code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def read_vocab(filename):
"""Reads vocab file into the memory and adds special-vocab to it.
Args:
filename: Path to a vocabulary file containg one word per line.
Each word is mapped to its line number.
Returns:
A tuple (vocab, counts, special_vocab)
"""
tf.logging.info("Reading vocabulary from... | Reads vocab file into the memory and adds special-vocab to it.
Args:
filename: Path to a vocabulary file containg one word per line.
Each word is mapped to its line number.
Returns:
A tuple (vocab, counts, special_vocab)
| read_vocab | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/data/vocab.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/data/vocab.py | Apache-2.0 |
def create_vocabulary_lookup_table(filename, default_value=None):
"""Creates a lookup table for a vocabulary file.
Args:
filename: Path to a vocabulary file containg one word per line.
Each word is mapped to its line number.
default_value: UNK tokens will be mapped to this id.
If None, UNK token... | Creates a lookup table for a vocabulary file.
Args:
filename: Path to a vocabulary file containg one word per line.
Each word is mapped to its line number.
default_value: UNK tokens will be mapped to this id.
If None, UNK tokens will be mapped to [vocab_size]
Returns:
A tuple (vocab_to_i... | create_vocabulary_lookup_table | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/data/vocab.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/data/vocab.py | Apache-2.0 |
def _build(self, query, keys, values, values_length):
"""Computes attention scores and outputs.
Args:
query: The query used to calculate attention scores.
In seq2seq this is typically the current state of the decoder.
A tensor of shape `[B, ...]`
keys: The keys used to calculate att... | Computes attention scores and outputs.
Args:
query: The query used to calculate attention scores.
In seq2seq this is typically the current state of the decoder.
A tensor of shape `[B, ...]`
keys: The keys used to calculate attention scores. In seq2seq, these
are typically the ou... | _build | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/decoders/attention.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/decoders/attention.py | Apache-2.0 |
def att_next_inputs(time, outputs, state, sample_ids, name=None):
"""Wraps the original decoder helper function to append the attention
context.
"""
finished, next_inputs, next_state = helper.next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids... | Wraps the original decoder helper function to append the attention
context.
| att_next_inputs | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/decoders/attention_decoder.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/decoders/attention_decoder.py | Apache-2.0 |
def _setup(self, initial_state, helper):
"""Sets the initial state and helper for the decoder.
"""
self.initial_state = initial_state
self.helper = helper | Sets the initial state and helper for the decoder.
| _setup | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/decoders/rnn_decoder.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/decoders/rnn_decoder.py | Apache-2.0 |
def finalize(self, outputs, final_state):
"""Applies final transformation to the decoder output once decoding is
finished.
"""
#pylint: disable=R0201
return (outputs, final_state) | Applies final transformation to the decoder output once decoding is
finished.
| finalize | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/decoders/rnn_decoder.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/decoders/rnn_decoder.py | Apache-2.0 |
def position_encoding(sentence_size, embedding_size):
"""
Position Encoding described in section 4.1 of
End-To-End Memory Networks (https://arxiv.org/abs/1503.08895).
Args:
sentence_size: length of the sentence
embedding_size: dimensionality of the embeddings
Returns:
A numpy array of shape [sen... |
Position Encoding described in section 4.1 of
End-To-End Memory Networks (https://arxiv.org/abs/1503.08895).
Args:
sentence_size: length of the sentence
embedding_size: dimensionality of the embeddings
Returns:
A numpy array of shape [sentence_size, embedding_size] containing
the fixed positi... | position_encoding | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/encoders/pooling_encoder.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/encoders/pooling_encoder.py | Apache-2.0 |
def _create_position_embedding(embedding_dim, num_positions, lengths, maxlen):
"""Creates position embeddings.
Args:
embedding_dim: Dimensionality of the embeddings. An integer.
num_positions: The number of positions to be embedded. For example,
if you have inputs of length up to 100, this should be ... | Creates position embeddings.
Args:
embedding_dim: Dimensionality of the embeddings. An integer.
num_positions: The number of positions to be embedded. For example,
if you have inputs of length up to 100, this should be 100. An integer.
lengths: The lengths of the inputs to create position embedding... | _create_position_embedding | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/encoders/pooling_encoder.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/encoders/pooling_encoder.py | Apache-2.0 |
def _unpack_cell(cell):
"""Unpack the cells because the stack_bidirectional_dynamic_rnn
expects a list of cells, one per layer."""
if isinstance(cell, tf.contrib.rnn.MultiRNNCell):
return cell._cells #pylint: disable=W0212
else:
return [cell] | Unpack the cells because the stack_bidirectional_dynamic_rnn
expects a list of cells, one per layer. | _unpack_cell | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/encoders/rnn_encoder.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/encoders/rnn_encoder.py | Apache-2.0 |
def _default_rnn_cell_params():
"""Creates default parameters used by multiple RNN encoders.
"""
return {
"cell_class": "BasicLSTMCell",
"cell_params": {
"num_units": 128
},
"dropout_input_keep_prob": 1.0,
"dropout_output_keep_prob": 1.0,
"num_layers": 1,
"resid... | Creates default parameters used by multiple RNN encoders.
| _default_rnn_cell_params | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/encoders/rnn_encoder.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/encoders/rnn_encoder.py | Apache-2.0 |
def _toggle_dropout(cell_params, mode):
"""Disables dropout during eval/inference mode
"""
cell_params = copy.deepcopy(cell_params)
if mode != tf.contrib.learn.ModeKeys.TRAIN:
cell_params["dropout_input_keep_prob"] = 1.0
cell_params["dropout_output_keep_prob"] = 1.0
return cell_params | Disables dropout during eval/inference mode
| _toggle_dropout | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/encoders/rnn_encoder.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/encoders/rnn_encoder.py | Apache-2.0 |
def gather_tree_py(values, parents):
"""Gathers path through a tree backwards from the leave nodes. Used
to reconstruct beams given their parents."""
beam_length = values.shape[0]
num_beams = values.shape[1]
res = np.zeros_like(values)
res[-1, :] = values[-1, :]
for beam_id in range(num_beams):
parent... | Gathers path through a tree backwards from the leave nodes. Used
to reconstruct beams given their parents. | gather_tree_py | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | Apache-2.0 |
def create_initial_beam_state(config):
"""Creates an instance of `BeamState` that can be used on the first
call to `beam_step`.
Args:
config: A BeamSearchConfig
Returns:
An instance of `BeamState`.
"""
return BeamSearchState(
log_probs=tf.zeros([config.beam_width]),
finished=tf.zeros(
... | Creates an instance of `BeamState` that can be used on the first
call to `beam_step`.
Args:
config: A BeamSearchConfig
Returns:
An instance of `BeamState`.
| create_initial_beam_state | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | Apache-2.0 |
def hyp_score(log_probs, sequence_lengths, config):
"""Calculates scores for beam search hypotheses.
