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allenai/allennlp | allennlp/state_machines/states/grammar_based_state.py | GrammarBasedState.get_valid_actions | 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] | python | 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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allenai/allennlp | allennlp/data/dataset_readers/multiprocess_dataset_reader.py | _worker | def _worker(reader: DatasetReader,
input_queue: Queue,
output_queue: Queue,
index: int) -> None:
"""
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allenai/allennlp | allennlp/modules/conditional_random_field.py | allowed_transitions | def allowed_transitions(constraint_type: str, labels: Dict[int, str]) -> List[Tuple[int, int]]:
"""
Given labels and a constraint type, returns the allowed transitions. It will
additionally include transitions for the start and end states, which are used
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... | python | def allowed_transitions(constraint_type: str, labels: Dict[int, str]) -> List[Tuple[int, int]]:
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Given labels and a constraint type, returns the allowed transitions. It will
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allenai/allennlp | allennlp/modules/conditional_random_field.py | is_transition_allowed | def is_transition_allowed(constraint_type: str,
from_tag: str,
from_entity: str,
to_tag: str,
to_entity: str):
"""
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from_tag: str,
from_entity: str,
to_tag: str,
to_entity: str):
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allenai/allennlp | allennlp/modules/conditional_random_field.py | ConditionalRandomField._input_likelihood | def _input_likelihood(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""
Computes the (batch_size,) denominator term for the log-likelihood, which is the
sum of the likelihoods across all possible state sequences.
"""
batch_size, sequence_length, num_tags = logi... | python | def _input_likelihood(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
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allenai/allennlp | allennlp/modules/conditional_random_field.py | ConditionalRandomField._joint_likelihood | def _joint_likelihood(self,
logits: torch.Tensor,
tags: torch.Tensor,
mask: torch.LongTensor) -> torch.Tensor:
"""
Computes the numerator term for the log-likelihood, which is just score(inputs, tags)
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allenai/allennlp | allennlp/modules/conditional_random_field.py | ConditionalRandomField.forward | def forward(self,
inputs: torch.Tensor,
tags: torch.Tensor,
mask: torch.ByteTensor = None) -> torch.Tensor:
"""
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# pylint: disable=arguments-differ
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tags: torch.Tensor,
mask: torch.ByteTensor = None) -> torch.Tensor:
"""
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allenai/allennlp | allennlp/modules/conditional_random_field.py | ConditionalRandomField.viterbi_tags | 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 given inputs.
If constraints are applied, disallows all other transitions.
"""
... | python | def viterbi_tags(self,
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"""
Uses viterbi algorithm to find most likely tags for the given inputs.
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allenai/allennlp | allennlp/nn/beam_search.py | BeamSearch.search | def search(self,
start_predictions: torch.Tensor,
start_state: StateType,
step: StepFunctionType) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Given a starting state and a step function, apply beam search to find the
most likely target sequences.
... | python | def search(self,
start_predictions: torch.Tensor,
start_state: StateType,
step: StepFunctionType) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Given a starting state and a step function, apply beam search to find the
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allenai/allennlp | scripts/examine_sql_coverage.py | main | def main(data_directory: int, dataset: str = None, filter_by: str = None, verbose: bool = False) -> None:
"""
Parameters
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data_directory : str, required.
The path to the data directory of https://github.com/jkkummerfeld/text2sql-data
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"""
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allenai/allennlp | allennlp/common/from_params.py | takes_arg | 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 it's a function or method, we're checking the object itself.
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allenai/allennlp | allennlp/common/from_params.py | remove_optional | def remove_optional(annotation: type):
"""
Optional[X] annotations are actually represented as Union[X, NoneType].
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args = getattr(annotation, '__args__', (... | python | def remove_optional(annotation: type):
"""
Optional[X] annotations are actually represented as Union[X, NoneType].
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allenai/allennlp | allennlp/common/from_params.py | create_kwargs | def create_kwargs(cls: Type[T], params: Params, **extras) -> Dict[str, Any]:
"""
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allenai/allennlp | allennlp/common/from_params.py | create_extras | def create_extras(cls: Type[T],
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allenai/allennlp | allennlp/common/from_params.py | FromParams.from_params | def from_params(cls: Type[T], params: Params, **extras) -> T:
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allenai/allennlp | allennlp/state_machines/transition_functions/transition_function.py | TransitionFunction.take_step | def take_step(self,
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allowed_actions: List[Set] = None) -> List[StateType]:
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allenai/allennlp | allennlp/training/optimizers.py | _safe_sparse_mask | def _safe_sparse_mask(tensor: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
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In PyTorch 1.0, Tensor._sparse_mask was changed to Tensor.sparse_mask.
