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def _docspec_comments(obj) -> Dict[str, str]: """ Inspect the docstring and get the comments for each parameter. """ |
# Sometimes our docstring is on the class, and sometimes it's on the initializer,
# so we've got to check both.
class_docstring = getattr(obj, '__doc__', None)
init_docstring = getattr(obj.__init__, '__doc__', None) if hasattr(obj, '__init__') else None
docstring = class_docstring or init_docstrin... |
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def render_config(config: Config, indent: str = "") -> str: """ Pretty-print a config in sort-of-JSON+comments. """ |
# Add four spaces to the indent.
new_indent = indent + " "
return "".join([
# opening brace + newline
"{\n",
# "type": "...", (if present)
f'{new_indent}"type": "{config.typ3}",\n' if config.typ3 else '',
# render each item
"".join... |
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def _render(item: ConfigItem, indent: str = "") -> str: """ Render a single config item, with the provided indent """ |
optional = item.default_value != _NO_DEFAULT
if is_configurable(item.annotation):
rendered_annotation = f"{item.annotation} (configurable)"
else:
rendered_annotation = str(item.annotation)
rendered_item = "".join([
# rendered_comment,
indent,
"// " ... |
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def url_to_filename(url: str, etag: str = None) -> str: """ Convert `url` into a hashed filename in a repeatable way. If `etag` is specified, append its hash to t... |
url_bytes = url.encode('utf-8')
url_hash = sha256(url_bytes)
filename = url_hash.hexdigest()
if etag:
etag_bytes = etag.encode('utf-8')
etag_hash = sha256(etag_bytes)
filename += '.' + etag_hash.hexdigest()
return filename |
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def split_s3_path(url: str) -> Tuple[str, str]: """Split a full s3 path into the bucket name and path.""" |
parsed = urlparse(url)
if not parsed.netloc or not parsed.path:
raise ValueError("bad s3 path {}".format(url))
bucket_name = parsed.netloc
s3_path = parsed.path
# Remove '/' at beginning of path.
if s3_path.startswith("/"):
s3_path = s3_path[1:]
return bucket_name, s3_path |
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def s3_request(func: Callable):
""" Wrapper function for s3 requests in order to create more helpful error messages. """ |
@wraps(func)
def wrapper(url: str, *args, **kwargs):
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if int(exc.response["Error"]["Code"]) == 404:
raise FileNotFoundError("file {} not found".format(url))
else:
... |
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def s3_etag(url: str) -> Optional[str]: """Check ETag on S3 object.""" |
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
s3_object = s3_resource.Object(bucket_name, s3_path)
return s3_object.e_tag |
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def s3_get(url: str, temp_file: IO) -> None: """Pull a file directly from S3.""" |
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file) |
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def get_from_cache(url: str, cache_dir: str = None) -> str: """ Given a URL, look for the corresponding dataset in the local cache. If it's not there, download it... |
if cache_dir is None:
cache_dir = CACHE_DIRECTORY
os.makedirs(cache_dir, exist_ok=True)
# Get eTag to add to filename, if it exists.
if url.startswith("s3://"):
etag = s3_etag(url)
else:
response = requests.head(url, allow_redirects=True)
if response.status_code !=... |
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def batch_split_sentences(self, texts: List[str]) -> List[List[str]]: """ This method lets you take advantage of spacy's batch processing. Default implementation ... |
return [self.split_sentences(text) for text in texts] |
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def dataset_iterator(self, file_path: str) -> Iterator[OntonotesSentence]: """ An iterator over the entire dataset, yielding all sentences processed. """ |
for conll_file in self.dataset_path_iterator(file_path):
yield from self.sentence_iterator(conll_file) |
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def dataset_path_iterator(file_path: str) -> Iterator[str]: """ An iterator returning file_paths in a directory containing CONLL-formatted files. """ |
logger.info("Reading CONLL sentences from dataset files at: %s", file_path)
for root, _, files in list(os.walk(file_path)):
for data_file in files:
# These are a relic of the dataset pre-processing. Every
# file will be duplicated - one file called filename.g... |
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def dataset_document_iterator(self, file_path: str) -> Iterator[List[OntonotesSentence]]: """ An iterator over CONLL formatted files which yields documents, regar... |
with codecs.open(file_path, 'r', encoding='utf8') as open_file:
conll_rows = []
document: List[OntonotesSentence] = []
for line in open_file:
line = line.strip()
if line != '' and not line.startswith('#'):
# Non-empty line.... |
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def sentence_iterator(self, file_path: str) -> Iterator[OntonotesSentence]: """ An iterator over the sentences in an individual CONLL formatted file. """ |
for document in self.dataset_document_iterator(file_path):
for sentence in document:
yield sentence |
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def _process_span_annotations_for_word(annotations: List[str], span_labels: List[List[str]], current_span_labels: List[Optional[str]]) -> None: """ Given a sequen... |
for annotation_index, annotation in enumerate(annotations):
# strip all bracketing information to
# get the actual propbank label.
label = annotation.strip("()*")
if "(" in annotation:
# Entering into a span for a particular semantic role label.
... |
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def print_results_from_args(args: argparse.Namespace):
""" Prints results from an ``argparse.Namespace`` object. """ |
path = args.path
metrics_name = args.metrics_filename
keys = args.keys
results_dict = {}
for root, _, files in os.walk(path):
if metrics_name in files:
full_name = os.path.join(root, metrics_name)
metrics = json.load(open(full_name))
results_dict[full_n... |
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def forward(self, input_tensor):
# pylint: disable=arguments-differ """ Apply dropout to input tensor. Parameters input_tensor: ``torch.FloatTensor`` A tensor of... |
ones = input_tensor.data.new_ones(input_tensor.shape[0], input_tensor.shape[-1])
dropout_mask = torch.nn.functional.dropout(ones, self.p, self.training, inplace=False)
if self.inplace:
input_tensor *= dropout_mask.unsqueeze(1)
return None
else:
return... |
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def unwrap_to_tensors(*tensors: torch.Tensor):
""" If you actually passed gradient-tracking Tensors to a Metric, there will be a huge memory leak, because it wil... |
return (x.detach().cpu() if isinstance(x, torch.Tensor) else x for x in tensors) |
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def replace_variables(sentence: List[str], sentence_variables: Dict[str, str]) -> Tuple[List[str], List[str]]: """ Replaces abstract variables in text with their ... |
tokens = []
tags = []
for token in sentence:
if token not in sentence_variables:
tokens.append(token)
tags.append("O")
else:
for word in sentence_variables[token].split():
tokens.append(word)
tags.append(token)
return t... |
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def clean_and_split_sql(sql: str) -> List[str]: """ Cleans up and unifies a SQL query. This involves unifying quoted strings and splitting brackets which aren't f... |
sql_tokens: List[str] = []
for token in sql.strip().split():
token = token.replace('"', "'").replace("%", "")
if token.endswith("(") and len(token) > 1:
sql_tokens.extend(split_table_and_column_names(token[:-1]))
sql_tokens.extend(split_table_and_column_names(token[-1]))... |
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def resolve_primary_keys_in_schema(sql_tokens: List[str], schema: Dict[str, List[TableColumn]]) -> List[str]: """ Some examples in the text2sql datasets use ID as... |
primary_keys_for_tables = {name: max(columns, key=lambda x: x.is_primary_key).name
for name, columns in schema.items()}
resolved_tokens = []
for i, token in enumerate(sql_tokens):
if i > 2:
table_name = sql_tokens[i - 2]
if token == "ID" and ta... |
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def sort_and_run_forward(self, module: Callable[[PackedSequence, Optional[RnnState]], Tuple[Union[PackedSequence, torch.Tensor], RnnState]], inputs: torch.Tensor,... |
# In some circumstances you may have sequences of zero length. ``pack_padded_sequence``
# requires all sequence lengths to be > 0, so remove sequences of zero length before
# calling self._module, then fill with zeros.
# First count how many sequences are empty.
batch_size = ma... |
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def _get_initial_states(self, batch_size: int, num_valid: int, sorting_indices: torch.LongTensor) -> Optional[RnnState]: """ Returns an initial state for use in a... |
# We don't know the state sizes the first time calling forward,
# so we let the module define what it's initial hidden state looks like.
if self._states is None:
return None
# Otherwise, we have some previous states.
if batch_size > self._states[0].size(1):
... |
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def _update_states(self, final_states: RnnStateStorage, restoration_indices: torch.LongTensor) -> None: """ After the RNN has run forward, the states need to be u... |
# TODO(Mark): seems weird to sort here, but append zeros in the subclasses.
# which way around is best?
new_unsorted_states = [state.index_select(1, restoration_indices)
for state in final_states]
if self._states is None:
# We don't already ha... |
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def to_value(original_string, corenlp_value=None):
"""Convert the string to Value object. Args: original_string (basestring):
Original string corenlp_value (bas... |
if isinstance(original_string, Value):
# Already a Value
return original_string
if not corenlp_value:
corenlp_value = original_string
# Number?
amount = NumberValue.parse(corenlp_value)
if amount is not None:
return NumberValue(amount, original_string)
