text: # Copyright 2016 Google Inc. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Small library that points to a data set.
  
  Methods of Data class:
    data_files: Returns a python list of all (sharded) data set files.
    num_examples_per_epoch: Returns the number of examples in the data set.
    num_classes: Returns the number of classes in the data set.
    reader: Return a reader for a single entry from the data set.
  """
  from __future__ import absolute_import 
  from __future__ import division
  from __future__ import print_function
  
  
  from abc import ABCMeta
  from abc import abstractmethod
  import os
  
  import tensorflow as tf 
  
  FLAGS = tf.app.flags.FLAGS
  
  # Basic model parameters.
  tf.app.flags.DEFINE_string('data_dir', './data_utils/data/hmdb51_rgb_3d/',
                            """Path to the processed data, i.e. """
                            """TFRecord of Example protos.""")
  
  class Dataset(object):
    """A simple class for handling data sets."""
    __metaclass__ = ABCMeta
  
    def __init__(self, name, subset):
      """Initialize dataset using a subset and the path to the data."""
      assert subset in self.available_subsets(), self.available_subsets()
      self.name = name
      self.subset = subset
  
  
    @abstractmethod
    def num_classes(self):
      """Returns the number of classes in the data set."""
      pass
      # return 10
  
    @abstractmethod
    def num_examples_per_epoch(self):
      """"""
  
    @abstractmethod
    def num_classes(self):
      """Returns the number of classes in the data set."""
      pass
      # return 10
  
    @abstractmethod
    def num_examples_per_epoch(self):
      """Returns the number of examples in the data subset."""
      pass
      # if self.subset == 'train':
      #   return 10000
      # if self.subset == 'validation':
      #   return 1000
  
    @abstractmethod
    def download_message(self):
      """Prints a download message for the Dataset."""
      pass
  
    def available_subsets(self):
      """Returns the list of available subsets."""
      return ['train', 'validation']
  
    def data_files(self):
      """Returns a python list of all (sharded) data subset files.
  
      Returns:
        python list of all (sharded) data set files.
      Raises:
        ValueError: if there are not data_files matching the subset.
      """
      tf_record_pattern = os.path.join(FLAGS.data_dir, '%s-*' % self.subset)
     
      data_files = tf.gfile.Glob(tf_record_pattern)
    
      if not data_files:
        print('No files found for dataset %s/%s at %s' % (self.name, self.subset,
                                                          FLAGS.data_dir))
  
        self.download_message()
        exit(-1)
      return data_files
  
    def reader(self):
      """Return a reader for a single entry from the data set.
  
      See io_ops.py for details of Reader class.
  
      Returns:
        Reader object that reads the data set.
      """
      return tf.TFRecordReader()
  