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allenai/allennlp | allennlp/data/dataset_readers/semantic_dependency_parsing.py | parse_sentence | 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.
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'id': '1',
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allenai/allennlp | allennlp/common/checks.py | parse_cuda_device | 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 param.
"""
def from_list(strings):
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Disambiguates single GPU and multiple GPU settings for cuda_device param.
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allenai/allennlp | allennlp/commands/fine_tune.py | fine_tune_model_from_args | def fine_tune_model_from_args(args: argparse.Namespace):
"""
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allenai/allennlp | allennlp/commands/fine_tune.py | fine_tune_model_from_file_paths | def fine_tune_model_from_file_paths(model_archive_path: str,
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extend_vocab: bool = False,
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allenai/allennlp | allennlp/commands/fine_tune.py | fine_tune_model | def fine_tune_model(model: Model,
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allenai/allennlp | allennlp/modules/pruner.py | Pruner.forward | def forward(self, # pylint: disable=arguments-differ
embeddings: torch.FloatTensor,
mask: torch.LongTensor,
num_items_to_keep: Union[int, torch.LongTensor]) -> Tuple[torch.FloatTensor, torch.LongTensor,
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allenai/allennlp | allennlp/data/iterators/data_iterator.py | add_epoch_number | def add_epoch_number(batch: Batch, epoch: int) -> Batch:
"""
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"""
for instance in batch.instances:
instance.fields['epoch_num'] = MetadataField(epoch)
return batch | python | def add_epoch_number(batch: Batch, epoch: int) -> Batch:
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allenai/allennlp | allennlp/data/iterators/data_iterator.py | DataIterator._take_instances | def _take_instances(self,
instances: Iterable[Instance],
max_instances: Optional[int] = None) -> Iterator[Instance]:
"""
Take the next `max_instances` instances from the given dataset.
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allenai/allennlp | allennlp/data/iterators/data_iterator.py | DataIterator._ensure_batch_is_sufficiently_small | def _ensure_batch_is_sufficiently_small(
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allenai/allennlp | allennlp/data/iterators/data_iterator.py | DataIterator.get_num_batches | def get_num_batches(self, instances: Iterable[Instance]) -> int:
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allenai/allennlp | allennlp/data/iterators/data_iterator.py | DataIterator._create_batches | def _create_batches(self, instances: Iterable[Instance], shuffle: bool) -> Iterable[Batch]:
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allenai/allennlp | allennlp/common/tee_logger.py | replace_cr_with_newline | def replace_cr_with_newline(message: str):
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TQDM and requests use carriage returns to get the training line to update for each batch
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allenai/allennlp | allennlp/predictors/predictor.py | Predictor.capture_model_internals | def capture_model_internals(self) -> Iterator[dict]:
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allenai/allennlp | allennlp/predictors/predictor.py | Predictor._batch_json_to_instances | def _batch_json_to_instances(self, json_dicts: List[JsonDict]) -> List[Instance]:
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allenai/allennlp | allennlp/predictors/predictor.py | Predictor.from_path | def from_path(cls, archive_path: str, predictor_name: str = None) -> 'Predictor':
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allenai/allennlp | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | attention | def attention(query: torch.Tensor,
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value: torch.Tensor,
mask: torch.Tensor = None,
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allenai/allennlp | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | subsequent_mask | def subsequent_mask(size: int, device: str = 'cpu') -> torch.Tensor:
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allenai/allennlp | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | make_model | def make_model(num_layers: int = 6,
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allenai/allennlp | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | TransformerEncoder.forward | def forward(self, x, mask):