# Date?
... |
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def to_value_list(original_strings, corenlp_values=None):
"""Convert a list of strings to a list of Values Args: original_strings (list[basestring]) corenlp_valu... |
assert isinstance(original_strings, (list, tuple, set))
if corenlp_values is not None:
assert isinstance(corenlp_values, (list, tuple, set))
assert len(original_strings) == len(corenlp_values)
return list(set(to_value(x, y) for (x, y)
in zip(original_strings, cor... |
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def check_denotation(target_values, predicted_values):
"""Return True if the predicted denotation is correct. Args: target_values (list[Value]) predicted_values ... |
# Check size
if len(target_values) != len(predicted_values):
return False
# Check items
for target in target_values:
if not any(target.match(pred) for pred in predicted_values):
return False
return True |
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def parse(text):
"""Try to parse into a number. Return: the number (int or float) if successful; otherwise None. """ |
try:
return int(text)
except ValueError:
try:
amount = float(text)
assert not isnan(amount) and not isinf(amount)
return amount
except (ValueError, AssertionError):
return None |
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def parse(text):
"""Try to parse into a date. Return: tuple (year, month, date) if successful; otherwise None. """ |
try:
ymd = text.lower().split('-')
assert len(ymd) == 3
year = -1 if ymd[0] in ('xx', 'xxxx') else int(ymd[0])
month = -1 if ymd[1] == 'xx' else int(ymd[1])
day = -1 if ymd[2] == 'xx' else int(ymd[2])
assert not year == month == day == -1
... |
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def forward(self, # pylint: disable=arguments-differ sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.LongTensor = None, s... |
raise NotImplementedError |
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def decode(self, initial_state: State, transition_function: TransitionFunction, supervision: SupervisionType) -> Dict[str, torch.Tensor]: """ Takes an initial sta... |
raise NotImplementedError |
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def state_dict(self) -> Dict[str, Any]: """ Returns the state of the scheduler as a ``dict``. """ |
return {key: value for key, value in self.__dict__.items() if key != 'optimizer'} |
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def load_state_dict(self, state_dict: Dict[str, Any]) -> None: """ Load the schedulers state. Parameters state_dict : ``Dict[str, Any]`` Scheduler state. Should b... |
self.__dict__.update(state_dict) |
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def ensemble(subresults: List[Dict[str, torch.Tensor]]) -> torch.Tensor: """ Identifies the best prediction given the results from the submodels. Parameters subre... |
# Choose the highest average confidence span.
span_start_probs = sum(subresult['span_start_probs'] for subresult in subresults) / len(subresults)
span_end_probs = sum(subresult['span_end_probs'] for subresult in subresults) / len(subresults)
return get_best_span(span_start_probs.log(), span_end_probs... |
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def load_weights(self, weight_file: str) -> None: """ Load the pre-trained weights from the file. """ |
requires_grad = self.requires_grad
with h5py.File(cached_path(weight_file), 'r') as fin:
for i_layer, lstms in enumerate(
zip(self.forward_layers, self.backward_layers)
):
for j_direction, lstm in enumerate(lstms):
# lstm ... |
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def return_type(self) -> Type: """ Gives the final return type for this function. If the function takes a single argument, this is just ``self.second``. If the fu... |
return_type = self.second
while isinstance(return_type, ComplexType):
return_type = return_type.second
return return_type |
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def argument_types(self) -> List[Type]: """ Gives the types of all arguments to this function. For functions returning a basic type, we grab all ``.first`` types ... |
arguments = [self.first]
remaining_type = self.second
while isinstance(remaining_type, ComplexType):
arguments.append(remaining_type.first)
remaining_type = remaining_type.second
return arguments |
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def substitute_any_type(self, basic_types: Set[BasicType]) -> List[Type]: """ Takes a set of ``BasicTypes`` and replaces any instances of ``ANY_TYPE`` inside this... |
substitutions = []
for first_type in substitute_any_type(self.first, basic_types):
for second_type in substitute_any_type(self.second, basic_types):
substitutions.append(self.__class__(first_type, second_type))
return substitutions |
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def log_parameter_and_gradient_statistics(self, # pylint: disable=invalid-name model: Model, batch_grad_norm: float) -> None: """ Send the mean and std of all par... |
if self._should_log_parameter_statistics:
# Log parameter values to Tensorboard
for name, param in model.named_parameters():
self.add_train_scalar("parameter_mean/" + name, param.data.mean())
self.add_train_scalar("parameter_std/" + name, param.data.std()... |
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def log_learning_rates(self, model: Model, optimizer: torch.optim.Optimizer):
""" Send current parameter specific learning rates to tensorboard """ |
if self._should_log_learning_rate:
# optimizer stores lr info keyed by parameter tensor
# we want to log with parameter name
names = {param: name for name, param in model.named_parameters()}
