Update HTTP links to HTTPS where possible (#3142)

* Update HTTP links to HTTPS where possible

* Fix line too long

Author: bryant1410

LICENSE

@@ -1,6 +1,6 @@
                                  Apache License
                            Version 2.0, January 2004
-                        http://www.apache.org/licenses/
+                        https://www.apache.org/licenses/
 
    TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
 
@@ -192,7 +192,7 @@
    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
+       https://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,

MODELS.md

@@ -92,7 +92,7 @@ Based on [Dozat and Manning, 2017](https://arxiv.org/pdf/1611.01734.pdf)
 
 * [biaffine-dependency-parser-ptb-2018.08.23.tar.gz](https://allennlp.s3.amazonaws.com/models/biaffine-dependency-parser-ptb-2018.08.23.tar.gz) (69 MB) uses [Penn Treebank](https://catalog.ldc.upenn.edu/ldc99t42) style dependencies.
 
-* [biaffine-dependency-parser-ud-2018.08.23.tar.gz](https://allennlp.s3.amazonaws.com/models/biaffine-dependency-parser-ud-2018.08.23.tar.gz) (61 MB) uses [Universal Dependency](http://universaldependencies.org/) style depedencies.
+* [biaffine-dependency-parser-ud-2018.08.23.tar.gz](https://allennlp.s3.amazonaws.com/models/biaffine-dependency-parser-ud-2018.08.23.tar.gz) (61 MB) uses [Universal Dependency](https://universaldependencies.org/) style depedencies.
 
 ```
 f1: 0.941

README.md

@@ -8,7 +8,7 @@ for developing state-of-the-art deep learning models on a wide variety of lingui
 
 ## Quick Links
 
-* [Website](http://www.allennlp.org/)
+* [Website](https://allennlp.org/)
 * [Tutorial](https://allennlp.org/tutorials)
 * [Documentation](https://allenai.github.io/allennlp-docs/)
 * [Contributing Guidelines](CONTRIBUTING.md)
@@ -96,7 +96,7 @@ AllenNLP installs a script when you install the python package, meaning you can
 You can now test your installation with `allennlp test-install`.
 
 _`pip` currently installs Pytorch for CUDA 9 only (or no GPU). If you require an older version,
-please visit http://pytorch.org/ and install the relevant pytorch binary._
+please visit https://pytorch.org/ and install the relevant pytorch binary._
 
 ### Installing using Docker
 

allennlp/commands/elmo.py

@@ -5,7 +5,7 @@
 layers used to compute ELMo representations to a single (potentially large) file.
 
 The input file is previously tokenized, whitespace separated text, one sentence per line.
-The output is a hdf5 file (<http://docs.h5py.org/en/latest/>) where, with the --all flag, each
+The output is a hdf5 file (<https://h5py.readthedocs.io/en/latest/>) where, with the --all flag, each
 sentence is a size (3, num_tokens, 1024) array with the biLM representations.
 
 For information, see "Deep contextualized word representations", Peters et al 2018.

allennlp/data/dataset_readers/dataset_utils/ontonotes.py

@@ -85,7 +85,7 @@ class Ontonotes:
     This DatasetReader is designed to read in the English OntoNotes v5.0 data
     in the format used by the CoNLL 2011/2012 shared tasks. In order to use this
     Reader, you must follow the instructions provided `here (v12 release):
-    <http://cemantix.org/data/ontonotes.html>`_, which will allow you to download
+    <https://cemantix.org/data/ontonotes.html>`_, which will allow you to download
     the CoNLL style annotations for the  OntoNotes v5.0 release -- LDC2013T19.tgz
     obtained from LDC.
 

allennlp/models/semantic_role_labeler.py

@@ -265,7 +265,7 @@ def write_to_conll_eval_file(prediction_file: TextIO,
     predicate in a sentence to two provided file references.
 
     The CoNLL SRL format is described in
-    `the shared task data README <http://www.lsi.upc.edu/~srlconll/conll05st-release/README>`_ .
+    `the shared task data README <https://www.lsi.upc.edu/~srlconll/conll05st-release/README>`_ .
 
     This function expects IOB2-formatted tags, where the B- tag is used in the beginning
     of every chunk (i.e. all chunks start with the B- tag).