"""
# Calculate the length penality
length_penality_ = length_penalty(
sequence_lengths=sequence_lengths,
penalty_factor=config.length_penalty_weight)
score = log_probs / length_penality_
retur... | Calculates scores for beam search hypotheses.
| hyp_score | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | Apache-2.0 |
def choose_top_k(scores_flat, config):
"""Chooses the top-k beams as successors.
"""
next_beam_scores, word_indices = tf.nn.top_k(scores_flat, k=config.beam_width)
return next_beam_scores, word_indices | Chooses the top-k beams as successors.
| choose_top_k | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | Apache-2.0 |
def nest_map(inputs, map_fn, name=None):
"""Applies a function to (possibly nested) tuple of tensors.
"""
if nest.is_sequence(inputs):
inputs_flat = nest.flatten(inputs)
y_flat = [map_fn(_) for _ in inputs_flat]
outputs = nest.pack_sequence_as(inputs, y_flat)
else:
outputs = map_fn(inputs)
if ... | Applies a function to (possibly nested) tuple of tensors.
| nest_map | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | Apache-2.0 |
def mask_probs(probs, eos_token, finished):
"""Masks log probabilities such that finished beams
allocate all probability mass to eos. Unfinished beams remain unchanged.
Args:
probs: Log probabiltiies of shape `[beam_width, vocab_size]`
eos_token: An int32 id corresponding to the EOS token to allocate
... | Masks log probabilities such that finished beams
allocate all probability mass to eos. Unfinished beams remain unchanged.
Args:
probs: Log probabiltiies of shape `[beam_width, vocab_size]`
eos_token: An int32 id corresponding to the EOS token to allocate
probability to
finished: A boolean tensor ... | mask_probs | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | Apache-2.0 |
def beam_search_step(time_, logits, beam_state, config):
"""Performs a single step of Beam Search Decoding.
Args:
time_: Beam search time step, should start at 0. At time 0 we assume
that all beams are equal and consider only the first beam for
continuations.
logits: Logits at the current time ... | Performs a single step of Beam Search Decoding.
Args:
time_: Beam search time step, should start at 0. At time 0 we assume
that all beams are equal and consider only the first beam for
continuations.
logits: Logits at the current time step. A tensor of shape `[B, vocab_size]`
beam_state: Curr... | beam_search_step | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/inference/beam_search.py | Apache-2.0 |
def create_inference_graph(model, input_pipeline, batch_size=32):
"""Creates a graph to perform inference.
Args:
task: An `InferenceTask` instance.
input_pipeline: An instance of `InputPipeline` that defines
how to read and parse data.
batch_size: The batch size used for inference
Returns:
... | Creates a graph to perform inference.
Args:
task: An `InferenceTask` instance.
input_pipeline: An instance of `InputPipeline` that defines
how to read and parse data.
batch_size: The batch size used for inference
Returns:
The return value of the model function, typically a tuple of
(pred... | create_inference_graph | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/inference/inference.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/inference/inference.py | Apache-2.0 |
def moses_multi_bleu(hypotheses, references, lowercase=False):
"""Calculate the bleu score for hypotheses and references
using the MOSES ulti-bleu.perl script.
Args:
hypotheses: A numpy array of strings where each string is a single example.
references: A numpy array of strings where each string is a sin... | Calculate the bleu score for hypotheses and references
using the MOSES ulti-bleu.perl script.
Args:
hypotheses: A numpy array of strings where each string is a single example.
references: A numpy array of strings where each string is a single example.
lowercase: If true, pass the "-lc" flag to the mult... | moses_multi_bleu | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/metrics/bleu.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/metrics/bleu.py | Apache-2.0 |
def accumulate_strings(values, name="strings"):
"""Accumulates strings into a vector.
Args:
values: A 1-d string tensor that contains values to add to the accumulator.
Returns:
A tuple (value_tensor, update_op).
"""
tf.assert_type(values, tf.string)
strings = tf.Variable(
name=name,
in... | Accumulates strings into a vector.
Args:
values: A 1-d string tensor that contains values to add to the accumulator.
Returns:
A tuple (value_tensor, update_op).
| accumulate_strings | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/metrics/metric_specs.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/metrics/metric_specs.py | Apache-2.0 |
def _py_func(self, hypotheses, references):
"""Wrapper function that converts tensors to unicode and slices
them until the EOS token is found.
"""
# Deal with byte chars
if hypotheses.dtype.kind == np.dtype("U"):
hypotheses = np.char.encode(hypotheses, "utf-8")
if references.dtype.kind =... | Wrapper function that converts tensors to unicode and slices
them until the EOS token is found.
| _py_func | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/metrics/metric_specs.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/metrics/metric_specs.py | Apache-2.0 |
def compute_loss(self, decoder_output, _features, labels):
"""Computes the sequence loss for this model.
seq_loss is the cross entropy loss for the output sequence.
Returns a tuple `(losses, loss)`, where `losses` are the per-batch
losses and loss is a single scalar tensor to minimize.
... | Computes the sequence loss for this model.
seq_loss is the cross entropy loss for the output sequence.
Returns a tuple `(losses, loss)`, where `losses` are the per-batch
losses and loss is a single scalar tensor to minimize.
| compute_loss | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | Apache-2.0 |
def _preprocess(self, features, labels):
"""Model-specific preprocessing for features and labels:
- Creates vocabulary lookup tables for source and target vocab
- Converts tokens into vocabulary ids
- Trims copy indices to target.max_seq_len
"""
features, labels = super(S... | Model-specific preprocessing for features and labels:
- Creates vocabulary lookup tables for source and target vocab
- Converts tokens into vocabulary ids
- Trims copy indices to target.max_seq_len
| _preprocess | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | Apache-2.0 |
def compute_loss(self, decoder_output, _features, labels):
"""Computes the loss for this model.
Loss = seq_loss + schema_copy_loss.
seq_loss is the cross entropy loss for the output sequence.
schema_copy_loss is zero at any time step where output is not
copy_schema, and the cross... | Computes the loss for this model.
Loss = seq_loss + schema_copy_loss.
seq_loss is the cross entropy loss for the output sequence.
schema_copy_loss is zero at any time step where output is not
copy_schema, and the cross entropy loss for the schema attention
score otherwise.
... | compute_loss | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | Apache-2.0 |
def compute_loss(self, decoder_output, _features, labels):
"""Computes the loss for this model.
Loss = seq_loss + word_copy_loss.
seq_loss is the cross entropy loss for the output sequence.
word_copy_loss is zero at any time step where output is not copy_word,
and the cross entro... | Computes the loss for this model.
