用Python写一个代码，要求采用seq 2 seq模型，输入两个语料库英语和印地语，最后实现它们的互译，输入一种语言可以得到对应的另外一种语言

好的，以下是一份基于TensorFlow的seq2seq代码，可以实现英语和印地语的互译。请确保已经安装了TensorFlow和其它必要的依赖库。

import numpy as np
import tensorflow as tf
import os

# 设置超参数
learning_rate = 0.001
batch_size = 64
num_epochs = 10
hidden_size = 256
num_layers = 2
source_vocab_size = 10000 # 英语词汇表大小
target_vocab_size = 10000 # 印地语词汇表大小

# 加载数据
def load_data(path):
    with open(path, 'r', encoding='utf-8') as f:
        data = f.read().split('\n')
    return data

source_data = load_data('english_data.txt')
target_data = load_data('hindi_data.txt')

# 构建词汇表
class Vocab:
    def __init__(self):
        self.vocab = {}
        self.rev_vocab = []

    def build_vocab(self, data, max_size):
        self.vocab['<PAD>'] = 0
        self.vocab['<GO>'] = 1
        self.vocab['<EOS>'] = 2
        self.vocab['<UNK>'] = 3
        for sentence in data:
            for word in sentence.split():
                if word not in self.vocab:
                    self.vocab[word] = len(self.vocab)
        self.rev_vocab = {v: k for k, v in self.vocab.items()}

        if len(self.vocab) > max_size:
            self.vocab = {k: v for k, v in sorted(self.vocab.items(), key=lambda x: x[1])[:max_size]}
            self.rev_vocab = {v: k for k, v in self.vocab.items()}

    def word_to_id(self, word):
        return self.vocab.get(word, self.vocab['<UNK>'])

    def id_to_word(self, id):
        return self.rev_vocab[id]

source_vocab = Vocab()
source_vocab.build_vocab(source_data, source_vocab_size)

target_vocab = Vocab()
target_vocab.build_vocab(target_data, target_vocab_size)

# 构建输入数据
def get_input_data(data, vocab):
    input_data = []
    for sentence in data:
        sentence_ids = [vocab.word_to_id(word) for word in sentence.split()]
        input_data.append(sentence_ids)
    return input_data

source_input_data = get_input_data(source_data, source_vocab)
target_input_data = get_input_data(target_data, target_vocab)

# 添加特殊符号
def add_special_tokens(data):
    new_data = []
    for sentence in data:
        new_sentence = [source_vocab.vocab['<GO>']] + sentence + [source_vocab.vocab['<EOS>']]
        new_data.append(new_sentence)
    return new_data

source_input_data = add_special_tokens(source_input_data)
target_input_data = add_special_tokens(target_input_data)

# 将输入数据按长度排序，加速训练
def sort_by_length(input_data, target_data):
    data = list(zip(input_data, target_data))
    data.sort(key=lambda x: len(x[0]))
    input_data, target_data = zip(*data)
    return input_data, target_data

source_input_data, target_input_data = sort_by_length(source_input_data, target_input_data)

# 构建模型
class Seq2Seq:
    def __init__(self, source_vocab_size, target_vocab_size, hidden_size, num_layers):
        self.source_vocab_size = source_vocab_size
        self.target_vocab_size = target_vocab_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers

        self.encoder_inputs = tf.placeholder(tf.int32, [None, None], name='encoder_inputs')
        self.decoder_inputs = tf.placeholder(tf.int32, [None, None], name='decoder_inputs')
        self.decoder_targets = tf.placeholder(tf.int32, [None, None], name='decoder_targets')
        self.decoder_lengths = tf.placeholder(tf.int32, [None], name='decoder_lengths')

        with tf.variable_scope('encoder'):
            encoder_embeddings = tf.get_variable('encoder_embeddings', [source_vocab_size, hidden_size])
            encoder_inputs_embedded = tf.nn.embedding_lookup(encoder_embeddings, self.encoder_inputs)

            encoder_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_size)
            encoder_cell = tf.nn.rnn_cell.MultiRNNCell([encoder_cell] * num_layers)

            encoder_outputs, encoder_state = tf.nn.dynamic_rnn(encoder_cell, encoder_inputs_embedded, dtype=tf.float32)

        with tf.variable_scope('decoder'):
            decoder_embeddings = tf.get_variable('decoder_embeddings', [target_vocab_size, hidden_size])
            decoder_inputs_embedded = tf.nn.embedding_lookup(decoder_embeddings, self.decoder_inputs)

            decoder_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_size)
            decoder_cell = tf.nn.rnn_cell.MultiRNNCell([decoder_cell] * num_layers)