for group in optimizer.param_groups:
if 'lr' not in grou... |
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def log_histograms(self, model: Model, histogram_parameters: Set[str]) -> None: """ Send histograms of parameters to tensorboard. """ |
for name, param in model.named_parameters():
if name in histogram_parameters:
self.add_train_histogram("parameter_histogram/" + name, param) |
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def align_entities(extracted: List[str], literals: JsonDict, stemmer: NltkPorterStemmer) -> List[str]: """ Use stemming to attempt alignment between extracted wor... |
literal_keys = list(literals.keys())
literal_values = list(literals.values())
overlaps = [get_stem_overlaps(extract, literal_values, stemmer) for extract in extracted]
worlds = []
for overlap in overlaps:
if overlap[0] > overlap[1]:
worlds.append(literal_keys[0])
elif ov... |
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def multi_perspective_match(vector1: torch.Tensor, vector2: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ Calculate multi-perspect... |
assert vector1.size(0) == vector2.size(0)
assert weight.size(1) == vector1.size(2) == vector1.size(2)
# (batch, seq_len, 1)
similarity_single = F.cosine_similarity(vector1, vector2, 2).unsqueeze(2)
# (1, 1, num_perspectives, hidden_size)
weight = weight.unsqueeze(0).unsqueeze(0)
# (batch... |
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def multi_perspective_match_pairwise(vector1: torch.Tensor, vector2: torch.Tensor, weight: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: """ Calculate multi-p... |
num_perspectives = weight.size(0)
# (1, num_perspectives, 1, hidden_size)
weight = weight.unsqueeze(0).unsqueeze(2)
# (batch, num_perspectives, seq_len*, hidden_size)
vector1 = weight * vector1.unsqueeze(1).expand(-1, num_perspectives, -1, -1)
vector2 = weight * vector2.unsqueeze(1).expand(-1... |
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def get_date_from_utterance(tokenized_utterance: List[Token], year: int = 1993) -> List[datetime]: """ When the year is not explicitly mentioned in the utterance,... |
dates = []
utterance = ' '.join([token.text for token in tokenized_utterance])
year_result = re.findall(r'199[0-4]', utterance)
if year_result:
year = int(year_result[0])
trigrams = ngrams([token.text for token in tokenized_utterance], 3)
for month, tens, digit in trigrams:
# ... |
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def get_numbers_from_utterance(utterance: str, tokenized_utterance: List[Token]) -> Dict[str, List[int]]: """ Given an utterance, this function finds all the numb... |
# When we use a regex to find numbers or strings, we need a mapping from
# the character to which token triggered it.
char_offset_to_token_index = {token.idx : token_index
for token_index, token in enumerate(tokenized_utterance)}
# We want to look up later for each ti... |
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def digit_to_query_time(digit: str) -> List[int]: """ Given a digit in the utterance, return a list of the times that it corresponds to. """ |
if len(digit) > 2:
return [int(digit), int(digit) + TWELVE_TO_TWENTY_FOUR]
elif int(digit) % 12 == 0:
return [0, 1200, 2400]
return [int(digit) * HOUR_TO_TWENTY_FOUR,
(int(digit) * HOUR_TO_TWENTY_FOUR + TWELVE_TO_TWENTY_FOUR) % HOURS_IN_DAY] |
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def get_approximate_times(times: List[int]) -> List[int]: """ Given a list of times that follow a word such as ``about``, we return a list of times that could app... |
approximate_times = []
for time in times:
hour = int(time/HOUR_TO_TWENTY_FOUR) % 24
minute = time % HOUR_TO_TWENTY_FOUR
approximate_time = datetime.now()
approximate_time = approximate_time.replace(hour=hour, minute=minute)
start_time_range = approximate_time - timedelt... |
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def _time_regex_match(regex: str, utterance: str, char_offset_to_token_index: Dict[int, int], map_match_to_query_value: Callable[[str], List[int]], indices_of_app... |
linking_scores_dict: Dict[str, List[int]] = defaultdict(list)
number_regex = re.compile(regex)
for match in number_regex.finditer(utterance):
query_values = map_match_to_query_value(match.group())
# If the time appears after a word like ``about`` then we also add
# the times that ma... |
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def _evaluate_sql_query_subprocess(self, predicted_query: str, sql_query_labels: List[str]) -> int: """ We evaluate here whether the predicted query and the query... |
postprocessed_predicted_query = self.postprocess_query_sqlite(predicted_query)
try:
self._cursor.execute(postprocessed_predicted_query)
predicted_rows = self._cursor.fetchall()
except sqlite3.Error as error:
logger.warning(f'Error executing predicted: {erro... |
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def format_grammar_string(grammar_dictionary: Dict[str, List[str]]) -> str: """ Formats a dictionary of production rules into the string format expected by the Pa... |
grammar_string = '\n'.join([f"{nonterminal} = {' / '.join(right_hand_side)}"
for nonterminal, right_hand_side in grammar_dictionary.items()])
return grammar_string.replace("\\", "\\\\") |
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def initialize_valid_actions(grammar: Grammar, keywords_to_uppercase: List[str] = None) -> Dict[str, List[str]]: """ We initialize the valid actions with the glob... |
valid_actions: Dict[str, Set[str]] = defaultdict(set)
for key in grammar:
rhs = grammar[key]
