# class Tokenizer(object):
#     def __init__(self, args):
#         self.args = args
# 
#     def get_dict(self, seqs):
#         s = set()
#         for seq in seqs:
#             for elem in seq:
#                 s.add(elem)
#         return {e: i + 1 for i, e in enumerate(s)}
# 
#     def convert_sequence(self, seqs, dic):
#         result = []
#         for seq in seqs:
#             a = []
#             for elem in seq:
#                 if elem not in dic:
#                     unk = '<unk>'
#                     if unk not in dic:
#                         dic[unk] = len(dic) + 1
#                     a.append(dic[unk])
#                 else:
#                     a.append(dic[elem])
#             result.append(a)
#         return result
# 
#     def padding(self, seqs, el, pad=0):
#         lengths = []
#         for seq in seqs:
#             lengths.append(len(seq) + 1)
#             for _ in range(el - len(seq)):
#                 seq.append(pad)
#         return seqs, lengths
# 
#     def initialize_basic(self, X, Y, X_test, Y_test):
#         voc = self.get_dict(X)
#         act = self.get_dict(Y)
# 
#         x_out = self.convert_sequence(X, voc)
#         y_out = self.convert_sequence(Y, act)
#         x_test_out = self.convert_sequence(X_test, voc)
#         y_test_out = self.convert_sequence(Y_test, act)
#         return x_out, y_out, x_test_out, y_test_out, voc, act
# 
#     def get_maximum_length(self, train, test):
#         train_max = max([len(x) for x in train])
#         test_max = max([len(x) for x in test])
#         return max(train_max, test_max) + 1
# 
#     def initialize(self, X, Y, X_test, Y_test):
#         X, Y, X_test, Y_test, voc, act = self.initialize_basic(
#             X, Y, X_test, Y_test)
#         max_input = self.get_maximum_length(X, X_test)
#         max_output = self.get_maximum_length(Y, Y_test)
#         X, X_len = self.padding(X, max_input)
#         Y, Y_len = self.padding(Y, max_output)
#         X_test, X_test_len = self.padding(X_test, max_input)
#         Y_test, Y_test_len = self.padding(Y_test, max_output)
#         samples = X, Y, X_test, Y_test
#         dicts = voc, act
#         lengths = X_len, Y_len, X_test_len, Y_test_len
#         maxs = max_input, max_output
#         return samples, dicts, lengths, maxs
# 
# Path: data_generator.py
# def get_data_generator(name, args):
#     if name == 'fewshot':
#         return FewShotDataGenerator(args)
#     elif name == 'scan':
#         return SCANGenerator(args)
#     elif name == 'toy':
#         return ToyDataGenerator(args)
#     else:
#         raise ValueError("Data generator name is not defined: " + name)
# 
# Path: model.py
# def get_model(name, args):
#     if name == 'simple':
#         return SimpleModel(args)
#     elif name == 'lstm':
#         return LSTMModel(args)
#     elif name == 's2s':
#         return S2SModel(args)
#     elif name == 's2s_att':
#         return S2SAttentionModel(args)
#     elif name == 'rand_reg':
#         return RandRegModel(args)
#     elif name == 'normal':
#         return NormalModel(args)
#     else:
#         raise ValueError("Model name is not defined: " + name)


import argparse
import random
import editdistance
import os
import numpy as np
from data_generator import Tokenizer
from data_generator import get_data_generator
from model import get_model
            f.write(str(i) + '\t')
            f.write(str(len(b)) + '\t')
            f.write(str(len(c)) + '\t')
            f.write(str(ed) + '\t')
            f.write(str(wer))
            f.write('\n')
            f.write(' '.join(a))
            f.write('\n')
            f.write(' '.join(str(x[0]) for x in e))
            f.write('\n')
            f.write(' '.join(b))
            f.write('\n')
            f.write(' '.join(c))
            f.write('\n\n')
            visualization(i, a, b, c, d, e, directory)


def process(args):
    # prepare data
    dg = get_data_generator(args.data_name, args)
    train_X, train_Y = dg.get_train_data()
    test_X, test_Y = dg.get_test_data()

    if args.use_start_symbol:
        train_X = [['S'] + x for x in train_X]
        test_X = [['S'] + x for x in test_X]