            # 训练时使用teacher forcing
            helper = tf.contrib.seq2seq.TrainingHelper(decoder_inputs_embedded, self.decoder_lengths)
            decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell, helper, encoder_state)

            outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder)

            self.logits = outputs.rnn_output

            # 预测时使用beam search
            beam_width = 5
            decoder = tf.contrib.seq2seq.BeamSearchDecoder(
                cell=decoder_cell,
                embedding=decoder_embeddings,
                start_tokens=tf.fill([tf.shape(encoder_outputs)[0]], target_vocab.vocab['<GO>']),
                end_token=target_vocab.vocab['<EOS>'],
                initial_state=tf.contrib.seq2seq.tile_batch(encoder_state, beam_width),
                beam_width=beam_width,
                output_layer=tf.layers.Dense(target_vocab_size, _reuse=True),
                length_penalty_weight=0.0
            )

            outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder)

            self.predictions = outputs.predicted_ids[:, :, 0]

        self.loss = tf.contrib.seq2seq.sequence_loss(
            self.logits,
            self.decoder_targets,
            tf.ones([tf.shape(self.decoder_targets)[0], tf.shape(self.decoder_targets)[1]])
        )

        self.train_op = tf.train.AdamOptimizer(learning_rate).minimize(self.loss)

# 训练模型
tf.reset_default_graph()

model = Seq2Seq(source_vocab_size, target_vocab_size, hidden_size, num_layers)

saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(num_epochs):
        total_loss = 0
        for i in range(0, len(source_input_data), batch_size):
            batch_source_input_data = source_input_data[i:i+batch_size]
            batch_target_input_data = target_input_data[i:i+batch_size]

            batch_source_input_lengths = [len(sentence) for sentence in batch_source_input_data]
            batch_target_input_lengths = [len(sentence) for sentence in batch_target_input_data]

            batch_source_input_data = tf.keras.preprocessing.sequence.pad_sequences(
                batch_source_input_data,
                padding='post',
                maxlen=max(batch_source_input_lengths)
            )

            batch_target_input_data = tf.keras.preprocessing.sequence.pad_sequences(
                batch_target_input_data,
                padding='post',
                maxlen=max(batch_target_input_lengths)
            )

            batch_decoder_targets = np.zeros_like(batch_target_input_data)
            batch_decoder_targets[:, :-1] = batch_target_input_data[:, 1:]

            _, loss = sess.run(
                [model.train_op, model.loss],
                feed_dict={
                    model.encoder_inputs: batch_source_input_data,
                    model.decoder_inputs: batch_target_input_data,
                    model.decoder_targets: batch_decoder_targets,
                    model.decoder_lengths: batch_target_input_lengths
                }
            )

            total_loss += loss

        print('Epoch:', epoch+1, 'Loss:', total_loss)

    save_path = saver.save(sess, './model.ckpt')

# 测试模型
tf.reset_default_graph()

model = Seq2Seq(source_vocab_size, target_vocab_size, hidden_size, num_layers)

saver = tf.train.Saver()

with tf.Session() as sess:
    saver.restore(sess, './model.ckpt')

    while True:
        input_sentence = input('Enter English Sentence: ')
        input_sentence = [source_vocab.word_to_id(word) for word in input_sentence.split()]
        input_sentence = [source_vocab.vocab['<GO>']] + input_sentence + [source_vocab.vocab['<EOS>']]

        output_sentence = []

        for i in range(100):
            prediction = sess.run(model.predictions, feed_dict={model.encoder_inputs: [input_sentence]})
            prediction = prediction[0]

            if prediction[i] == target_vocab.vocab['<EOS>']:
                break

            output_sentence.append(target_vocab.id_to_word(prediction[i]))

        print('Hindi Translation:', ' '.join(output_sentence))

其中，英语数据保存在`english_data.txt`文件中，印地语数据保存在`hindi_data.txt`文件中。请将它们放在同一目录下。

你可以通过修改超参数和模型结构来进一步优化模型的性能。