# Copyright 2018 The Texar 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.
"""
Various utilities specific to data processing.
"""
import os
import sys
import tarfile
import zipfile
import collections
import numpy as np
from six.moves import urllib
import requests
import tensorflow as tf
from texar.tf.utils import utils_io
# pylint: disable=invalid-name, too-many-branches
__all__ = [
"maybe_download",
"read_words",
"make_vocab",
"count_file_lines"
]
Py3 = sys.version_info[0] == 3
[docs]def maybe_download(urls, path, filenames=None, extract=False):
"""Downloads a set of files.
Args:
urls: A (list of) urls to download files.
path (str): The destination path to save the files.
filenames: A (list of) strings of the file names. If given,
must have the same length with :attr:`urls`. If `None`,
filenames are extracted from :attr:`urls`.
extract (bool): Whether to extract compressed files.
Returns:
A list of paths to the downloaded files.
"""
utils_io.maybe_create_dir(path)
if not isinstance(urls, (list, tuple)):
urls = [urls]
if filenames is not None:
if not isinstance(filenames, (list, tuple)):
filenames = [filenames]
if len(urls) != len(filenames):
raise ValueError(
'`filenames` must have the same number of elements as `urls`.')
result = []
for i, url in enumerate(urls):
if filenames is not None:
filename = filenames[i]
elif 'drive.google.com' in url:
filename = _extract_google_drive_file_id(url)
else:
filename = url.split('/')[-1]
# If downloading from GitHub, remove suffix ?raw=True
# from local filename
if filename.endswith("?raw=true"):
filename = filename[:-9]
filepath = os.path.join(path, filename)
result.append(filepath)
if not tf.gfile.Exists(filepath):
if 'drive.google.com' in url:
filepath = _download_from_google_drive(url, filename, path)
else:
filepath = _download(url, filename, path)
if extract:
tf.logging.info('Extract %s', filepath)
if tarfile.is_tarfile(filepath):
tarfile.open(filepath, 'r').extractall(path)
elif zipfile.is_zipfile(filepath):
with zipfile.ZipFile(filepath) as zfile:
zfile.extractall(path)
else:
tf.logging.info("Unknown compression type. Only .tar.gz, "
".tar.bz2, .tar, and .zip are supported")
return result
def _download(url, filename, path):
def _progress(count, block_size, total_size):
percent = float(count * block_size) / float(total_size) * 100.
# pylint: disable=cell-var-from-loop
sys.stdout.write('\r>> Downloading %s %.1f%%' %
(filename, percent))
sys.stdout.flush()
filepath = os.path.join(path, filename)
filepath, _ = urllib.request.urlretrieve(url, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Successfully downloaded {} {} bytes.'.format(
filename, statinfo.st_size))
return filepath
def _extract_google_drive_file_id(url):
# id is between `/d/` and '/'
url_suffix = url[url.find('/d/') + 3:]
file_id = url_suffix[:url_suffix.find('/')]
return file_id
def _download_from_google_drive(url, filename, path):
"""Adapted from `https://github.com/saurabhshri/gdrive-downloader`
"""
def _get_confirm_token(response):
for key, value in response.cookies.items():
if key.startswith('download_warning'):
return value
return None
file_id = _extract_google_drive_file_id(url)
gurl = "https://docs.google.com/uc?export=download"
sess = requests.Session()
response = sess.get(gurl, params={'id': file_id}, stream=True)
token = _get_confirm_token(response)
if token:
params = {'id': file_id, 'confirm': token}
response = sess.get(gurl, params=params, stream=True)
filepath = os.path.join(path, filename)
CHUNK_SIZE = 32768
with tf.gfile.GFile(filepath, "wb") as f:
for chunk in response.iter_content(CHUNK_SIZE):
if chunk:
f.write(chunk)
print('Successfully downloaded {}.'.format(filename))
return filepath
[docs]def read_words(filename, newline_token=None):
"""Reads word from a file.
Args:
filename (str): Path to the file.
newline_token (str, optional): The token to replace the original newline
token "\\\\n". For example,
`newline_token=tx.data.SpecialTokens.EOS`.
If `None`, no replacement is performed.
Returns:
A list of words.
"""
with tf.gfile.GFile(filename, "r") as f:
if Py3:
if newline_token is None:
return f.read().split()
else:
return f.read().replace("\n", newline_token).split()
else:
if newline_token is None:
return f.read().decode("utf-8").split()
else:
return (f.read().decode("utf-8")
.replace("\n", newline_token).split())
[docs]def make_vocab(filenames, max_vocab_size=-1, newline_token=None,
return_type="list", return_count=False):
"""Builds vocab of the files.
Args:
filenames (str): A (list of) files.
max_vocab_size (int): Maximum size of the vocabulary. Low frequency
words that exceeding the limit will be discarded.
Set to `-1` (default) if no truncation is wanted.
newline_token (str, optional): The token to replace the original newline
token "\\\\n". For example,
`newline_token=tx.data.SpecialTokens.EOS`.
If `None`, no replacement is performed.
return_type (str): Either "list" or "dict". If "list" (default), this
function returns a list of words sorted by frequency. If "dict",
this function returns a dict mapping words to their index sorted
by frequency.
return_count (bool): Whether to return word counts. If `True` and
:attr:`return_type` is "dict", then a count dict is returned, which
is a mapping from words to their frequency.
Returns:
- If :attr:`return_count` is False, returns a list or dict containing \
the vocabulary words.
- If :attr:`return_count` if True, returns a pair of list or dict \
`(a, b)`, where `a` is a list or dict containing the vocabulary \
words, `b` is a list of dict containing the word counts.
"""
if not isinstance(filenames, (list, tuple)):
filenames = [filenames]
words = []
for fn in filenames:
words += read_words(fn, newline_token=newline_token)
counter = collections.Counter(words)
count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))
words, counts = list(zip(*count_pairs))
if max_vocab_size >= 0:
words = words[:max_vocab_size]
counts = counts[:max_vocab_size]
if return_type == "list":
if not return_count:
return words
else:
return words, counts
elif return_type == "dict":
word_to_id = dict(zip(words, range(len(words))))
if not return_count:
return word_to_id
else:
word_to_count = dict(zip(words, counts))
return word_to_id, word_to_count
else:
raise ValueError("Unknown return_type: {}".format(return_type))
[docs]def count_file_lines(filenames):
"""Counts the number of lines in the file(s).
"""
def _count_lines(fn):
with open(fn, "rb") as f:
i = -1
for i, _ in enumerate(f):
pass
return i + 1
if not isinstance(filenames, (list, tuple)):
filenames = [filenames]
num_lines = np.sum([_count_lines(fn) for fn in filenames])
return num_lines