    ori_test_X, ori_test_Y = test_X, test_Y

    # Tokenize

# Path: data_generator.py
# class Tokenizer(object):
#     def __init__(self, args):
#         self.args = args
# 
#     def get_dict(self, seqs):
#         s = set()
#         for seq in seqs:
#             for elem in seq:
#                 s.add(elem)
#         return {e: i + 1 for i, e in enumerate(s)}
# 
#     def convert_sequence(self, seqs, dic):
#         result = []
#         for seq in seqs:
#             a = []
#             for elem in seq:
#                 if elem not in dic:
#                     unk = '<unk>'
#                     if unk not in dic:
#                         dic[unk] = len(dic) + 1
#                     a.append(dic[unk])
#                 else:
#                     a.append(dic[elem])
#             result.append(a)
#         return result
# 
#     def padding(self, seqs, el, pad=0):
#         lengths = []
#         for seq in seqs:
#             lengths.append(len(seq) + 1)
#             for _ in range(el - len(seq)):
#                 seq.append(pad)
#         return seqs, lengths
# 
#     def initialize_basic(self, X, Y, X_test, Y_test):
#         voc = self.get_dict(X)
#         act = self.get_dict(Y)
# 
#         x_out = self.convert_sequence(X, voc)
#         y_out = self.convert_sequence(Y, act)
#         x_test_out = self.convert_sequence(X_test, voc)
#         y_test_out = self.convert_sequence(Y_test, act)
#         return x_out, y_out, x_test_out, y_test_out, voc, act
# 
#     def get_maximum_length(self, train, test):
#         train_max = max([len(x) for x in train])
#         test_max = max([len(x) for x in test])
#         return max(train_max, test_max) + 1
# 
#     def initialize(self, X, Y, X_test, Y_test):
#         X, Y, X_test, Y_test, voc, act = self.initialize_basic(
#             X, Y, X_test, Y_test)
#         max_input = self.get_maximum_length(X, X_test)
#         max_output = self.get_maximum_length(Y, Y_test)
#         X, X_len = self.padding(X, max_input)
#         Y, Y_len = self.padding(Y, max_output)
#         X_test, X_test_len = self.padding(X_test, max_input)
#         Y_test, Y_test_len = self.padding(Y_test, max_output)
#         samples = X, Y, X_test, Y_test
#         dicts = voc, act
#         lengths = X_len, Y_len, X_test_len, Y_test_len
#         maxs = max_input, max_output
#         return samples, dicts, lengths, maxs
# 
# Path: data_generator.py
# def get_data_generator(name, args):
#     if name == 'fewshot':
#         return FewShotDataGenerator(args)
#     elif name == 'scan':
#         return SCANGenerator(args)
#     elif name == 'toy':
#         return ToyDataGenerator(args)
#     else:
#         raise ValueError("Data generator name is not defined: " + name)
# 
# Path: model.py
# def get_model(name, args):
#     if name == 'simple':
#         return SimpleModel(args)
#     elif name == 'lstm':
#         return LSTMModel(args)
#     elif name == 's2s':
#         return S2SModel(args)
#     elif name == 's2s_att':
#         return S2SAttentionModel(args)
#     elif name == 'rand_reg':
#         return RandRegModel(args)
#     elif name == 'normal':
#         return NormalModel(args)
#     else:
#         raise ValueError("Model name is not defined: " + name)


# Path: main.py
import argparse
import random
import editdistance
import os
import numpy as np
from data_generator import Tokenizer
from data_generator import get_data_generator
from model import get_model

            f.write(str(i) + '\t')
            f.write(str(len(b)) + '\t')
            f.write(str(len(c)) + '\t')
            f.write(str(ed) + '\t')
            f.write(str(wer))
            f.write('\n')
            f.write(' '.join(a))
            f.write('\n')
            f.write(' '.join(str(x[0]) for x in e))
            f.write('\n')
            f.write(' '.join(b))
            f.write('\n')
            f.write(' '.join(c))
            f.write('\n\n')
            visualization(i, a, b, c, d, e, directory)


def process(args):
    # prepare data
    dg = get_data_generator(args.data_name, args)
    train_X, train_Y = dg.get_train_data()
    test_X, test_Y = dg.get_test_data()

    if args.use_start_symbol:
        train_X = [['S'] + x for x in train_X]
        test_X = [['S'] + x for x in test_X]

    ori_test_X, ori_test_Y = test_X, test_Y

    # Tokenize

    tokenizer = Tokenizer(args)