@@ -309,7 +309,7 @@ def write_bio_formatted_tags_to_file(prediction_file: TextIO,
     predicate in a sentence to two provided file references.
 
     The CoNLL SRL format is described in
-    `the shared task data README <http://www.lsi.upc.edu/~srlconll/conll05st-release/README>`_ .
+    `the shared task data README <https://www.lsi.upc.edu/~srlconll/conll05st-release/README>`_ .
 
     This function expects IOB2-formatted tags, where the B- tag is used in the beginning
     of every chunk (i.e. all chunks start with the B- tag).
@@ -352,7 +352,7 @@ def write_conll_formatted_tags_to_file(prediction_file: TextIO,
     predicate in a sentence to two provided file references.
 
     The CoNLL SRL format is described in
-    `the shared task data README <http://www.lsi.upc.edu/~srlconll/conll05st-release/README>`_ .
+    `the shared task data README <https://www.lsi.upc.edu/~srlconll/conll05st-release/README>`_ .
 
     This function expects IOB2-formatted tags, where the B- tag is used in the beginning
     of every chunk (i.e. all chunks start with the B- tag).

allennlp/modules/__init__.py

@@ -1,6 +1,6 @@
 """
 Custom PyTorch
-`Module <http://pytorch.org/docs/master/nn.html#torch.nn.Module>`_ s
+`Module <https://pytorch.org/docs/master/nn.html#torch.nn.Module>`_ s
 that are used as components in AllenNLP
 :class:`~allennlp.models.model.Model` s.
 """

allennlp/modules/seq2seq_encoders/__init__.py

@@ -6,9 +6,9 @@
 
 The available Seq2Seq encoders are
 
-* `"gru" <http://pytorch.org/docs/master/nn.html#torch.nn.GRU>`_
-* `"lstm" <http://pytorch.org/docs/master/nn.html#torch.nn.LSTM>`_
-* `"rnn" <http://pytorch.org/docs/master/nn.html#torch.nn.RNN>`_
+* `"gru" <https://pytorch.org/docs/master/nn.html#torch.nn.GRU>`_
+* `"lstm" <https://pytorch.org/docs/master/nn.html#torch.nn.LSTM>`_
+* `"rnn" <https://pytorch.org/docs/master/nn.html#torch.nn.RNN>`_
 * :class:`"augmented_lstm" <allennlp.modules.augmented_lstm.AugmentedLstm>`
 * :class:`"alternating_lstm" <allennlp.modules.stacked_alternating_lstm.StackedAlternatingLstm>`
 * :class:`"alternating_highway_lstm" <allennlp.modules.stacked_alternating_lstm.StackedAlternatingLstm> (GPU only)`

allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py

@@ -1,6 +1,6 @@
 """
 The BidirectionalTransformerEncoder from Calypso.
-This is basically the transformer from http://nlp.seas.harvard.edu/2018/04/03/attention.html
+This is basically the transformer from https://nlp.seas.harvard.edu/2018/04/03/attention.html
 so credit to them.
 
 This code should be considered "private" in that we have several

allennlp/modules/seq2vec_encoders/__init__.py

@@ -6,9 +6,9 @@
 
 The available Seq2Vec encoders are
 
-* `"gru" <http://pytorch.org/docs/master/nn.html#torch.nn.GRU>`_
-* `"lstm" <http://pytorch.org/docs/master/nn.html#torch.nn.LSTM>`_
-* `"rnn" <http://pytorch.org/docs/master/nn.html#torch.nn.RNN>`_
+* `"gru" <https://pytorch.org/docs/master/nn.html#torch.nn.GRU>`_
+* `"lstm" <https://pytorch.org/docs/master/nn.html#torch.nn.LSTM>`_
+* `"rnn" <https://pytorch.org/docs/master/nn.html#torch.nn.RNN>`_
 * :class:`"cnn" <allennlp.modules.seq2vec_encoders.cnn_encoder.CnnEncoder>`
 * :class:`"augmented_lstm" <allennlp.modules.augmented_lstm.AugmentedLstm>`
 * :class:`"alternating_lstm" <allennlp.modules.stacked_alternating_lstm.StackedAlternatingLstm>`

allennlp/modules/token_embedders/embedding.py

@@ -272,7 +272,7 @@ def from_params(cls, vocab: Vocabulary, params: Params) -> 'Embedding':  # type:
 
               where ``archive_uri`` can be a file system path or a URL. For example::
 
-                    "(http://nlp.stanford.edu/data/glove.twitter.27B.zip)#glove.twitter.27B.200d.txt"
+                    "(https://nlp.stanford.edu/data/glove.twitter.27B.zip)#glove.twitter.27B.200d.txt"
         """
         # pylint: disable=arguments-differ
         num_embeddings = params.pop_int('num_embeddings', None)

allennlp/nn/activations.py

@@ -2,26 +2,26 @@
 An :class:`Activation` is just a function
 that takes some parameters and returns an element-wise activation function.