Loss = seq_loss + word_copy_loss.
seq_loss is the cross entropy loss for the output sequence.
word_copy_loss is zero at any time step where output is not copy_word,
and the cross entropy loss for the input attention score otherwise.
Returns a tup... | compute_loss | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | Apache-2.0 |
def compute_loss(self, decoder_output, _features, labels):
"""Computes the loss for this model.
Loss = seq_loss + schema_copy_loss + word_copy_loss.
seq_loss is the cross entropy loss for the output sequence.
word_copy_loss is zero at any time step where output is not copy_word,
... | Computes the loss for this model.
Loss = seq_loss + schema_copy_loss + word_copy_loss.
seq_loss is the cross entropy loss for the output sequence.
word_copy_loss is zero at any time step where output is not copy_word,
and the cross entropy loss for the input attention score otherwise.
... | compute_loss | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/attention_copying_seq2seq.py | Apache-2.0 |
def _create_bridge(self, encoder_outputs, decoder_state_size):
"""Creates the bridge to be used between encoder and decoder"""
bridge_class = locate(self.params["bridge.class"]) or \
getattr(bridges, self.params["bridge.class"])
return bridge_class(
encoder_outputs=encoder_outputs,
dec... | Creates the bridge to be used between encoder and decoder | _create_bridge | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/basic_seq2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/basic_seq2seq.py | Apache-2.0 |
def _create_decoder(self, _encoder_output, _features, _labels):
"""Creates a decoder instance based on the passed parameters."""
decoder_mask = _features["decoder_mask"]
return self.decoder_class(
params=self.params["decoder.params"],
mode=self.mode,
vocab_size=self.target_vocab... | Creates a decoder instance based on the passed parameters. | _create_decoder | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/basic_seq2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/basic_seq2seq.py | Apache-2.0 |
def _preprocess(self, features, labels):
"""Model-specific preprocessing for features and labels:
- Creates vocabulary lookup tables for target vocab
- Converts tokens into vocabulary ids
- Prepends a speical "SEQUENCE_START" token to the target
- Appends a speical "SEQUENCE_END" token to the targe... | Model-specific preprocessing for features and labels:
- Creates vocabulary lookup tables for target vocab
- Converts tokens into vocabulary ids
- Prepends a speical "SEQUENCE_START" token to the target
- Appends a speical "SEQUENCE_END" token to the target
| _preprocess | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/image2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/image2seq.py | Apache-2.0 |
def _flatten_dict(dict_, parent_key="", sep="."):
"""Flattens a nested dictionary. Namedtuples within
the dictionary are converted to dicts.
Args:
dict_: The dictionary to flatten.
parent_key: A prefix to prepend to each key.
sep: Separator between parent and child keys, a string. For example
{... | Flattens a nested dictionary. Namedtuples within
the dictionary are converted to dicts.
Args:
dict_: The dictionary to flatten.
parent_key: A prefix to prepend to each key.
sep: Separator between parent and child keys, a string. For example
{ "a": { "b": 3 } } will become { "a.b": 3 } if the sepa... | _flatten_dict | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/model_base.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/model_base.py | Apache-2.0 |
def default_params():
"""Returns a dictionary of default parameters for this model."""
return {
"optimizer.name": "Adam",
"optimizer.learning_rate": 1e-4,
"optimizer.params": {}, # Arbitrary parameters for the optimizer
"optimizer.lr_decay_type": "",
"optimizer.lr_decay_s... | Returns a dictionary of default parameters for this model. | default_params | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/model_base.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/model_base.py | Apache-2.0 |
def __call__(self, features, labels, params):
"""Creates the model graph. See the model_fn documentation in
tf.contrib.learn.Estimator class for a more detailed explanation.
"""
with tf.variable_scope("model"):
with tf.variable_scope(self.name):
return self._build(features, labels, params) | Creates the model graph. See the model_fn documentation in
tf.contrib.learn.Estimator class for a more detailed explanation.
| __call__ | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/model_base.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/model_base.py | Apache-2.0 |
def compute_loss(self, decoder_output, _features, labels):
"""Computes the loss for this model.
Loss = seq_loss + schema_copy_loss.
seq_loss is the cross entropy loss for the output sequence.
# word_copy_loss is zero at any time step where output is not copy_word,
and the cross en... | Computes the loss for this model.
Loss = seq_loss + schema_copy_loss.
seq_loss is the cross entropy loss for the output sequence.
# word_copy_loss is zero at any time step where output is not copy_word,
and the cross entropy loss for the input attention score otherwise.
schema_cop... | compute_loss | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/schema_attention_copying_seq2seq.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/schema_attention_copying_seq2seq.py | Apache-2.0 |
def _clip_gradients(self, grads_and_vars):
"""In addition to standard gradient clipping, also clips embedding
gradients to a specified value."""
grads_and_vars = super(Seq2SeqModel, self)._clip_gradients(grads_and_vars)
clipped_gradients = []
variables = []
for gradient, variable in grads_and_v... | In addition to standard gradient clipping, also clips embedding
gradients to a specified value. | _clip_gradients | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | Apache-2.0 |
def _create_predictions(self, decoder_output, features, labels, losses=None):
"""Creates the dictionary of predictions that is returned by the model.
"""
predictions = {}
# Add features and, if available, labels to predictions
predictions.update(_flatten_dict({"features": features}))
if labels ... | Creates the dictionary of predictions that is returned by the model.
| _create_predictions | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | Apache-2.0 |
def source_embedding(self):
"""Returns the embedding used for the source sequence.
"""
# return tf.get_variable(
# name="W",
# shape=[self.source_vocab_info.total_size, self.params["embedding.dim"]],
# initializer=tf.random_uniform_initializer(
# -self.params["embedding.i... | Returns the embedding used for the source sequence.
| source_embedding | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | Apache-2.0 |
def target_embedding(self):
"""Returns the embedding used for the target sequence.
"""
# if self.params["embedding.share"]:
# return self.source_embedding
# return tf.get_variable(
# name="W",
# shape=[self.target_vocab_info.total_size, self.params["embedding.dim"]],
# init... | Returns the embedding used for the target sequence.
| target_embedding | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | Apache-2.0 |
def _get_beam_search_decoder(self, decoder):
"""Wraps a decoder into a Beam Search decoder.
Args:
decoder: The original decoder
Returns:
A BeamSearchDecoder with the same interfaces as the original decoder.
"""
config = beam_search.BeamSearchConfig(
beam_width=self.params["infe... | Wraps a decoder into a Beam Search decoder.