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allenai/allennlp | allennlp/semparse/contexts/table_question_knowledge_graph.py | TableQuestionKnowledgeGraph.read_from_file | def read_from_file(cls, filename: str, question: List[Token]) -> 'TableQuestionKnowledgeGraph':
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allenai/allennlp | allennlp/semparse/contexts/table_question_knowledge_graph.py | TableQuestionKnowledgeGraph.read_from_json | def read_from_json(cls, json_object: Dict[str, Any]) -> 'TableQuestionKnowledgeGraph':
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allenai/allennlp | allennlp/semparse/contexts/table_question_knowledge_graph.py | TableQuestionKnowledgeGraph._get_numbers_from_tokens | def _get_numbers_from_tokens(tokens: List[Token]) -> List[Tuple[str, str]]:
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Finds numbers in the input tokens and returns them as strings. We do some simple heuristic
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allenai/allennlp | allennlp/semparse/contexts/table_question_knowledge_graph.py | TableQuestionKnowledgeGraph._get_cell_parts | def _get_cell_parts(cls, cell_text: str) -> List[Tuple[str, str]]:
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allenai/allennlp | allennlp/semparse/contexts/table_question_knowledge_graph.py | TableQuestionKnowledgeGraph._should_split_column_cells | def _should_split_column_cells(cls, column_cells: List[str]) -> bool:
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allenai/allennlp | allennlp/semparse/contexts/table_question_knowledge_graph.py | TableQuestionKnowledgeGraph.get_linked_agenda_items | def get_linked_agenda_items(self) -> List[str]:
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allenai/allennlp | scripts/convert_openie_to_conll.py | main | def main(inp_fn: str,
domain: str,
out_fn: str) -> None:
"""
inp_fn: str, required.
Path to file from which to read Open IE extractions in Open IE4's format.
domain: str, required.
Domain to be used when writing CoNLL format.
out_fn: str, required.
Path to file to ... | python | def main(inp_fn: str,
domain: str,
out_fn: str) -> None:
"""
inp_fn: str, required.
Path to file from which to read Open IE extractions in Open IE4's format.
domain: str, required.
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allenai/allennlp | scripts/convert_openie_to_conll.py | element_from_span | def element_from_span(span: List[int],
span_type: str) -> Element:
"""
Return an Element from span (list of spacy toks)
"""
return Element(span_type,
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span_type: str) -> Element:
"""
Return an Element from span (list of spacy toks)
"""
return Element(span_type,
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allenai/allennlp | scripts/convert_openie_to_conll.py | split_predicate | 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 ... | python | 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) \
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allenai/allennlp | scripts/convert_openie_to_conll.py | extraction_to_conll | 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)
... | python | 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)
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allenai/allennlp | scripts/convert_openie_to_conll.py | interpret_span | def interpret_span(text_spans: str) -> List[int]:
"""
Return an integer tuple from
textual representation of closed / open spans.
"""
m = regex.match("^(?:(?:([\(\[]\d+, \d+[\)\]])|({\d+}))[,]?\s*)+$",
text_spans)
spans = m.captures(1) + m.captures(2)
int_spans = []
... | python | def interpret_span(text_spans: str) -> List[int]:
"""
Return an integer tuple from
textual representation of closed / open spans.
"""
m = regex.match("^(?:(?:([\(\[]\d+, \d+[\)\]])|({\d+}))[,]?\s*)+$",
text_spans)
spans = m.captures(1) + m.captures(2)
int_spans = []
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allenai/allennlp | scripts/convert_openie_to_conll.py | interpret_element | 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) | python | def interpret_element(element_type: str, text: str, span: str) -> Element:
"""
Construct an Element instance from regexp
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allenai/allennlp | scripts/convert_openie_to_conll.py | parse_element | def parse_element(raw_element: str) -> List[Element]:
"""
Parse a raw element into text and indices (integers).
"""
elements = [regex.match("^(([a-zA-Z]+)\(([^;]+),List\(([^;]*)\)\))$",
elem.lstrip().rstrip())
for elem
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"""
Parse a raw element into text and indices (integers).