 For the most part we just use
-`PyTorch activations <http://pytorch.org/docs/master/nn.html#non-linear-activations>`_.
+`PyTorch activations <https://pytorch.org/docs/master/nn.html#non-linear-activations>`_.
 Here we provide a thin wrapper to allow registering them and instantiating them ``from_params``.
 
 The available activation functions are
 
 * "linear"
-* `"relu" <http://pytorch.org/docs/master/nn.html#torch.nn.ReLU>`_
-* `"relu6" <http://pytorch.org/docs/master/nn.html#torch.nn.ReLU6>`_
-* `"elu" <http://pytorch.org/docs/master/nn.html#torch.nn.ELU>`_
-* `"prelu" <http://pytorch.org/docs/master/nn.html#torch.nn.PReLU>`_
-* `"leaky_relu" <http://pytorch.org/docs/master/nn.html#torch.nn.LeakyReLU>`_
-* `"threshold" <http://pytorch.org/docs/master/nn.html#torch.nn.Threshold>`_
-* `"hardtanh" <http://pytorch.org/docs/master/nn.html#torch.nn.Hardtanh>`_
-* `"sigmoid" <http://pytorch.org/docs/master/nn.html#torch.nn.Sigmoid>`_
-* `"tanh" <http://pytorch.org/docs/master/nn.html#torch.nn.Tanh>`_
-* `"log_sigmoid" <http://pytorch.org/docs/master/nn.html#torch.nn.LogSigmoid>`_
-* `"softplus" <http://pytorch.org/docs/master/nn.html#torch.nn.Softplus>`_
-* `"softshrink" <http://pytorch.org/docs/master/nn.html#torch.nn.Softshrink>`_
-* `"softsign" <http://pytorch.org/docs/master/nn.html#torch.nn.Softsign>`_
-* `"tanhshrink" <http://pytorch.org/docs/master/nn.html#torch.nn.Tanhshrink>`_
+* `"relu" <https://pytorch.org/docs/master/nn.html#torch.nn.ReLU>`_
+* `"relu6" <https://pytorch.org/docs/master/nn.html#torch.nn.ReLU6>`_
+* `"elu" <https://pytorch.org/docs/master/nn.html#torch.nn.ELU>`_
+* `"prelu" <https://pytorch.org/docs/master/nn.html#torch.nn.PReLU>`_
+* `"leaky_relu" <https://pytorch.org/docs/master/nn.html#torch.nn.LeakyReLU>`_
+* `"threshold" <https://pytorch.org/docs/master/nn.html#torch.nn.Threshold>`_
+* `"hardtanh" <https://pytorch.org/docs/master/nn.html#torch.nn.Hardtanh>`_
+* `"sigmoid" <https://pytorch.org/docs/master/nn.html#torch.nn.Sigmoid>`_
+* `"tanh" <https://pytorch.org/docs/master/nn.html#torch.nn.Tanh>`_
+* `"log_sigmoid" <https://pytorch.org/docs/master/nn.html#torch.nn.LogSigmoid>`_
+* `"softplus" <https://pytorch.org/docs/master/nn.html#torch.nn.Softplus>`_
+* `"softshrink" <https://pytorch.org/docs/master/nn.html#torch.nn.Softshrink>`_
+* `"softsign" <https://pytorch.org/docs/master/nn.html#torch.nn.Softsign>`_
+* `"tanhshrink" <https://pytorch.org/docs/master/nn.html#torch.nn.Tanhshrink>`_
 """
 
 import torch

allennlp/nn/beam_search.py

@@ -28,7 +28,7 @@ class BeamSearch:
         If not given, this just defaults to ``beam_size``. Setting this parameter
         to a number smaller than ``beam_size`` may give better results, as it can introduce
         more diversity into the search. See `Beam Search Strategies for Neural Machine Translation.
-        Freitag and Al-Onaizan, 2017 <http://arxiv.org/abs/1702.01806>`_.
+        Freitag and Al-Onaizan, 2017 <https://arxiv.org/abs/1702.01806>`_.
     """
 
     def __init__(self,

allennlp/nn/initializers.py

@@ -7,17 +7,18 @@
 
 The available initialization functions are
 
-* `"normal" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.normal_>`_
-* `"uniform" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.uniform_>`_
-* `"constant" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.constant_>`_
-* `"eye" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.eye_>`_
-* `"dirac" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.dirac_>`_
-* `"xavier_uniform" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.xavier_uniform_>`_
-* `"xavier_normal" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.xavier_normal_>`_
-* `"kaiming_uniform" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.kaiming_uniform_>`_
-* `"kaiming_normal" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.kaiming_normal_>`_
-* `"orthogonal" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.orthogonal_>`_
-* `"sparse" <http://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.sparse_>`_