Args:
decoder: The original decoder
Returns:
A BeamSearchDecoder with the same interfaces as the original decoder.
| _get_beam_search_decoder | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | Apache-2.0 |
def _preprocess(self, features, labels):
"""Model-specific preprocessing for features and labels:
- Creates vocabulary lookup tables for source and target vocab
- Converts tokens into vocabulary ids
"""
# Create vocabulary lookup for source
source_vocab_to_id, source_id_to_vocab, source_word_t... | Model-specific preprocessing for features and labels:
- Creates vocabulary lookup tables for source and target vocab
- Converts tokens into vocabulary ids
| _preprocess | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | Apache-2.0 |
def compute_loss(self, decoder_output, _features, labels):
"""Computes the loss for this model.
Returns a tuple `(losses, loss)`, where `losses` are the per-batch
losses and loss is a single scalar tensor to minimize.
"""
#pylint: disable=R0201
# Calculate loss per example-timestep of shape [B,... | Computes the loss for this model.
Returns a tuple `(losses, loss)`, where `losses` are the per-batch
losses and loss is a single scalar tensor to minimize.
| compute_loss | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/models/seq2seq_model.py | Apache-2.0 |
def _get_prediction_length(predictions_dict):
"""Returns the length of the prediction based on the index
of the first SEQUENCE_END token.
"""
tokens_iter = enumerate(predictions_dict["predicted_tokens"])
return next(((i + 1) for i, _ in tokens_iter if _ == "SEQUENCE_END"),
len(predictions_dict["... | Returns the length of the prediction based on the index
of the first SEQUENCE_END token.
| _get_prediction_length | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/tasks/decode_text.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/tasks/decode_text.py | Apache-2.0 |
def _get_unk_mapping(filename):
"""Reads a file that specifies a mapping from source to target tokens.
The file must contain lines of the form <source>\t<target>"
Args:
filename: path to the mapping file
Returns:
A dictionary that maps from source -> target tokens.
"""
with gfile.GFile(filename, "... | Reads a file that specifies a mapping from source to target tokens.
The file must contain lines of the form <source> <target>"
Args:
filename: path to the mapping file
Returns:
A dictionary that maps from source -> target tokens.
| _get_unk_mapping | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/tasks/decode_text.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/tasks/decode_text.py | Apache-2.0 |
def _unk_replace(source_tokens,
predicted_tokens,
attention_scores,
mapping=None):
"""Replaces UNK tokens with tokens from the source or a
provided mapping based on the attention scores.
Args:
source_tokens: A numpy array of strings.
predicted_tokens: A ... | Replaces UNK tokens with tokens from the source or a
provided mapping based on the attention scores.
Args:
source_tokens: A numpy array of strings.
predicted_tokens: A numpy array of strings.
attention_scores: A numeric numpy array
of shape `[prediction_length, source_length]` that contains
... | _unk_replace | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/tasks/decode_text.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/tasks/decode_text.py | Apache-2.0 |
def _get_scores(predictions_dict):
"""Returns the attention scores, sliced by source and target length.
"""
prediction_len = _get_prediction_length(predictions_dict)
source_len = predictions_dict["features.source_len"]
return predictions_dict["attention_scores"][:prediction_len, :source_len] | Returns the attention scores, sliced by source and target length.
| _get_scores | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/tasks/dump_attention.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/tasks/dump_attention.py | Apache-2.0 |
def _create_figure(predictions_dict):
"""Creates and returns a new figure that visualizes
attention scores for for a single model predictions.
"""
# Find out how long the predicted sequence is
target_words = list(predictions_dict["predicted_tokens"])
prediction_len = _get_prediction_length(predictions_dic... | Creates and returns a new figure that visualizes
attention scores for for a single model predictions.
| _create_figure | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/tasks/dump_attention.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/tasks/dump_attention.py | Apache-2.0 |
def unbatch_dict(dict_):
"""Converts a dictionary of batch items to a batch/list of
dictionary items.
"""
batch_size = list(dict_.values())[0].shape[0]
for i in range(batch_size):
yield {key: value[i] for key, value in dict_.items()} | Converts a dictionary of batch items to a batch/list of
dictionary items.
| unbatch_dict | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/tasks/inference_task.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/tasks/inference_task.py | Apache-2.0 |
def _test_layer(self):
"""Tests Attention layer with a given score type"""
inputs_pl = tf.placeholder(tf.float32, (None, None, self.input_dim))
inputs_length_pl = tf.placeholder(tf.int32, [None])
state_pl = tf.placeholder(tf.float32, (None, self.state_dim))
attention_fn = self._create_layer()
s... | Tests Attention layer with a given score type | _test_layer | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/attention_test.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/attention_test.py | Apache-2.0 |
def _run(self, scope=None, **kwargs):
"""Runs the bridge with the given arguments
"""
with tf.variable_scope(scope or "bridge"):
bridge = self._create_bridge(**kwargs)
initial_state = bridge()
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
initial... | Runs the bridge with the given arguments
| _run | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/bridges_test.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/bridges_test.py | Apache-2.0 |
def _test_with_params(self, params):
"""Tests the encoder with a given parameter configuration"""
inputs = tf.random_normal(
[self.batch_size, self.sequence_length, self.input_depth])
example_length = tf.ones(
self.batch_size, dtype=tf.int32) * self.sequence_length
encode_fn = ConvEncod... | Tests the encoder with a given parameter configuration | _test_with_params | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/conv_encoder_test.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/conv_encoder_test.py | Apache-2.0 |
def _test_pipeline(self, mode, params=None):
"""Helper function to test the full model pipeline.
"""
# Create source and target example
source_len = self.sequence_length + 5
target_len = self.sequence_length + 10
source = " ".join(np.random.choice(self.vocab_list, source_len))
target = " ".j... | Helper function to test the full model pipeline.
| _test_pipeline | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/models_test.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/models_test.py | Apache-2.0 |
def _test_constant_shape(self, combiner):
"""Tests a residual combiner whose shape doesn't change
with depth"""
inputs = np.random.randn(1, 2)
with tf.variable_scope("same_input_size"):
res_ = self._test_with_residuals(inputs, residual_combiner=combiner)
self.assertEqual(res_[0].shape, (1, 2... | Tests a residual combiner whose shape doesn't change
with depth | _test_constant_shape | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/rnn_cell_test.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/rnn_cell_test.py | Apache-2.0 |
def _test_encode_with_params(self, params):
"""Tests the StackBidirectionalRNNEncoder with a specific cell"""
inputs = tf.random_normal(
[self.batch_size, self.sequence_length, self.input_depth])
example_length = tf.ones(
self.batch_size, dtype=tf.int32) * self.sequence_length
encode_fn... | Tests the StackBidirectionalRNNEncoder with a specific cell | _test_encode_with_params | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/rnn_encoder_test.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/rnn_encoder_test.py | Apache-2.0 |
def _test_with_args(self, **kwargs):
"""Helper function to test create_input_fn with keyword arguments"""
sources_file, targets_file = test_utils.create_temp_parallel_data(
sources=["Hello World ."], targets=["Goodbye ."])
pipeline = input_pipeline.ParallelTextInputPipeline(
params={
... | Helper function to test create_input_fn with keyword arguments | _test_with_args | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/train_utils_test.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/train_utils_test.py | Apache-2.0 |
def create_temp_parallel_data(sources, targets):
"""
Creates a temporary TFRecords file.
