"""
@author: Arjun Krishna
@desc: data-manager
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import pandas as pd
import numpy as np
import os

class DataManager:

  def __init__(self, data_path):
    self.data_path  = data_path
    
    train_data = pd.read_csv(os.path.join(data_path, 'train.txt'), header=None)
    
    chars = set()
    for i in range(train_data.size):
      s = train_data.loc[i][0].lower()
      for c in s:
        chars.add(c)
    
    self.chars = list(chars)
    self.chars += [' <eos> ']
    self.vocab_size = len(self.chars)

    self.id2char = dict(zip(range(self.vocab_size), self.chars))
    self.char2id = dict(zip(self.chars, range(self.vocab_size)))

    self.raw_train_data = []
    for i in range(train_data.size):
      s = train_data.loc[i][0].lower()
      self.raw_train_data += [self.char2id[c] for c in s if c in self.char2id] + [self.char2id[' <eos> ']]


  def get_train(self, batch_size, num_steps):

    with tf.name_scope("Data_Producer", [self.raw_train_data, batch_size, num_steps]):
      self.train_data = tf.convert_to_tensor(self.raw_train_data, name="train_data", dtype=tf.int32)

      data_len = tf.size(self.train_data)
      batch_len = data_len // batch_size

      data = tf.reshape(self.train_data[0 : batch_size*batch_len],
                        [batch_size, batch_len])
      epoch_size = (batch_len - 1) // num_steps

      self.epoch_size = epoch_size

      assertion = tf.assert_positive(
        epoch_size,
        message="epoch_size == 0, decrease batch_size or num_steps")

      with tf.control_dependencies([assertion]):
        epoch_size = tf.identity(epoch_size, name="epoch_size")

      i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue()

      x = tf.strided_slice(data, [0, i * num_steps],
                           [batch_size, (i + 1) * num_steps])
      x.set_shape([batch_size, num_steps])
      y = tf.strided_slice(data, [0, i * num_steps + 1],
                           [batch_size, (i + 1) * num_steps + 1])
      y.set_shape([batch_size, num_steps])

      return x, y
d916f28447b44f6fa521ca7935996b1853eddc8c
<mask token>


class DataManager:
    <mask token>
    <mask token>

<mask token>


class DataManager:
    <mask token>

    def get_train(self, batch_size, num_steps):
        with tf.name_scope('Data_Producer', [self.raw_train_data,
            batch_size, num_steps]):
            self.train_data = tf.convert_to_tensor(self.raw_train_data,
                name='train_data', dtype=tf.int32)
            data_len = tf.size(self.train_data)
            batch_len = data_len // batch_size
            data = tf.reshape(self.train_data[0:batch_size * batch_len], [
                batch_size, batch_len])
            epoch_size = (batch_len - 1) // num_steps
            self.epoch_size = epoch_size
            assertion = tf.assert_positive(epoch_size, message=
                'epoch_size == 0, decrease batch_size or num_steps')
            with tf.control_dependencies([assertion]):
                epoch_size = tf.identity(epoch_size, name='epoch_size')
            i = tf.train.range_input_producer(epoch_size, shuffle=False
                ).dequeue()
            x = tf.strided_slice(data, [0, i * num_steps], [batch_size, (i +
                1) * num_steps])
            x.set_shape([batch_size, num_steps])
            y = tf.strided_slice(data, [0, i * num_steps + 1], [batch_size,
                (i + 1) * num_steps + 1])
            y.set_shape([batch_size, num_steps])
            return x, y

<mask token>


class DataManager:

    def __init__(self, data_path):
        self.data_path = data_path
        train_data = pd.read_csv(os.path.join(data_path, 'train.txt'),
            header=None)
        chars = set()
        for i in range(train_data.size):
            s = train_data.loc[i][0].lower()
            for c in s:
                chars.add(c)
        self.chars = list(chars)
        self.chars += [' <eos> ']
        self.vocab_size = len(self.chars)
        self.id2char = dict(zip(range(self.vocab_size), self.chars))
        self.char2id = dict(zip(self.chars, range(self.vocab_size)))
        self.raw_train_data = []
        for i in range(train_data.size):
            s = train_data.loc[i][0].lower()
            self.raw_train_data += [self.char2id[c] for c in s if c in self
                .char2id] + [self.char2id[' <eos> ']]

    def get_train(self, batch_size, num_steps):
        with tf.name_scope('Data_Producer', [self.raw_train_data,
            batch_size, num_steps]):
            self.train_data = tf.convert_to_tensor(self.raw_train_data,
                name='train_data', dtype=tf.int32)
            data_len = tf.size(self.train_data)
            batch_len = data_len // batch_size
            data = tf.reshape(self.train_data[0:batch_size * batch_len], [
                batch_size, batch_len])
            epoch_size = (batch_len - 1) // num_steps
            self.epoch_size = epoch_size
            assertion = tf.assert_positive(epoch_size, message=
                'epoch_size == 0, decrease batch_size or num_steps')
            with tf.control_dependencies([assertion]):
                epoch_size = tf.identity(epoch_size, name='epoch_size')
            i = tf.train.range_input_producer(epoch_size, shuffle=False
                ).dequeue()
            x = tf.strided_slice(data, [0, i * num_steps], [batch_size, (i +
                1) * num_steps])
            x.set_shape([batch_size, num_steps])
            y = tf.strided_slice(data, [0, i * num_steps + 1], [batch_size,
                (i + 1) * num_steps + 1])
            y.set_shape([batch_size, num_steps])
            return x, y