"""
elements = [regex.match("^(([a-zA-Z]+)\(([^;]+),List\(([^;]*)\)\))$",
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allenai/allennlp | scripts/convert_openie_to_conll.py | convert_sent_to_conll | 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.... | python | 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)
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allenai/allennlp | scripts/convert_openie_to_conll.py | pad_line_to_ontonotes | 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, '-',\
... | python | 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 : ]
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lemma = "-"
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allenai/allennlp | scripts/convert_openie_to_conll.py | convert_sent_dict_to_conll | 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 conver... | python | def convert_sent_dict_to_conll(sent_dic, domain) -> str:
"""
Given a dictionary from sentence -> extractions,
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"""
return '\n\n'.join(['\n'.join(['\t'.join(map(str, pad_line_to_ontonotes(line, domain)))
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awslabs/serverless-application-model | examples/apps/kinesis-analytics-process-kpl-record/aws_kinesis_agg/deaggregator.py | deaggregate_record | def deaggregate_record(decoded_data):
'''Given a Kinesis record data that is decoded, deaggregate if it was packed using the
Kinesis Producer Library into individual records. This method will be a no-op for any
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decoded_data - the base64... | python | def deaggregate_record(decoded_data):
'''Given a Kinesis record data that is decoded, deaggregate if it was packed using the
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awslabs/serverless-application-model | samtranslator/model/s3_utils/uri_parser.py | parse_s3_uri | 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: dict
"""
if not isinstance(uri, string_types):
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url = urlparse(uri)
query = parse_qs(url.query)
if url.schem... | python | 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: dict
"""
if not isinstance(uri, string_types):
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awslabs/serverless-application-model | samtranslator/model/s3_utils/uri_parser.py | to_s3_uri | def to_s3_uri(code_dict):
"""Constructs a S3 URI string from given code dictionary
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:return: S3 URI of form s3://bucket/key?versionId=version
:rtype stri... | python | def to_s3_uri(code_dict):
"""Constructs a S3 URI string from given code dictionary
:param dict code_dict: Dictionary containing Lambda function Code S3 location of the form
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awslabs/serverless-application-model | samtranslator/model/s3_utils/uri_parser.py | construct_s3_location_object | def construct_s3_location_object(location_uri, logical_id, property_name):
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awslabs/serverless-application-model | samtranslator/model/function_policies.py | FunctionPolicies._get_policies | def _get_policies(self, resource_properties):
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* Managed policy name: string
... | python | def _get_policies(self, resource_properties):
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awslabs/serverless-application-model | samtranslator/model/function_policies.py | FunctionPolicies._contains_policies | def _contains_policies(self, resource_properties):
"""
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:param dict resource_properties: Properties of the resource
:return: True if we can process this resource. False, otherwise
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... | python | def _contains_policies(self, resource_properties):
"""
Is there policies data in this resource?
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awslabs/serverless-application-model | samtranslator/model/function_policies.py | FunctionPolicies._get_type | def _get_type(self, policy):
"""
Returns the type of the given policy
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"""
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awslabs/serverless-application-model | samtranslator/model/function_policies.py | FunctionPolicies._is_policy_template | def _is_policy_template(self, policy):
"""
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:param dict policy: Policy data
:return: True, if this is a policy template. False if it is not
"""
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"""
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awslabs/serverless-application-model | examples/apps/greengrass-hello-world/greengrasssdk/IoTDataPlane.py | Client.get_thing_shadow | def get_thing_shadow(self, **kwargs):
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:Keyword Arguments:
* *thingName* (``string``) --
[REQUIRED]
The name of the thing.
:returns: (``dict``) --
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r"""
Call shadow lambda to obtain current shadow state.
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awslabs/serverless-application-model | examples/apps/greengrass-hello-world/greengrasssdk/IoTDataPlane.py | Client.update_thing_shadow | def update_thing_shadow(self, **kwargs):
r"""
Updates the thing shadow for the specified thing.
:Keyword Arguments:
* *thingName* (``string``) --
[REQUIRED]
The name of the thing.
* *payload* (``bytes or seekable file-like object``) --
... | python | def update_thing_shadow(self, **kwargs):
r"""
Updates the thing shadow for the specified thing.
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* *thingName* (``string``) --
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* *payload* (``bytes or seekable file-like object``) --
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awslabs/serverless-application-model | examples/apps/greengrass-hello-world/greengrasssdk/IoTDataPlane.py | Client.delete_thing_shadow | def delete_thing_shadow(self, **kwargs):
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Deletes the thing shadow for the specified thing.
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[REQUIRED]
The name of the thing.
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r"""
Deletes the thing shadow for the specified thing.
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awslabs/serverless-application-model | examples/apps/greengrass-hello-world/greengrasssdk/IoTDataPlane.py | Client.publish | def publish(self, **kwargs):
r"""
Publishes state information.
:Keyword Arguments:
* *topic* (``string``) --
[REQUIRED]
The name of the MQTT topic.
