old_contents: # Copyright 2016 The TensorFlow Authors. 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.
  # ==============================================================================
  
  """Text datasets."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import tarfile
  
  import numpy as np
  
  from tensorflow.contrib.learn.python.learn.datasets import base
  from tensorflow.python.platform import gfile
  
  DBPEDIA_URL = 'https://github.com/le-scientifique/torchDatasets/blob/master/dbpedia_csv.tar.gz'
  
  
  def maybe_download_dbpedia(data_dir):
    """Download if DBpedia data is not present."""
    train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv')
    test_path = os.path.join(data_dir, 'dbpedia_csv/test.csv')
    if not (gfile.Exists(train_path) and gfile.Exists(test_path)):
      archive_path = base.maybe_download(
          'dbpedia_csv.tar.gz', data_dir, DBPEDIA_URL)
      tfile = tarfile.open(archive_path, 'r:*')
      tfile.extractall(data_dir)
  
  
  def load_dbpedia(size='small', test_with_fake_data=False):
    """Get DBpedia datasets from CSV files."""
    if not test_with_fake_data:
      data_dir = os.path.join(os.getenv('TF_EXP_BASE_DIR', ''), 'dbpedia_data')
      maybe_download_dbpedia(data_dir)
  
      train_path = os.path.join(data_dir, 'dbpedia_csv', 'train.csv')
      test_path = os.path.join(data_dir, 'dbpedia_csv', 'test.csv')
  
      if size == 'small':
        # Reduce the size of original data by a factor of 1000.
        base.shrink_csv(train_path, 1000)
        base.shrink_csv(test_path, 1000)
        train_path = train_path.replace('train.csv', 'train_small.csv')
        test_path = test_path.replace('test.csv', 'test_small.csv')
    else:
      module_path = os.path.dirname(__file__)
      train_path = os.path.join(module_path, 'data', 'text_train.csv')
      test_path = os.path.join(module_path, 'data', 'text_test.csv')
  
    train = base.load_csv_without_header(
        train_path, target_dtype=np.int32, features_dtype=np.str, target_column=0)
    test = base.load_csv_without_header(
        test_path, target_dtype=np.int32, features_dtype=np.str, target_column=0)
  
    return base.Datasets(train=train, validation=None, test=test)
  
new_contents: # Copyright 2016 The TensorFlow Authors. 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.
  # ==============================================================================
  
  """Text datasets."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import tarfile
  
  import numpy as np
  
  from tensorflow.contrib.learn.python.learn.datasets import base
  from tensorflow.python.platform import gfile
  
  DBPEDIA_URL = 'https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz'
  
  
  def maybe_download_dbpedia(data_dir):
    """Download if DBpedia data is not present."""
    train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv')
    test_path = os.path.join(data_dir, 'dbpedia_csv/test.csv')
    if not (gfile.Exists(train_path) and gfile.Exists(test_path)):
      archive_path = base.maybe_download(
          'dbpedia_csv.tar.gz', data_dir, DBPEDIA_URL)
      tfile = tarfile.open(archive_path, 'r:*')
      tfile.extractall(data_dir)
  
  
  def load_dbpedia(size='small', test_with_fake_data=False):
    """Get DBpedia datasets from CSV files."""
    if not test_with_fake_data:
      data_dir = os.path.join(os.getenv('TF_EXP_BASE_DIR', ''), 'dbpedia_data')
      maybe_download_dbpedia(data_dir)
  
      train_path = os.path.join(data_dir, 'dbpedia_csv', 'train.csv')
      test_path = os.path.join(data_dir, 'dbpedia_csv', 'test.csv')
  
      if size == 'small':
        # Reduce the size of original data by a factor of 1000.
        base.shrink_csv(train_path, 1000)
        base.shrink_csv(test_path, 1000)
        train_path = train_path.replace('train.csv', 'train_small.csv')
        test_path = test_path.replace('test.csv', 'test_small.csv')
    else:
      module_path = os.path.dirname(__file__)
      train_path = os.path.join(module_path, 'data', 'text_train.csv')
      test_path = os.path.join(module_path, 'data', 'text_test.csv')
  
    train = base.load_csv_without_header(
        train_path, target_dtype=np.int32, features_dtype=np.str, target_column=0)
    test = base.load_csv_without_header(
        test_path, target_dtype=np.int32, features_dtype=np.str, target_column=0)
  
    return base.Datasets(train=train, validation=None, test=test)
  
subject: Fix url to dbpedia to be downloadable archive in tf.learn.datasets. Change: 143750328