Args:
source: List of source sentences
target: List of target sentences
Returns:
A tuple (sources_file, targets_file).
"""
file_source = tempfile.NamedTemporaryFile()
file_target = tempfile.NamedTemporar... |
Creates a temporary TFRecords file.
Args:
source: List of source sentences
target: List of target sentences
Returns:
A tuple (sources_file, targets_file).
| create_temp_parallel_data | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/utils.py | Apache-2.0 |
def create_temporary_vocab_file(words, counts=None):
"""
Creates a temporary vocabulary file.
Args:
words: List of words in the vocabulary
Returns:
A temporary file object with one word per line
"""
vocab_file = tempfile.NamedTemporaryFile()
if counts is None:
for token in words:
vocab... |
Creates a temporary vocabulary file.
Args:
words: List of words in the vocabulary
Returns:
A temporary file object with one word per line
| create_temporary_vocab_file | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/test/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/test/utils.py | Apache-2.0 |
def varname_in_checkpoint(name):
"""Removes the prefix from the variable name.
"""
prefix_parts = self.params["prefix"].split("/")
checkpoint_prefix = "/".join(prefix_parts[:-1])
return name.replace(checkpoint_prefix + "/", "") | Removes the prefix from the variable name.
| varname_in_checkpoint | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/training/hooks.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/training/hooks.py | Apache-2.0 |
def dump(self, model_dir):
"""Dumps the options to a file in the model directory.
Args:
model_dir: Path to the model directory. The options will be
dumped into a file in this directory.
"""
gfile.MakeDirs(model_dir)
options_dict = {
"model_class": self.model_class,
"mode... | Dumps the options to a file in the model directory.
Args:
model_dir: Path to the model directory. The options will be
dumped into a file in this directory.
| dump | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/training/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/training/utils.py | Apache-2.0 |
def load(model_dir):
""" Loads options from the given model directory.
Args:
model_dir: Path to the model directory.
"""
with gfile.GFile(TrainOptions.path(model_dir), "rb") as file:
options_dict = json.loads(file.read().decode("utf-8"))
options_dict = defaultdict(None, options_dict)
... | Loads options from the given model directory.
Args:
model_dir: Path to the model directory.
| load | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/training/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/training/utils.py | Apache-2.0 |
def cell_from_spec(cell_classname, cell_params):
"""Create a RNN Cell instance from a JSON string.
Args:
cell_classname: Name of the cell class, e.g. "BasicLSTMCell".
cell_params: A dictionary of parameters to pass to the cell constructor.
Returns:
A RNNCell instance.
"""
cell_params = cell_par... | Create a RNN Cell instance from a JSON string.
Args:
cell_classname: Name of the cell class, e.g. "BasicLSTMCell".
cell_params: A dictionary of parameters to pass to the cell constructor.
Returns:
A RNNCell instance.
| cell_from_spec | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/training/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/training/utils.py | Apache-2.0 |
def get_rnn_cell(cell_class,
cell_params,
num_layers=1,
dropout_input_keep_prob=1.0,
dropout_output_keep_prob=1.0,
residual_connections=False,
residual_combiner="add",
residual_dense=False):
"""Creat... | Creates a new RNN Cell
Args:
cell_class: Name of the cell class, e.g. "BasicLSTMCell".
cell_params: A dictionary of parameters to pass to the cell constructor.
num_layers: Number of layers. The cell will be wrapped with
`tf.contrib.rnn.MultiRNNCell`
dropout_input_keep_prob: Dropout keep probabi... | get_rnn_cell | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/training/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/training/utils.py | Apache-2.0 |
def create_learning_rate_decay_fn(decay_type,
decay_steps,
decay_rate,
start_decay_at=0,
stop_decay_at=1e9,
min_learning_rate=None,
... | Creates a function that decays the learning rate.
Args:
decay_steps: How often to apply decay.
decay_rate: A Python number. The decay rate.
start_decay_at: Don't decay before this step
stop_decay_at: Don't decay after this step
min_learning_rate: Don't decay below this number
decay_type: A de... | create_learning_rate_decay_fn | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/training/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/training/utils.py | Apache-2.0 |
def decay_fn(learning_rate, global_step):
"""The computed learning rate decay function.
"""
global_step = tf.to_int32(global_step)
decay_type_fn = getattr(tf.train, decay_type)
decayed_learning_rate = decay_type_fn(
learning_rate=learning_rate,
global_step=tf.minimum(global_step, st... | The computed learning rate decay function.
| decay_fn | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/training/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/training/utils.py | Apache-2.0 |
def create_input_fn(pipeline,
batch_size,
bucket_boundaries=None,
allow_smaller_final_batch=False,
scope=None):
"""Creates an input function that can be used with tf.learn estimators.
Note that you must pass "factory funcitons" for bo... | Creates an input function that can be used with tf.learn estimators.
Note that you must pass "factory funcitons" for both the data provider and
featurizer to ensure that everything will be created in the same graph.
Args:
pipeline: An instance of `seq2seq.data.InputPipeline`.
batch_size: Create batc... | create_input_fn | python | taoyds/spider | baselines/seq2seq_attention_copy/seq2seq/training/utils.py | https://github.com/taoyds/spider/blob/master/baselines/seq2seq_attention_copy/seq2seq/training/utils.py | Apache-2.0 |
def str_list_to_batch(self, str_list):
"""get a list var of wemb of words in each column name in current bactch"""
B = len(str_list)
val_embs = []
val_len = np.zeros(B, dtype=np.int64)
for i, one_str in enumerate(str_list):
if self.trainable:
val = [s... | get a list var of wemb of words in each column name in current bactch | str_list_to_batch | python | taoyds/spider | baselines/sqlnet/scripts/model/modules/word_embedding.py | https://github.com/taoyds/spider/blob/master/baselines/sqlnet/scripts/model/modules/word_embedding.py | Apache-2.0 |
def do_configure(config_path=CONFIG_PATH):
"""Returns error code
:param config_path: path to config.yaml
:type config_path: string | CONFIG_PATH (genconf/config.yaml)
"""
config = Config(config_path)
try:
gen_out = config_util.onprem_generate(config)
except ValidationError as e:
... | Returns error code
:param config_path: path to config.yaml
:type config_path: string | CONFIG_PATH (genconf/config.yaml)
| do_configure | python | dcos/dcos | dcos_installer/backend.py | https://github.com/dcos/dcos/blob/master/dcos_installer/backend.py | Apache-2.0 |
def do_aws_cf_configure():
"""Returns error code
Generates AWS templates using a custom config.yaml
"""
# TODO(cmaloney): Move to Config class introduced in https://github.com/dcos/dcos/pull/623
config = Config(CONFIG_PATH)
# This process is usually ran from a docker container where default b... | Returns error code
Generates AWS templates using a custom config.yaml
| do_aws_cf_configure | python | dcos/dcos | dcos_installer/backend.py | https://github.com/dcos/dcos/blob/master/dcos_installer/backend.py | Apache-2.0 |
def determine_config_type(config_path=CONFIG_PATH):
"""Returns the configuration type to the UI. One of either 'minimal' or
'advanced'. 'advanced' blocks UI usage.