<mask token>
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import pandas as pd
import numpy as np
import os


class DataManager:

    def __init__(self, data_path):
        self.data_path = data_path
        train_data = pd.read_csv(os.path.join(data_path, 'train.txt'),
            header=None)
        chars = set()
        for i in range(train_data.size):
            s = train_data.loc[i][0].lower()
            for c in s:
                chars.add(c)
        self.chars = list(chars)
        self.chars += [' <eos> ']
        self.vocab_size = len(self.chars)
        self.id2char = dict(zip(range(self.vocab_size), self.chars))
        self.char2id = dict(zip(self.chars, range(self.vocab_size)))
        self.raw_train_data = []
        for i in range(train_data.size):
            s = train_data.loc[i][0].lower()
            self.raw_train_data += [self.char2id[c] for c in s if c in self
                .char2id] + [self.char2id[' <eos> ']]

    def get_train(self, batch_size, num_steps):
        with tf.name_scope('Data_Producer', [self.raw_train_data,
            batch_size, num_steps]):
            self.train_data = tf.convert_to_tensor(self.raw_train_data,
                name='train_data', dtype=tf.int32)
            data_len = tf.size(self.train_data)
            batch_len = data_len // batch_size
            data = tf.reshape(self.train_data[0:batch_size * batch_len], [
                batch_size, batch_len])
            epoch_size = (batch_len - 1) // num_steps
            self.epoch_size = epoch_size
            assertion = tf.assert_positive(epoch_size, message=
                'epoch_size == 0, decrease batch_size or num_steps')
            with tf.control_dependencies([assertion]):
                epoch_size = tf.identity(epoch_size, name='epoch_size')
            i = tf.train.range_input_producer(epoch_size, shuffle=False
                ).dequeue()
            x = tf.strided_slice(data, [0, i * num_steps], [batch_size, (i +
                1) * num_steps])
            x.set_shape([batch_size, num_steps])
            y = tf.strided_slice(data, [0, i * num_steps + 1], [batch_size,
                (i + 1) * num_steps + 1])
            y.set_shape([batch_size, num_steps])
            return x, y

"""
@author: Arjun Krishna
@desc: data-manager
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import pandas as pd
import numpy as np
import os

class DataManager:

  def __init__(self, data_path):
    self.data_path  = data_path
    
    train_data = pd.read_csv(os.path.join(data_path, 'train.txt'), header=None)
    
    chars = set()
    for i in range(train_data.size):
      s = train_data.loc[i][0].lower()
      for c in s:
        chars.add(c)
    
    self.chars = list(chars)
    self.chars += [' <eos> ']
    self.vocab_size = len(self.chars)

    self.id2char = dict(zip(range(self.vocab_size), self.chars))
    self.char2id = dict(zip(self.chars, range(self.vocab_size)))

    self.raw_train_data = []
    for i in range(train_data.size):
      s = train_data.loc[i][0].lower()
      self.raw_train_data += [self.char2id[c] for c in s if c in self.char2id] + [self.char2id[' <eos> ']]


  def get_train(self, batch_size, num_steps):

    with tf.name_scope("Data_Producer", [self.raw_train_data, batch_size, num_steps]):
      self.train_data = tf.convert_to_tensor(self.raw_train_data, name="train_data", dtype=tf.int32)

      data_len = tf.size(self.train_data)
      batch_len = data_len // batch_size

      data = tf.reshape(self.train_data[0 : batch_size*batch_len],
                        [batch_size, batch_len])
      epoch_size = (batch_len - 1) // num_steps

      self.epoch_size = epoch_size

      assertion = tf.assert_positive(
        epoch_size,
        message="epoch_size == 0, decrease batch_size or num_steps")

      with tf.control_dependencies([assertion]):
        epoch_size = tf.identity(epoch_size, name="epoch_size")

      i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue()

      x = tf.strided_slice(data, [0, i * num_steps],
                           [batch_size, (i + 1) * num_steps])
      x.set_shape([batch_size, num_steps])
      y = tf.strided_slice(data, [0, i * num_steps + 1],
                           [batch_size, (i + 1) * num_steps + 1])
      y.set_shape([batch_size, num_steps])

      return x, y