* *payload* (``bytes or seekable file-like object``) --
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r"""
Publishes state information.
:Keyword Arguments:
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awslabs/serverless-application-model | samtranslator/plugins/globals/globals.py | Globals.merge | def merge(self, resource_type, resource_properties):
"""
Adds global properties to the resource, if necessary. This method is a no-op if there are no global properties
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:param string resource_type: Type of the resource (Ex: AWS::Serverless::Function)
:param... | python | def merge(self, resource_type, resource_properties):
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awslabs/serverless-application-model | samtranslator/plugins/globals/globals.py | Globals._parse | def _parse(self, globals_dict):
"""
Takes a SAM template as input and parses the Globals section
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:return: Processed globals dictionary which can be used to quickly identify properties to merge
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Takes a SAM template as input and parses the Globals section
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awslabs/serverless-application-model | samtranslator/plugins/globals/globals.py | GlobalProperties._do_merge | def _do_merge(self, global_value, local_value):
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awslabs/serverless-application-model | samtranslator/plugins/globals/globals.py | GlobalProperties._merge_dict | def _merge_dict(self, global_dict, local_dict):
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:param local_dict: Local dictionary to be merged
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awslabs/serverless-application-model | samtranslator/plugins/globals/globals.py | GlobalProperties._token_of | def _token_of(self, input):
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awslabs/serverless-application-model | samtranslator/validator/validator.py | SamTemplateValidator.validate | def validate(template_dict, schema=None):
"""
Is this a valid SAM template dictionary
:param dict template_dict: Data to be validated
:param dict schema: Optional, dictionary containing JSON Schema representing SAM template
:return: Empty string if there are no validation errors... | python | def validate(template_dict, schema=None):
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awslabs/serverless-application-model | examples/apps/lex-book-trip-python/lambda_function.py | generate_car_price | def generate_car_price(location, days, age, car_type):
"""
Generates a number within a reasonable range that might be expected for a flight.
The price is fixed for a given pair of locations.
"""
car_types = ['economy', 'standard', 'midsize', 'full size', 'minivan', 'luxury']
base_location_cost ... | python | def generate_car_price(location, days, age, car_type):
"""
Generates a number within a reasonable range that might be expected for a flight.
The price is fixed for a given pair of locations.
"""
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awslabs/serverless-application-model | examples/apps/lex-book-trip-python/lambda_function.py | generate_hotel_price | def generate_hotel_price(location, nights, room_type):
"""
Generates a number within a reasonable range that might be expected for a hotel.
The price is fixed for a pair of location and roomType.
"""
room_types = ['queen', 'king', 'deluxe']
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... | python | def generate_hotel_price(location, nights, room_type):
"""
Generates a number within a reasonable range that might be expected for a hotel.
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awslabs/serverless-application-model | examples/apps/lex-book-trip-python/lambda_function.py | book_hotel | def book_hotel(intent_request):
"""
Performs dialog management and fulfillment for booking a hotel.
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1) Use of elicitSlot in slot validation and re-prompting
2) Use of sessionAttributes to pass information that can be... | python | def book_hotel(intent_request):
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awslabs/serverless-application-model | examples/apps/lex-book-trip-python/lambda_function.py | book_car | def book_car(intent_request):
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awslabs/serverless-application-model | examples/apps/lex-book-trip-python/lambda_function.py | dispatch | def dispatch(intent_request):
"""
Called when the user specifies an intent for this bot.
"""
logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))
intent_name = intent_request['currentIntent']['name']
# Dispatch to your bot... | python | def dispatch(intent_request):
"""
Called when the user specifies an intent for this bot.
"""
logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))
intent_name = intent_request['currentIntent']['name']
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awslabs/serverless-application-model | samtranslator/model/eventsources/pull.py | PullEventSource.to_cloudformation | def to_cloudformation(self, **kwargs):
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awslabs/serverless-application-model | samtranslator/model/eventsources/pull.py | PullEventSource._link_policy | def _link_policy(self, role):
"""If this source triggers a Lambda function whose execution role is auto-generated by SAM, add the
appropriate managed policy to this Role.
:param model.iam.IAMROle role: the execution role generated for the function
"""
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awslabs/serverless-application-model | samtranslator/sdk/parameter.py | SamParameterValues.add_default_parameter_values | def add_default_parameter_values(self, sam_template):
"""
Method to read default values for template parameters and merge with user supplied values.