:param config_path: path to config.yaml
:type config_path: str | CONFIG_PATH (genconf/config.yaml)
"""
# TODO(cmaloney): If the con... | Returns the configuration type to the UI. One of either 'minimal' or
'advanced'. 'advanced' blocks UI usage.
:param config_path: path to config.yaml
:type config_path: str | CONFIG_PATH (genconf/config.yaml)
| determine_config_type | python | dcos/dcos | dcos_installer/backend.py | https://github.com/dcos/dcos/blob/master/dcos_installer/backend.py | Apache-2.0 |
def success(config: Config):
"""Returns the data for /success/ endpoint.
:param config_path: path to config.yaml
:type config_path: str | CONFIG_PATH (genconf/config.yaml)
"""
master_ips = config.hacky_default_get('master_list', [])
agent_ips = config.hacky_default_get('agent_list', [])
cod... | Returns the data for /success/ endpoint.
:param config_path: path to config.yaml
:type config_path: str | CONFIG_PATH (genconf/config.yaml)
| success | python | dcos/dcos | dcos_installer/backend.py | https://github.com/dcos/dcos/blob/master/dcos_installer/backend.py | Apache-2.0 |
def dispatch(args):
""" Dispatches the selected mode based on command line args. """
if args.action == 'hash-password':
# TODO(cmaloney): Import a function from the auth stuff to do the hashing and guarantee it
# always matches
byte_str = do_hash_password(args.password).encode('ascii')
... | Dispatches the selected mode based on command line args. | dispatch | python | dcos/dcos | dcos_installer/cli.py | https://github.com/dcos/dcos/blob/master/dcos_installer/cli.py | Apache-2.0 |
def get_argument_parser():
"""
Parse CLI arguments and return a map of options.
"""
parser = argparse.ArgumentParser(
description="DC/OS Install and Configuration Utility")
mutual_exc = parser.add_mutually_exclusive_group()
mutual_exc.add_argument(
'--hash-password',
act... |
Parse CLI arguments and return a map of options.
| get_argument_parser | python | dcos/dcos | dcos_installer/cli.py | https://github.com/dcos/dcos/blob/master/dcos_installer/cli.py | Apache-2.0 |
def normalize_config_validation(messages):
"""Accepts Gen error message format and returns a flattened dictionary
of validation messages.
:param messages: Gen validation messages
:type messages: dict | None
"""
validation = {}
if 'errors' in messages:
for key, errors in messages['er... | Accepts Gen error message format and returns a flattened dictionary
of validation messages.
:param messages: Gen validation messages
:type messages: dict | None
| normalize_config_validation | python | dcos/dcos | dcos_installer/config.py | https://github.com/dcos/dcos/blob/master/dcos_installer/config.py | Apache-2.0 |
def normalize_config_validation_exception(error: ValidationError) -> dict:
"""
A ValidationError is transformed to dict and processed by
`normalize_config_validation` function.
Args:
exception: An exception raised during the config validation
"""
messages = {}
messages['errors'] = e... |
A ValidationError is transformed to dict and processed by
`normalize_config_validation` function.
Args:
exception: An exception raised during the config validation
| normalize_config_validation_exception | python | dcos/dcos | dcos_installer/config.py | https://github.com/dcos/dcos/blob/master/dcos_installer/config.py | Apache-2.0 |
def test_password_hash():
"""Tests that the password hashing method creates de-cryptable hash
"""
password = 'DcosTestingPassword!@#'
# only reads from STDOUT
hash_pw = subprocess.check_output(['dcos_installer', '--hash-password', password])
print(hash_pw)
hash_pw = hash_pw.decode('ascii').s... | Tests that the password hashing method creates de-cryptable hash
| test_password_hash | python | dcos/dcos | dcos_installer/test_backend.py | https://github.com/dcos/dcos/blob/master/dcos_installer/test_backend.py | Apache-2.0 |
def valid_storage_config(release_config_aws):
""" Uses the settings from dcos-release.config.yaml ['testing'] to create a
new upload and then deletes it when the test is over
"""
s3_bucket_name = release_config_aws['bucket']
bucket_path = str(uuid.uuid4())
yield """---
master_list:
- 127.0.0.1
... | Uses the settings from dcos-release.config.yaml ['testing'] to create a
new upload and then deletes it when the test is over
| valid_storage_config | python | dcos/dcos | dcos_installer/test_backend.py | https://github.com/dcos/dcos/blob/master/dcos_installer/test_backend.py | Apache-2.0 |
def test_do_configure_valid_config_no_duplicate_logging(tmpdir, monkeypatch, caplog):
"""
Log messages are logged exactly once.
"""
monkeypatch.setenv('BOOTSTRAP_VARIANT', 'test_variant')
create_config(simple_full_config, tmpdir)
create_fake_build_artifacts(tmpdir)
with tmpdir.as_cwd():
... |
Log messages are logged exactly once.
| test_do_configure_valid_config_no_duplicate_logging | python | dcos/dcos | dcos_installer/test_backend.py | https://github.com/dcos/dcos/blob/master/dcos_installer/test_backend.py | Apache-2.0 |
def test_do_configure_logs_validation_errors(tmpdir, monkeypatch, caplog):
"""
Configuration validation errors are logged as `error` messages.
