text: import random
  
  import numpy as np
  
  from esrt.engine.base_input_feed import BaseInputFeed
  
  
  class LSEInputFeed(BaseInputFeed):
      def __init__(self, model, batch_size):
          self.model = model
          self.batch_size = batch_size
  
      def setup_data_set(self, data_set, words_to_train):
          self.data_set = data_set
          self.words_to_train = words_to_train
          self.finished_word_num = 0
          if self.model.net_struct == 'hdc':
              self.model.need_context = True
  
          if 'LSE' == self.model.net_struct:
              self.model.need_review = False
              self.model.need_context = True
  
      def intialize_epoch(self, training_seq):
          self.train_seq = training_seq
          self.review_size = len(self.train_seq)
          self.cur_review_i = 0
          self.cur_word_i = 0
  
      def get_train_batch(self,debug=False):
          user_idxs, product_idxs, review_idxs, word_idxs, context_word_idxs = [],[],[],[],[]
          query_word_idxs = []
          # learning_rate = self.model.init_learning_rate * max(0.0001,
          #                             1.0 - self.finished_word_num / self.words_to_train)
          learning_rate = self.model.init_learning_rate
          review_idx = self.train_seq[self.cur_review_i]
          user_idx = self.data_set.review_info[review_idx][0]
          product_idx = self.data_set.review_info[review_idx][1]
          query_idx = random.choice(self.data_set.product_query_idx[product_idx])
          text_list = self.data_set.review_text[review_idx]
          text_length = len(text_list)
          while len(word_idxs) < self.batch_size:
              #print('review %d word %d word_idx %d' % (review_idx, self.cur_word_i, text_list[self.cur_word_i]))
              #if sample this word
              if self.data_set.sub_sampling_rate == None or random.random() < self.data_set.sub_sampling_rate[text_list[self.cur_word_i]]:
                  user_idxs.append(user_idx)
                  product_idxs.append(product_idx)
                  query_word_idxs.append(self.data_set.query_words[query_idx])
                  review_idxs.append(review_idx)
                  word_idxs.append(text_list[self.cur_word_i])
  
                  i = self.cur_word_i
                  start_index = i - self.model.window_size if i - self.model.window_size > 0 else 0
                  context_word_list = text_list[start_index:i] + text_list[i + 1:i + self.model.window_size + 1]
                  while len(context_word_list) < 2 * self.model.window_size:
                      context_word_list += text_list[
                                           start_index:start_index + 2 * self.model.window_size - len(context_word_list)]
                  context_word_idxs.append(context_word_list)
  
              #move to the next
              self.cur_word_i += 1
              self.finished_word_num += 1
              if self.cur_word_i == text_length:
                  self.cur_review_i += 1
                  if self.cur_review_i == self.review_size:
                      break
                  self.cur_word_i = 0
                  review_idx = self.train_seq[self.cur_review_i]
                  user_idx = self.data_set.review_info[review_idx][0]
                  product_idx = self.data_set.review_info[review_idx][1]
                  query_idx = random.choice(self.data_set.product_query_idx[product_idx])
                  text_list = self.data_set.review_text[review_idx]
                  text_length = len(text_list)
  
          length = len(word_idxs)
          batch_context_word_idxs = []
          for length_idx in range(2 * self.model.window_size):
              batch_context_word_idxs.append(np.array([context_word_idxs[batch_idx][length_idx]
                                                       for batch_idx in range(length)], dtype=np.int64))
          has_next = False if self.cur_review_i == self.review_size else True
  
          # create input feed
          input_feed = {}
          input_feed[self.model.learning_rate.name] = learning_rate
          input_feed[self.model.user_idxs.name] = user_idxs
          input_feed[self.model.product_idxs.name] = product_idxs
          input_feed[self.model.query_word_idxs.name] = query_word_idxs
          input_feed[self.model.review_idxs.name] = review_idxs
          input_feed[self.model.word_idxs.name] = word_idxs
          for i in range(2 * self.model.window_size):
              input_feed[self.model.context_word_idxs[i].name] = batch_context_word_idxs[i]
          return input_feed, has_next
  
      def prepare_test_epoch(self, debug=False):
          self.test_user_query_set = set()
          self.test_seq = []
          for review_idx in range(len(self.data_set.review_info)):
              user_idx = self.data_set.review_info[review_idx][0]
              product_idx = self.data_set.review_info[review_idx][1]
              for query_idx in self.data_set.product_query_idx[product_idx]:
                  if (user_idx, query_idx) not in self.test_user_query_set:
                      self.test_user_query_set.add((user_idx, query_idx))
                      self.test_seq.append((user_idx, product_idx, query_idx, review_idx))
          self.cur_uqr_i = 0
  
      def get_test_batch(self,debug=False):
          user_idxs, product_idxs, review_idxs, word_idxs, context_word_idxs = [],[],[],[],[]
          query_word_idxs = []
          # learning_rate = self.model.init_learning_rate * max(0.0001,
          #                             1.0 - self.finished_word_num / self.words_to_train)
          learning_rate = self.model.init_learning_rate
          start_i = self.cur_uqr_i
          user_idx, product_idx, query_idx, review_idx = self.test_seq[self.cur_uqr_i]
  
          while len(user_idxs) < self.batch_size:
              text_list = self.data_set.review_text[review_idx]
              user_idxs.append(user_idx)
              product_idxs.append(product_idx)
              query_word_idxs.append(self.data_set.query_words[query_idx])
              review_idxs.append(review_idx)
              word_idxs.append(text_list[0])
  
              #move to the next review
              self.cur_uqr_i += 1
              if self.cur_uqr_i == len(self.test_seq):
                  break
              user_idx, product_idx, query_idx, review_idx = self.test_seq[self.cur_uqr_i]
  
  
          has_next = False if self.cur_uqr_i == len(self.test_seq) else True
          # create input feed
          input_feed = {}
          input_feed[self.model.learning_rate.name] = learning_rate
          input_feed[self.model.user_idxs.name] = user_idxs
          input_feed[self.model.product_idxs.name] = product_idxs
          input_feed[self.model.query_word_idxs.name] = query_word_idxs
          input_feed[self.model.review_idxs.name] = review_idxs
          input_feed[self.model.word_idxs.name] = word_idxs
  
  
          return input_feed, has_next, self.test_seq[start_i:self.cur_uqr_i]
  