# Sequence represents a series of expressions that match pieces of the text in order.
# Eg. A -> B C
if isinstance(rhs, Sequence):
valid_actions[key].add(format_action(key,... |
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def format_action(nonterminal: str, right_hand_side: str, is_string: bool = False, is_number: bool = False, keywords_to_uppercase: List[str] = None) -> str: """ T... |
keywords_to_uppercase = keywords_to_uppercase or []
if right_hand_side.upper() in keywords_to_uppercase:
right_hand_side = right_hand_side.upper()
if is_string:
return f'{nonterminal} -> ["\'{right_hand_side}\'"]'
elif is_number:
return f'{nonterminal} -> ["{right_hand_side}"]... |
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def add_action(self, node: Node) -> None: """ For each node, we accumulate the rules that generated its children in a list. """ |
if node.expr.name and node.expr.name not in ['ws', 'wsp']:
nonterminal = f'{node.expr.name} -> '
if isinstance(node.expr, Literal):
right_hand_side = f'["{node.text}"]'
else:
child_strings = []
for child in node.__iter__():
... |
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def visit(self, node):
""" See the ``NodeVisitor`` visit method. This just changes the order in which we visit nonterminals from right to left to left to right. ... |
method = getattr(self, 'visit_' + node.expr_name, self.generic_visit)
# Call that method, and show where in the tree it failed if it blows
# up.
try:
# Changing this to reverse here!
return method(node, [self.visit(child) for child in reversed(list(node))])
... |
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def update_grammar_to_be_variable_free(grammar_dictionary: Dict[str, List[str]]):
""" SQL is a predominately variable free language in terms of simple usage, in ... |
# Tables in variable free grammars cannot be aliased, so we
# remove this functionality from the grammar.
grammar_dictionary["select_result"] = ['"*"', '(table_name ws ".*")', 'expr']
# Similarly, collapse the definition of a source table
# to not contain aliases and modify references to subqueri... |
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def update_grammar_with_untyped_entities(grammar_dictionary: Dict[str, List[str]]) -> None: """ Variables can be treated as numbers or strings if their type can b... |
grammar_dictionary["string_set_vals"] = ['(value ws "," ws string_set_vals)', 'value']
grammar_dictionary["value"].remove('string')
grammar_dictionary["value"].remove('number')
grammar_dictionary["limit"] = ['("LIMIT" ws "1")', '("LIMIT" ws value)']
grammar_dictionary["expr"][1] = '(value wsp "LIKE... |
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def _load(cls, config: Params, serialization_dir: str, weights_file: str = None, cuda_device: int = -1) -> 'Model': """ Ensembles don't have vocabularies or weigh... |
model_params = config.get('model')
# The experiment config tells us how to _train_ a model, including where to get pre-trained
# embeddings from. We're now _loading_ the model, so those embeddings will already be
# stored in our weights. We don't need any pretrained weight file anymo... |
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def infer(self, setup: QuaRelType, answer_0: QuaRelType, answer_1: QuaRelType) -> int: """ Take the question and check if it is compatible with either of the answ... |
if self._check_quarels_compatible(setup, answer_0):
if self._check_quarels_compatible(setup, answer_1):
# Found two answers
return -2
else:
return 0
elif self._check_quarels_compatible(setup, answer_1):
return 1
... |
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def make_app(predictor: Predictor, field_names: List[str] = None, static_dir: str = None, sanitizer: Callable[[JsonDict], JsonDict] = None, title: str = "AllenNLP... |
if static_dir is not None:
static_dir = os.path.abspath(static_dir)
if not os.path.exists(static_dir):
logger.error("app directory %s does not exist, aborting", static_dir)
sys.exit(-1)
elif static_dir is None and field_names is None:
print("Neither build_dir nor... |
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def _html(title: str, field_names: List[str]) -> str: """ Returns bare bones HTML for serving up an input form with the specified fields that can render predictio... |
inputs = ''.join(_SINGLE_INPUT_TEMPLATE.substitute(field_name=field_name)
for field_name in field_names)
quoted_field_names = [f"'{field_name}'" for field_name in field_names]
quoted_field_list = f"[{','.join(quoted_field_names)}]"
return _PAGE_TEMPLATE.substitute(title=title,
... |
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def get_valid_actions(self) -> Dict[str, Tuple[torch.Tensor, torch.Tensor, List[int]]]: """ Returns the valid actions in the current grammar state. See the class ... |
actions = self._valid_actions[self._nonterminal_stack[-1]]
context_actions = []
for type_, variable in self._lambda_stacks:
if self._nonterminal_stack[-1] == type_:
production_string = f"{type_} -> {variable}"
context_actions.append(self._context_acti... |
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def assign_average_value(self) -> None: """ Replace all the parameter values with the averages. Save the current parameter values to restore later. """ |
for name, parameter in self._parameters:
self._backups[name].copy_(parameter.data)
parameter.data.copy_(self._shadows[name]) |
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def _prune_beam(states: List[State], beam_size: int, sort_states: bool = False) -> List[State]: """ This method can be used to prune the set of unfinished states ... |
states_by_batch_index: Dict[int, List[State]] = defaultdict(list)
for state in states:
assert len(state.batch_indices) == 1
batch_index = state.batch_indices[0]
states_by_batch_index[batch_index].append(state)