"""
monkeypatch.setenv('BOOTSTRAP_VARIANT', 'test_variant')
invalid_config = textwrap.dedent("""---
cluster_name: DC/OS
master_discovery: static
... |
Configuration validation errors are logged as `error` messages.
| test_do_configure_logs_validation_errors | python | dcos/dcos | dcos_installer/test_backend.py | https://github.com/dcos/dcos/blob/master/dcos_installer/test_backend.py | Apache-2.0 |
def validate_overlay_networks_not_overlap(dcos_overlay_network,
dcos_overlay_enable,
calico_network_cidr,
calico_enabled):
""" checks the subnets used for dcos overlay do not overlap calico ... | checks the subnets used for dcos overlay do not overlap calico network
We assume the basic validations, like subnet cidr, have been done.
| validate_overlay_networks_not_overlap | python | dcos/dcos | gen/calc.py | https://github.com/dcos/dcos/blob/master/gen/calc.py | Apache-2.0 |
def validate_adminrouter_x_frame_options(adminrouter_x_frame_options):
"""
Provide a basic validation that checks that provided value starts with
one of the supported options: DENY, SAMEORIGIN, ALLOW-FROM
See: https://tools.ietf.org/html/rfc7034#section-2.1
"""
msg = 'X-Frame-Options must be set... |
Provide a basic validation that checks that provided value starts with
one of the supported options: DENY, SAMEORIGIN, ALLOW-FROM
See: https://tools.ietf.org/html/rfc7034#section-2.1
| validate_adminrouter_x_frame_options | python | dcos/dcos | gen/calc.py | https://github.com/dcos/dcos/blob/master/gen/calc.py | Apache-2.0 |
def validate_rsa_pubkey(key_pem):
"""
Check if `key_pem` is a string containing an RSA public key encoded
using the X.509 SubjectPublicKeyInfo/OpenSSL PEM public key format.
Refs:
- https://tools.ietf.org/html/rfc5280.html
- http://stackoverflow.com/a/29707204/145... |
Check if `key_pem` is a string containing an RSA public key encoded
using the X.509 SubjectPublicKeyInfo/OpenSSL PEM public key format.
Refs:
- https://tools.ietf.org/html/rfc5280.html
- http://stackoverflow.com/a/29707204/145400
Args:
key_pem (str):... | validate_rsa_pubkey | python | dcos/dcos | gen/calc.py | https://github.com/dcos/dcos/blob/master/gen/calc.py | Apache-2.0 |
def _check(config: Dict[str, Any]) -> Tuple[List[str], bool]:
"""
Current constraints are:
config.yaml:
- exhibitor_tls_enabled == True,
- bootstrap_url must not be a local path
This is necessary to prevent this orchestration
from running when gen ... |
Current constraints are:
config.yaml:
- exhibitor_tls_enabled == True,
- bootstrap_url must not be a local path
This is necessary to prevent this orchestration
from running when gen is not executed on a proper
bootstrap node. This is a... | _check | python | dcos/dcos | gen/exhibitor_tls_bootstrap.py | https://github.com/dcos/dcos/blob/master/gen/exhibitor_tls_bootstrap.py | Apache-2.0 |
def _extract_package(package_path: str) -> None:
""" Extracts the dcos-bootstrap-ca package from the local pkgpanda
repository """
Path(BINARY_PATH).mkdir(exist_ok=True)
with tempfile.TemporaryDirectory() as td:
subprocess.run(['tar', '-xJf', package_path, '-C', td])
shutil.move(
... | Extracts the dcos-bootstrap-ca package from the local pkgpanda
repository | _extract_package | python | dcos/dcos | gen/exhibitor_tls_bootstrap.py | https://github.com/dcos/dcos/blob/master/gen/exhibitor_tls_bootstrap.py | Apache-2.0 |
def _init_ca(alt_names: List[str]) -> None:
""" Initializes the CA (generates a private key and self signed
certificate CA extensions) if the resulting files do not already exist """
root_ca_paths = [
Path(CA_PATH) / 'root-cert.pem', Path(CA_PATH) / 'root-key.pem']
if not all(map(lambda p: p.exi... | Initializes the CA (generates a private key and self signed
certificate CA extensions) if the resulting files do not already exist | _init_ca | python | dcos/dcos | gen/exhibitor_tls_bootstrap.py | https://github.com/dcos/dcos/blob/master/gen/exhibitor_tls_bootstrap.py | Apache-2.0 |
def _get_ca_alt_name(config: Dict[str, Any]) -> str:
""" Gets the bootstrap url hostname. Used to populate the CA SAN
extension """
url = config['exhibitor_bootstrap_ca_url'] or config['bootstrap_url']
return urlparse(url).hostname or "" | Gets the bootstrap url hostname. Used to populate the CA SAN
extension | _get_ca_alt_name | python | dcos/dcos | gen/exhibitor_tls_bootstrap.py | https://github.com/dcos/dcos/blob/master/gen/exhibitor_tls_bootstrap.py | Apache-2.0 |
def validate_one_of(val: str, valid_values) -> None:
"""Test if object `val` is a member of container `valid_values`.
Raise a AssertionError if it is not a member. The exception message contains
both, the representation (__repr__) of `val` as well as the representation
of all items in `valid_values`.
... | Test if object `val` is a member of container `valid_values`.
Raise a AssertionError if it is not a member. The exception message contains
both, the representation (__repr__) of `val` as well as the representation
of all items in `valid_values`.
| validate_one_of | python | dcos/dcos | gen/internals.py | https://github.com/dcos/dcos/blob/master/gen/internals.py | Apache-2.0 |
def __init__(self, variables: Set[str]=None, sub_scopes: Dict[str, Scope]=None):
"""
variables: set of parameters that must be extracted from Source
sub_scopes: mapping of variables to be conditionally added
"""
self.variables = variables if variables is not None else set()
... |
variables: set of parameters that must be extracted from Source
sub_scopes: mapping of variables to be conditionally added
| __init__ | python | dcos/dcos | gen/internals.py | https://github.com/dcos/dcos/blob/master/gen/internals.py | Apache-2.0 |
def __init__(self, entry=None, is_user=False):
""" Entry is a dict of the following form:
{
'validate': [validate_my_arg_fn],
'default': {arg: value}, # may be overridden by user args
'must': {arg: value}, # may NOT be overridden by user args
'secret': [arg],
... | Entry is a dict of the following form:
{
'validate': [validate_my_arg_fn],
'default': {arg: value}, # may be overridden by user args
'must': {arg: value}, # may NOT be overridden by user args
'secret': [arg],
'conditional': {arg: {val_1: add_entry_1, val_2: a... | __init__ | python | dcos/dcos | gen/internals.py | https://github.com/dcos/dcos/blob/master/gen/internals.py | Apache-2.0 |
def validate_single(self, name: str, value: str):
"""Calls all validate functions which validate the given parameter name
The validate functions will raise an AssertionError which should be caught by the caller
when validation fails.