+* `"normal" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.normal_>`_
+* `"uniform" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.uniform_>`_
+* `"constant" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.constant_>`_
+* `"eye" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.eye_>`_
+* `"dirac" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.dirac_>`_
+* `"xavier_uniform" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.xavier_uniform_>`_
+* `"xavier_normal" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.xavier_normal_>`_
+* `"kaiming_uniform"
+  <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.kaiming_uniform_>`_
+* `"kaiming_normal" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.kaiming_normal_>`_
+* `"orthogonal" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.orthogonal_>`_
+* `"sparse" <https://pytorch.org/docs/master/nn.html?highlight=orthogonal#torch.nn.init.sparse_>`_
 * :func:`"block_orthogonal" <block_orthogonal>`
 * :func:`"uniform_unit_scaling" <uniform_unit_scaling>`
 * :class:`"pretrained" <PretrainedModelInitializer>`

allennlp/service/server_simple.py

@@ -1,5 +1,5 @@
 """
-A `Flask <http://flask.pocoo.org/>`_ server for serving predictions
+A `Flask <https://palletsprojects.com/p/flask/>`_ server for serving predictions
 from a single AllenNLP model. It also includes a very, very bare-bones
 web front-end for exploring predictions (or you can provide your own).
 

allennlp/tests/data/dataset_readers/universal_dependencies_dataset_reader_test.py

@@ -48,7 +48,7 @@ def test_read_from_file(self):
         assert fields["head_indices"].labels == [4, 4, 4, 0, 6, 4, 6, 6, 4]
 
         # This instance tests specifically for filtering of elipsis:
-        # http://universaldependencies.org/u/overview/specific-syntax.html#ellipsis
+        # https://universaldependencies.org/u/overview/specific-syntax.html#ellipsis
         # The original sentence is:
         # "Over 300 Iraqis are reported dead and 500 [reported] wounded in Fallujah alone."
         # But the second "reported" is elided, and as such isn't included in the syntax tree.

allennlp/tests/training/callback_trainer_test.py

@@ -207,7 +207,7 @@ def test_trainer_can_run(self):
 
     @responses.activate
     def test_trainer_posts_to_url(self):
-        url = 'http://slack.com?webhook=ewifjweoiwjef'
+        url = 'https://slack.com?webhook=ewifjweoiwjef'
         responses.add(responses.POST, url)
         post_to_url = PostToUrl(url, message="only a test")
         callbacks = self.default_callbacks() + [post_to_url]

allennlp/tools/EVALB/LICENSE

@@ -21,4 +21,4 @@ OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
 ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
 OTHER DEALINGS IN THE SOFTWARE.
 
-For more information, please refer to <http://unlicense.org/>
+For more information, please refer to <https://unlicense.org/>

allennlp/tools/EVALB/evalb.dSYM/Contents/Info.plist

@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple Computer//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
+<!DOCTYPE plist PUBLIC "-//Apple Computer//DTD PLIST 1.0//EN" "https://www.apple.com/DTDs/PropertyList-1.0.dtd">
 <plist version="1.0">
 	<dict>
 		<key>CFBundleDevelopmentRegion</key>

allennlp/training/learning_rate_schedulers/__init__.py

@@ -1,14 +1,14 @@
 """
 AllenNLP uses most
-`PyTorch learning rate schedulers <http://pytorch.org/docs/master/optim.html#how-to-adjust-learning-rate>`_,
+`PyTorch learning rate schedulers <https://pytorch.org/docs/master/optim.html#how-to-adjust-learning-rate>`_,
 with a thin wrapper to allow registering them and instantiating them ``from_params``.
 
 The available learning rate schedulers from PyTorch are
 
-* `"step" <http://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.StepLR>`_
-* `"multi_step" <http://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.MultiStepLR>`_
-* `"exponential" <http://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.ExponentialLR>`_