pruned_states = []
for _, instance_states... |
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def _get_best_final_states(self, finished_states: List[StateType]) -> Dict[int, List[StateType]]: """ Returns the best finished states for each batch instance bas... |
batch_states: Dict[int, List[StateType]] = defaultdict(list)
for state in finished_states:
batch_states[state.batch_indices[0]].append(state)
best_states: Dict[int, List[StateType]] = {}
for batch_index, states in batch_states.items():
# The time this sort takes ... |
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def _read_pretrained_embeddings_file(file_uri: str, embedding_dim: int, vocab: Vocabulary, namespace: str = "tokens") -> torch.FloatTensor: """ Returns and embedd... |
file_ext = get_file_extension(file_uri)
if file_ext in ['.h5', '.hdf5']:
return _read_embeddings_from_hdf5(file_uri,
embedding_dim,
vocab, namespace)
return _read_embeddings_from_text_file(file_uri,
... |
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def _get_num_tokens_from_first_line(line: str) -> Optional[int]: """ This function takes in input a string and if it contains 1 or 2 integers, it assumes the larg... |
fields = line.split(' ')
if 1 <= len(fields) <= 2:
try:
int_fields = [int(x) for x in fields]
except ValueError:
return None
else:
num_tokens = max(int_fields)
logger.info('Recognized a header line in th... |
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def _get_predicted_embedding_addition(self, checklist_state: ChecklistStatelet, action_ids: List[int], action_embeddings: torch.Tensor) -> torch.Tensor: """ Gets ... |
# Our basic approach here will be to figure out which actions we want to bias, by doing
# some fancy indexing work, then multiply the action embeddings by a mask for those
# actions, and return the sum of the result.
# Shape: (num_terminal_actions, 1). This is 1 if we still want to pr... |
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def _create_tensor_dicts(input_queue: Queue, output_queue: Queue, iterator: DataIterator, shuffle: bool, index: int) -> None: """ Pulls at most ``max_instances_in... |
def instances() -> Iterator[Instance]:
instance = input_queue.get()
while instance is not None:
yield instance
instance = input_queue.get()
for tensor_dict in iterator(instances(), num_epochs=1, shuffle=shuffle):
output_queue.put(tensor_dict)
output_queue.p... |
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def _queuer(instances: Iterable[Instance], input_queue: Queue, num_workers: int, num_epochs: Optional[int]) -> None: """ Reads Instances from the iterable and put... |
epoch = 0
while num_epochs is None or epoch < num_epochs:
epoch += 1
for instance in instances:
input_queue.put(instance)
# Now put a None for each worker, since each needs to receive one
# to know that it's done.
for _ in range(num_workers):
input_queue.put(No... |
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def get_valid_actions(self) -> List[Dict[str, Tuple[torch.Tensor, torch.Tensor, List[int]]]]: """ Returns a list of valid actions for each element of the group. "... |
return [state.get_valid_actions() for state in self.grammar_state] |
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def _worker(reader: DatasetReader, input_queue: Queue, output_queue: Queue, index: int) -> None: """ A worker that pulls filenames off the input queue, uses the d... |
# Keep going until you get a file_path that's None.
while True:
file_path = input_queue.get()
if file_path is None:
# Put my index on the queue to signify that I'm finished
output_queue.put(index)
break
logger.info(f"reading instances from {file_path... |
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def allowed_transitions(constraint_type: str, labels: Dict[int, str]) -> List[Tuple[int, int]]: """ Given labels and a constraint type, returns the allowed transi... |
num_labels = len(labels)
start_tag = num_labels
end_tag = num_labels + 1
labels_with_boundaries = list(labels.items()) + [(start_tag, "START"), (end_tag, "END")]
allowed = []
for from_label_index, from_label in labels_with_boundaries:
if from_label in ("START", "END"):
from... |
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def is_transition_allowed(constraint_type: str, from_tag: str, from_entity: str, to_tag: str, to_entity: str):
""" Given a constraint type and strings ``from_tag... |
# pylint: disable=too-many-return-statements
if to_tag == "START" or from_tag == "END":
# Cannot transition into START or from END
return False
if constraint_type == "BIOUL":
if from_tag == "START":
return to_tag in ('O', 'B', 'U')
if to_tag == "END":
... |
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def viterbi_tags(self, logits: torch.Tensor, mask: torch.Tensor) -> List[Tuple[List[int], float]]: """ Uses viterbi algorithm to find most likely tags for the giv... |
_, max_seq_length, num_tags = logits.size()
# Get the tensors out of the variables
logits, mask = logits.data, mask.data
# Augment transitions matrix with start and end transitions
start_tag = num_tags
end_tag = num_tags + 1
transitions = torch.Tensor(num_tags ... |
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def takes_arg(obj, arg: str) -> bool: """ Checks whether the provided obj takes a certain arg. If it's a class, we're really checking whether its constructor does... |
if inspect.isclass(obj):
signature = inspect.signature(obj.__init__)
elif inspect.ismethod(obj) or inspect.isfunction(obj):
signature = inspect.signature(obj)
else:
raise ConfigurationError(f"object {obj} is not callable")
return arg in signature.parameters |
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def create_kwargs(cls: Type[T], params: Params, **extras) -> Dict[str, Any]: """ Given some class, a `Params` object, and potentially other keyword arguments, cre... |