Example:
If the template contains the following parameters defined
Parameters:
Param1:
Type: String
... | python | def add_default_parameter_values(self, sam_template):
"""
Method to read default values for template parameters and merge with user supplied values.
Example:
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Parameters:
Param1:
Type: String
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awslabs/serverless-application-model | samtranslator/sdk/parameter.py | SamParameterValues.add_pseudo_parameter_values | def add_pseudo_parameter_values(self):
"""
Add pseudo parameter values
:return: parameter values that have pseudo parameter in it
"""
if 'AWS::Region' not in self.parameter_values:
self.parameter_values['AWS::Region'] = boto3.session.Session().region_name | python | def add_pseudo_parameter_values(self):
"""
Add pseudo parameter values
:return: parameter values that have pseudo parameter in it
"""
if 'AWS::Region' not in self.parameter_values:
self.parameter_values['AWS::Region'] = boto3.session.Session().region_name | [
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awslabs/serverless-application-model | samtranslator/model/preferences/deployment_preference_collection.py | DeploymentPreferenceCollection.add | def add(self, logical_id, deployment_preference_dict):
"""
Add this deployment preference to the collection
:raise ValueError if an existing logical id already exists in the _resource_preferences
:param logical_id: logical id of the resource where this deployment preference applies
... | python | def add(self, logical_id, deployment_preference_dict):
"""
Add this deployment preference to the collection
:raise ValueError if an existing logical id already exists in the _resource_preferences
:param logical_id: logical id of the resource where this deployment preference applies
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awslabs/serverless-application-model | samtranslator/model/preferences/deployment_preference_collection.py | DeploymentPreferenceCollection.enabled_logical_ids | def enabled_logical_ids(self):
"""
:return: only the logical id's for the deployment preferences in this collection which are enabled
"""
return [logical_id for logical_id, preference in self._resource_preferences.items() if preference.enabled] | python | def enabled_logical_ids(self):
"""
:return: only the logical id's for the deployment preferences in this collection which are enabled
"""
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awslabs/serverless-application-model | samtranslator/model/preferences/deployment_preference_collection.py | DeploymentPreferenceCollection.deployment_group | def deployment_group(self, function_logical_id):
"""
:param function_logical_id: logical_id of the function this deployment group belongs to
:return: CodeDeployDeploymentGroup resource
"""
deployment_preference = self.get(function_logical_id)
deployment_group = CodeDeplo... | python | def deployment_group(self, function_logical_id):
"""
:param function_logical_id: logical_id of the function this deployment group belongs to
:return: CodeDeployDeploymentGroup resource
"""
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awslabs/serverless-application-model | examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py | get_welcome_response | def get_welcome_response():
""" If we wanted to initialize the session to have some attributes we could
add those here
"""
session_attributes = {}
card_title = "Welcome"
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""" If we wanted to initialize the session to have some attributes we could
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"""
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] | cccb0c96b5c91e53355ebc07e542467303a5eedd | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py#L46-L62 | train |
awslabs/serverless-application-model | examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py | set_color_in_session | def set_color_in_session(intent, session):
""" Sets the color in the session and prepares the speech to reply to the
user.
"""
card_title = intent['name']
session_attributes = {}
should_end_session = False
if 'Color' in intent['slots']:
favorite_color = intent['slots']['Color']['va... | python | def set_color_in_session(intent, session):
""" Sets the color in the session and prepares the speech to reply to the
user.
"""
card_title = intent['name']
session_attributes = {}
should_end_session = False
if 'Color' in intent['slots']:
favorite_color = intent['slots']['Color']['va... | [
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] | cccb0c96b5c91e53355ebc07e542467303a5eedd | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py#L79-L104 | train |
awslabs/serverless-application-model | examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py | on_intent | def on_intent(intent_request, session):
""" Called when the user specifies an intent for this skill """
print("on_intent requestId=" + intent_request['requestId'] +
", sessionId=" + session['sessionId'])
intent = intent_request['intent']
intent_name = intent_request['intent']['name']
# ... | python | def on_intent(intent_request, session):
""" Called when the user specifies an intent for this skill """
print("on_intent requestId=" + intent_request['requestId'] +
", sessionId=" + session['sessionId'])
intent = intent_request['intent']
intent_name = intent_request['intent']['name']
# ... | [
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... | Called when the user specifies an intent for this skill | [
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] | cccb0c96b5c91e53355ebc07e542467303a5eedd | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py#L148-L167 | train |
awslabs/serverless-application-model | examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py | lambda_handler | def lambda_handler(event, context):
""" Route the incoming request based on type (LaunchRequest, IntentRequest,
etc.) The JSON body of the request is provided in the event parameter.