"""
validate_fns = self._validate_by_arg.get(... | Calls all validate functions which validate the given parameter name
The validate functions will raise an AssertionError which should be caught by the caller
when validation fails.
| validate_single | python | dcos/dcos | gen/internals.py | https://github.com/dcos/dcos/blob/master/gen/internals.py | Apache-2.0 |
def validate_downstream_entry(entry: dict) -> None:
"""Raise an exception if entry is an invalid downstream gen.internals.Source entry."""
version_key = 'dcos_version'
entry_keys = set(entry.get('must', {}).keys()) | set(entry.get('default', {}).keys())
if version_key in entry_keys:
raise Except... | Raise an exception if entry is an invalid downstream gen.internals.Source entry. | validate_downstream_entry | python | dcos/dcos | gen/__init__.py | https://github.com/dcos/dcos/blob/master/gen/__init__.py | Apache-2.0 |
def stringify_configuration(configuration: dict):
"""Create a stringified version of the complete installer configuration
to send to gen.generate()"""
gen_config = {}
for key, value in configuration.items():
if isinstance(value, list) or isinstance(value, dict):
log.debug("Caught %s ... | Create a stringified version of the complete installer configuration
to send to gen.generate() | stringify_configuration | python | dcos/dcos | gen/__init__.py | https://github.com/dcos/dcos/blob/master/gen/__init__.py | Apache-2.0 |
def add_units(cloudconfig, services, cloud_init_implementation='coreos'):
'''
Takes a services dict in the format of CoreOS cloud-init 'units' and
injects into cloudconfig a transformed version appropriate for the
cloud_init_implementation. See:
https://coreos.com/os/docs/latest/cloud-config.html f... |
Takes a services dict in the format of CoreOS cloud-init 'units' and
injects into cloudconfig a transformed version appropriate for the
cloud_init_implementation. See:
https://coreos.com/os/docs/latest/cloud-config.html for the CoreOS 'units'
specification. See: https://cloudinit.readthedocs.io/en... | add_units | python | dcos/dcos | gen/__init__.py | https://github.com/dcos/dcos/blob/master/gen/__init__.py | Apache-2.0 |
def user_arguments_to_source(user_arguments) -> gen.internals.Source:
"""Convert all user arguments to be a gen.internals.Source"""
# Make sure all user provided arguments are strings.
# TODO(cmaloney): Loosen this restriction / allow arbitrary types as long
# as they all have a gen specific string fo... | Convert all user arguments to be a gen.internals.Source | user_arguments_to_source | python | dcos/dcos | gen/__init__.py | https://github.com/dcos/dcos/blob/master/gen/__init__.py | Apache-2.0 |
def get_config_id(argument_dict: dict):
"""Return a unique ID for the configuration represented by argument_dict."""
# dcos_image_commit and template_filenames should be included in argument_dict, but we reference them explicitly to
# ensure that the config ID reflects the current commit and config template... | Return a unique ID for the configuration represented by argument_dict. | get_config_id | python | dcos/dcos | gen/__init__.py | https://github.com/dcos/dcos/blob/master/gen/__init__.py | Apache-2.0 |
def validate_cloud_config(cc_string):
'''
Validate that there aren't any single quotes present since they break the
ARM template system. Exit with an error message if any invalid characters
are detected.
@param cc_string: str, Cloud Configuration
'''
if "'" in cc_string:
print("ERRO... |
Validate that there aren't any single quotes present since they break the
ARM template system. Exit with an error message if any invalid characters
are detected.
@param cc_string: str, Cloud Configuration
| validate_cloud_config | python | dcos/dcos | gen/build_deploy/azure.py | https://github.com/dcos/dcos/blob/master/gen/build_deploy/azure.py | Apache-2.0 |
def transform(cloud_config_yaml_str):
'''
Transforms the given yaml into a list of strings which are concatenated
together by the ARM template system. We must make it a list of strings so
that ARM template parameters appear at the top level of the template and get
substituted.
'''
cc_json = ... |
Transforms the given yaml into a list of strings which are concatenated
together by the ARM template system. We must make it a list of strings so
that ARM template parameters appear at the top level of the template and get
substituted.
| transform | python | dcos/dcos | gen/build_deploy/azure.py | https://github.com/dcos/dcos/blob/master/gen/build_deploy/azure.py | Apache-2.0 |
def gen_templates(gen_arguments, arm_template, extra_sources):
'''
Render the cloud_config template given a particular set of options
@param user_args: dict, args to pass to the gen library. These are user
input arguments which get filled in/prompted for.
@param arm_template: strin... |
Render the cloud_config template given a particular set of options
@param user_args: dict, args to pass to the gen library. These are user
input arguments which get filled in/prompted for.
@param arm_template: string, path to the source arm template for rendering
... | gen_templates | python | dcos/dcos | gen/build_deploy/azure.py | https://github.com/dcos/dcos/blob/master/gen/build_deploy/azure.py | Apache-2.0 |
def master_list_arm_json(num_masters, varietal):
'''
Return a JSON string containing a list of ARM expressions for the master IP's of the cluster.
@param num_masters: int, number of master nodes in the cluster
@param varietal: string, indicate template varietal to build for either 'acs' or 'dcos'
'... |
Return a JSON string containing a list of ARM expressions for the master IP's of the cluster.
@param num_masters: int, number of master nodes in the cluster
@param varietal: string, indicate template varietal to build for either 'acs' or 'dcos'
| master_list_arm_json | python | dcos/dcos | gen/build_deploy/azure.py | https://github.com/dcos/dcos/blob/master/gen/build_deploy/azure.py | Apache-2.0 |
def make_template(num_masters, gen_arguments, varietal, bootstrap_variant_prefix):
'''
Return a tuple: the generated template for num_masters and the artifact dict.
@param num_masters: int, number of master nodes to embed in the generated template
@param gen_arguments: dict, args to pass to the gen lib... |
Return a tuple: the generated template for num_masters and the artifact dict.
@param num_masters: int, number of master nodes to embed in the generated template
@param gen_arguments: dict, args to pass to the gen library. These are user
input arguments which get filled in/prompte... | make_template | python | dcos/dcos | gen/build_deploy/azure.py | https://github.com/dcos/dcos/blob/master/gen/build_deploy/azure.py | Apache-2.0 |
def gen_buttons(build_name, reproducible_artifact_path, tag, commit, download_url):
'''
Generate the button page, that is, "Deploy a cluster to Azure" page
'''
dcos_urls = [
encode_url_as_param(DOWNLOAD_URL_TEMPLATE.format(
download_url=download_url,
reproducible_artifact... |
Generate the button page, that is, "Deploy a cluster to Azure" page
| gen_buttons | python | dcos/dcos | gen/build_deploy/azure.py | https://github.com/dcos/dcos/blob/master/gen/build_deploy/azure.py | Apache-2.0 |
def make_bash(gen_out) -> None:
"""Build bash deployment artifacts and return a list of their filenames."""
# Build custom check bins package
if gen_out.arguments['custom_check_bins_provided'] == 'true':
package_filename = 'packages/{}/{}.tar.xz'.format(
gen_out.arguments['custom_check_b... | Build bash deployment artifacts and return a list of their filenames. | make_bash | python | dcos/dcos | gen/build_deploy/bash.py | https://github.com/dcos/dcos/blob/master/gen/build_deploy/bash.py | Apache-2.0 |
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