-* `"reduce_on_plateau" <http://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau>`_
+* `"step" <https://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.StepLR>`_
+* `"multi_step" <https://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.MultiStepLR>`_
+* `"exponential" <https://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.ExponentialLR>`_
+* `"reduce_on_plateau" <https://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau>`_
 
 In addition, AllenNLP also provides `cosine with restarts <https://arxiv.org/abs/1608.03983>`_,
 a Noam schedule, and a slanted triangular schedule, which are registered as

allennlp/training/metrics/conll_coref_scores.py

@@ -189,7 +189,7 @@ def muc(clusters, mention_to_gold):
         """
         Counts the mentions in each predicted cluster which need to be re-allocated in
         order for each predicted cluster to be contained by the respective gold cluster.
-        <http://aclweb.org/anthology/M/M95/M95-1005.pdf>
+        <https://aclweb.org/anthology/M/M95/M95-1005.pdf>
         """
         true_p, all_p = 0, 0
         for cluster in clusters:

allennlp/training/metrics/evalb_bracketing_scorer.py

@@ -21,7 +21,7 @@ class EvalbBracketingScorer(Metric):
     """
     This class uses the external EVALB software for computing a broad range of metrics
     on parse trees. Here, we use it to compute the Precision, Recall and F1 metrics.
-    You can download the source for EVALB from here: <http://nlp.cs.nyu.edu/evalb/>.
+    You can download the source for EVALB from here: <https://nlp.cs.nyu.edu/evalb/>.
 
     Note that this software is 20 years old. In order to compile it on modern hardware,
     you may need to remove an ``include <malloc.h>`` statement in ``evalb.c`` before it

allennlp/training/optimizers.py

@@ -1,18 +1,18 @@
 """
 AllenNLP just uses
-`PyTorch optimizers <http://pytorch.org/docs/master/optim.html>`_ ,
+`PyTorch optimizers <https://pytorch.org/docs/master/optim.html>`_ ,
 with a thin wrapper to allow registering them and instantiating them ``from_params``.
 
 The available optimizers are
 
-* `"adadelta" <http://pytorch.org/docs/master/optim.html#torch.optim.Adadelta>`_
-* `"adagrad" <http://pytorch.org/docs/master/optim.html#torch.optim.Adagrad>`_
-* `"adam" <http://pytorch.org/docs/master/optim.html#torch.optim.Adam>`_
-* `"sparse_adam" <http://pytorch.org/docs/master/optim.html#torch.optim.SparseAdam>`_
-* `"sgd" <http://pytorch.org/docs/master/optim.html#torch.optim.SGD>`_
-* `"rmsprop <http://pytorch.org/docs/master/optim.html#torch.optim.RMSprop>`_
-* `"adamax <http://pytorch.org/docs/master/optim.html#torch.optim.Adamax>`_
-* `"averaged_sgd <http://pytorch.org/docs/master/optim.html#torch.optim.ASGD>`_
+* `"adadelta" <https://pytorch.org/docs/master/optim.html#torch.optim.Adadelta>`_
+* `"adagrad" <https://pytorch.org/docs/master/optim.html#torch.optim.Adagrad>`_
+* `"adam" <https://pytorch.org/docs/master/optim.html#torch.optim.Adam>`_
+* `"sparse_adam" <https://pytorch.org/docs/master/optim.html#torch.optim.SparseAdam>`_
+* `"sgd" <https://pytorch.org/docs/master/optim.html#torch.optim.SGD>`_
+* `"rmsprop <https://pytorch.org/docs/master/optim.html#torch.optim.RMSprop>`_
+* `"adamax <https://pytorch.org/docs/master/optim.html#torch.optim.Adamax>`_
+* `"averaged_sgd <https://pytorch.org/docs/master/optim.html#torch.optim.ASGD>`_
 """
 
 import logging
@@ -52,7 +52,7 @@ def from_params(cls, model_parameters: List, params: Params):  # type: ignore
             # e.g., {'params': [list of parameters], 'lr': 1e-3, ...}
             # Any config option not specified in the additional options (e.g.
             # for the default group) is inherited from the top level config.
-            # see: http://pytorch.org/docs/0.3.0/optim.html?#per-parameter-options
+            # see: https://pytorch.org/docs/0.3.0/optim.html?#per-parameter-options
             #
             # groups contains something like:
             #"parameter_groups": [