"""
print("event.session.application.applicationId=" +
event['session']['application']['applicationId'])
"... | python | def lambda_handler(event, context):
""" Route the incoming request based on type (LaunchRequest, IntentRequest,
etc.) The JSON body of the request is provided in the event parameter.
"""
print("event.session.application.applicationId=" +
event['session']['application']['applicationId'])
"... | [
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"\"\"\"\n Uncomment this if statement and ... | Route the incoming request based on type (LaunchRequest, IntentRequest,
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] | cccb0c96b5c91e53355ebc07e542467303a5eedd | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py#L182-L207 | train |
awslabs/serverless-application-model | samtranslator/translator/logical_id_generator.py | LogicalIdGenerator.gen | def gen(self):
"""
Generate stable LogicalIds based on the prefix and given data. This method ensures that the logicalId is
deterministic and stable based on input prefix & data object. In other words:
logicalId changes *if and only if* either the `prefix` or `data_obj` changes
... | python | def gen(self):
"""
Generate stable LogicalIds based on the prefix and given data. This method ensures that the logicalId is
deterministic and stable based on input prefix & data object. In other words:
logicalId changes *if and only if* either the `prefix` or `data_obj` changes
... | [
"def",
"gen",
"(",
"self",
")",
":",
"data_hash",
"=",
"self",
".",
"get_hash",
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".",
"format",
"(",
"prefix",
"=",
"self",
".",
"_prefix",
",",
"hash",
"=",
"data_hash",
")"
] | Generate stable LogicalIds based on the prefix and given data. This method ensures that the logicalId is
deterministic and stable based on input prefix & data object. In other words:
logicalId changes *if and only if* either the `prefix` or `data_obj` changes
Internally we simply use a SHA... | [
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"object",
".",
"I... | cccb0c96b5c91e53355ebc07e542467303a5eedd | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/translator/logical_id_generator.py#L28-L47 | train |
awslabs/serverless-application-model | samtranslator/translator/logical_id_generator.py | LogicalIdGenerator.get_hash | def get_hash(self, length=HASH_LENGTH):
"""
Generate and return a hash of data that can be used as suffix of logicalId
:return: Hash of data if it was present
:rtype string
"""
data_hash = ""
if not self.data_str:
return data_hash
encoded_da... | python | def get_hash(self, length=HASH_LENGTH):
"""
Generate and return a hash of data that can be used as suffix of logicalId
:return: Hash of data if it was present
:rtype string
"""
data_hash = ""
if not self.data_str:
return data_hash
encoded_da... | [
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"HASH_LENGTH",
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":",
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"."... | Generate and return a hash of data that can be used as suffix of logicalId
:return: Hash of data if it was present
:rtype string | [
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] | cccb0c96b5c91e53355ebc07e542467303a5eedd | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/translator/logical_id_generator.py#L49-L72 | train |
awslabs/serverless-application-model | samtranslator/translator/logical_id_generator.py | LogicalIdGenerator._stringify | def _stringify(self, data):
"""
Stable, platform & language-independent stringification of a data with basic Python type.
We use JSON to dump a string instead of `str()` method in order to be language independent.
:param data: Data to be stringified. If this is one of JSON native types... | python | def _stringify(self, data):
"""
Stable, platform & language-independent stringification of a data with basic Python type.
We use JSON to dump a string instead of `str()` method in order to be language independent.
:param data: Data to be stringified. If this is one of JSON native types... | [
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"# Get the most compact dictionary (separators) and sort the keys recursively to get a stable output",
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"dumps"... | Stable, platform & language-independent stringification of a data with basic Python type.
We use JSON to dump a string instead of `str()` method in order to be language independent.
:param data: Data to be stringified. If this is one of JSON native types like string, dict, array etc, it will
... | [
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] | cccb0c96b5c91e53355ebc07e542467303a5eedd | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/translator/logical_id_generator.py#L74-L90 | train |
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