# Get the signature of the constructor.
signature = inspect.signature(cls.__init__)
kwargs: Dict[str, Any] = {}
# Iterate over all the constructor parameters and their annotations.
for name, param in signature.parameters.items():
# Skip "self". You're not *required* to call the first param... |
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def take_step(self, state: StateType, max_actions: int = None, allowed_actions: List[Set] = None) -> List[StateType]: """ The main method in the ``TransitionFunct... |
raise NotImplementedError |
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def parse_sentence(sentence_blob: str) -> Tuple[List[Dict[str, str]], List[Tuple[int, int]], List[str]]: """ Parses a chunk of text in the SemEval SDP format. Eac... |
annotated_sentence = []
arc_indices = []
arc_tags = []
predicates = []
lines = [line.split("\t") for line in sentence_blob.split("\n")
if line and not line.strip().startswith("#")]
for line_idx, line in enumerate(lines):
annotated_token = {k:v for k, v in zip(FIELDS, line)... |
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def parse_cuda_device(cuda_device: Union[str, int, List[int]]) -> Union[int, List[int]]: """ Disambiguates single GPU and multiple GPU settings for cuda_device pa... |
def from_list(strings):
if len(strings) > 1:
return [int(d) for d in strings]
elif len(strings) == 1:
return int(strings[0])
else:
return -1
if isinstance(cuda_device, str):
return from_list(re.split(r',\s*', cuda_device))
elif isinstance... |
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def add_epoch_number(batch: Batch, epoch: int) -> Batch: """ Add the epoch number to the batch instances as a MetadataField. """ |
for instance in batch.instances:
instance.fields['epoch_num'] = MetadataField(epoch)
return batch |
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def _take_instances(self, instances: Iterable[Instance], max_instances: Optional[int] = None) -> Iterator[Instance]: """ Take the next `max_instances` instances f... |
# If max_instances isn't specified, just iterate once over the whole dataset
if max_instances is None:
yield from iter(instances)
else:
# If we don't have a cursor for this dataset, create one. We use ``id()``
# for the key because ``instances`` could be a li... |
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def _memory_sized_lists(self, instances: Iterable[Instance]) -> Iterable[List[Instance]]: """ Breaks the dataset into "memory-sized" lists of instances, which it ... |
lazy = is_lazy(instances)
