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from snips_inference_agl.constants import INTENTS, UTTERANCES, DATA, SLOT_NAME, ENTITY
from snips_inference_agl.exceptions import DatasetFormatError
def type_error(expected_type, found_type, object_label=None):
if object_label is None:
raise DatasetFormatError("Invalid type: expected %s but found %s"
% (expected_type, found_type))
raise DatasetFormatError("Invalid type for '%s': expected %s but found %s"
% (object_label, expected_type, found_type))
def validate_type(obj, expected_type, object_label=None):
if not isinstance(obj, expected_type):
type_error(expected_type, type(obj), object_label)
def missing_key_error(key, object_label=None):
if object_label is None:
raise DatasetFormatError("Missing key: '%s'" % key)
raise DatasetFormatError("Expected %s to have key: '%s'"
% (object_label, key))
def validate_key(obj, key, object_label=None):
if key not in obj:
missing_key_error(key, object_label)
def validate_keys(obj, keys, object_label=None):
for key in keys:
validate_key(obj, key, object_label)
def get_slot_name_mapping(dataset, intent):
"""Returns a dict which maps slot names to entities for the provided intent
"""
slot_name_mapping = dict()
for utterance in dataset[INTENTS][intent][UTTERANCES]:
for chunk in utterance[DATA]:
if SLOT_NAME in chunk:
slot_name_mapping[chunk[SLOT_NAME]] = chunk[ENTITY]
return slot_name_mapping
def get_slot_name_mappings(dataset):
"""Returns a dict which maps intents to their slot name mapping"""
return {intent: get_slot_name_mapping(dataset, intent)
for intent in dataset[INTENTS]}
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