# Get an iterator over the next epoch worth of instances.
iterator = self._take_instances(instances, self._instances_per_epoch)
# We have four different cases to deal with:
# With lazy instances and no guidance about how many to load into memory,
... |
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def _ensure_batch_is_sufficiently_small( self, batch_instances: Iterable[Instance], excess: Deque[Instance]) -> List[List[Instance]]: """ If self._maximum_samples... |
if self._maximum_samples_per_batch is None:
assert not excess
return [list(batch_instances)]
key, limit = self._maximum_samples_per_batch
batches: List[List[Instance]] = []
batch: List[Instance] = []
padding_length = -1
excess.extend(batch_inst... |
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def _create_batches(self, instances: Iterable[Instance], shuffle: bool) -> Iterable[Batch]: """ This method should return one epoch worth of batches. """ |
raise NotImplementedError |
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def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor = None, dropout: Callable = None) -> Tuple[torch.Tensor, torch.Tenso... |
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = F.softmax(scores, dim=-1)
if dropout is not None:
p_attn = dropout(p_attn)
return torch.matmul(p_attn, value), p_a... |
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def subsequent_mask(size: int, device: str = 'cpu') -> torch.Tensor: """Mask out subsequent positions.""" |
mask = torch.tril(torch.ones(size, size, device=device, dtype=torch.int32)).unsqueeze(0)
return mask |
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def forward(self, x: torch.Tensor, sublayer: Callable[[torch.Tensor], torch.Tensor]) -> torch.Tensor: """Apply residual connection to any sublayer with the same s... |
return x + self.dropout(sublayer(self.norm(x))) |
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def block_orthogonal(tensor: torch.Tensor, split_sizes: List[int], gain: float = 1.0) -> None: """ An initializer which allows initializing model parameters in "b... |
data = tensor.data
sizes = list(tensor.size())
if any([a % b != 0 for a, b in zip(sizes, split_sizes)]):
raise ConfigurationError("tensor dimensions must be divisible by their respective "
"split_sizes. Found size: {} and split_sizes: {}".format(sizes, split_sizes))... |
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def lstm_hidden_bias(tensor: torch.Tensor) -> None: """ Initialize the biases of the forget gate to 1, and all other gates to 0, following Jozefowicz et al., An E... |
# gates are (b_hi|b_hf|b_hg|b_ho) of shape (4*hidden_size)
tensor.data.zero_()
hidden_size = tensor.shape[0] // 4
tensor.data[hidden_size:(2 * hidden_size)] = 1.0 |
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def _should_split_column_cells(cls, column_cells: List[str]) -> bool: """ Returns true if there is any cell in this column that can be split. """ |
return any(cls._should_split_cell(cell_text) for cell_text in column_cells) |
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def _should_split_cell(cls, cell_text: str) -> bool: """ Checks whether the cell should be split. We're just doing the same thing that SEMPRE did here. """ |
if ', ' in cell_text or '\n' in cell_text or '/' in cell_text:
return True
return False |
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def get_linked_agenda_items(self) -> List[str]: """ Returns entities that can be linked to spans in the question, that should be in the agenda, for training a cov... |
agenda_items: List[str] = []
for entity in self._get_longest_span_matching_entities():
agenda_items.append(entity)
# If the entity is a cell, we need to add the column to the agenda as well,
# because the answer most likely involves getting the row with the cell.
... |
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Description:
def split_predicate(ex: Extraction) -> Extraction: """ Ensure single word predicate by adding "before-predicate" and "after-predicate" arguments. """ |
rel_toks = ex.toks[char_to_word_index(ex.rel.span[0], ex.sent) \
: char_to_word_index(ex.rel.span[1], ex.sent) + 1]
if not rel_toks:
return ex
verb_inds = [tok_ind for (tok_ind, tok)
in enumerate(rel_toks)
if tok.tag_.startswith('VB')]
... |
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Description:
def extraction_to_conll(ex: Extraction) -> List[str]: """ Return a conll representation of a given input Extraction. """ |
ex = split_predicate(ex)
toks = ex.sent.split(' ')
ret = ['*'] * len(toks)
args = [ex.arg1] + ex.args2
rels_and_args = [("ARG{}".format(arg_ind), arg)
for arg_ind, arg in enumerate(args)] + \
[(rel_part.elem_type, rel_part)
for... |
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Description:
def interpret_element(element_type: str, text: str, span: str) -> Element: """ Construct an Element instance from regexp groups. """ |
return Element(element_type,
interpret_span(span),
text) |
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Description:
def convert_sent_to_conll(sent_ls: List[Extraction]):
""" Given a list of extractions for a single sentence - convert it to conll representation. """ |
# Sanity check - make sure all extractions are on the same sentence
assert(len(set([ex.sent for ex in sent_ls])) == 1)
toks = sent_ls[0].sent.split(' ')
return safe_zip(*[range(len(toks)),
toks] + \
[extraction_to_conll(ex)
for ex in sent_... |
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Description:
def pad_line_to_ontonotes(line, domain) -> List[str]: """ Pad line to conform to ontonotes representation. """ |
word_ind, word = line[ : 2]
pos = 'XX'
oie_tags = line[2 : ]
line_num = 0
parse = "-"
lemma = "-"
return [domain, line_num, word_ind, word, pos, parse, lemma, '-',\
'-', '-', '*'] + list(oie_tags) + ['-', ] |
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def convert_sent_dict_to_conll(sent_dic, domain) -> str: """ Given a dictionary from sentence -> extractions, return a corresponding CoNLL representation. """ |
return '\n\n'.join(['\n'.join(['\t'.join(map(str, pad_line_to_ontonotes(line, domain)))
for line in convert_sent_to_conll(sent_ls)])
for sent_ls
in sent_dic.iteritems()]) |
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def parse_s3_uri(uri):
"""Parses a S3 Uri into a dictionary of the Bucket, Key, and VersionId :return: a BodyS3Location dict or None if not an S3 Uri :rtype: dic... |
if not isinstance(uri, string_types):
return None
url = urlparse(uri)
query = parse_qs(url.query)
if url.scheme == 's3' and url.netloc and url.path:
s3_pointer = {
'Bucket': url.netloc,
'Key': url.path.lstrip('/')
}
if 'versionId' in query and l... |
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