text: # Copyright 2020 The Feast Authors
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import Dict, List, MutableMapping, Optional, Union
  
  import yaml
  from google.protobuf import json_format
  from google.protobuf.duration_pb2 import Duration
  from google.protobuf.json_format import MessageToDict, MessageToJson
  from google.protobuf.timestamp_pb2 import Timestamp
  
  from feast.data_source import DataSource, KafkaSource, KinesisSource
  from feast.feature import Feature
  from feast.loaders import yaml as feast_yaml
  from feast.protos.feast.core.FeatureTable_pb2 import FeatureTable as FeatureTableProto
  from feast.protos.feast.core.FeatureTable_pb2 import (
      FeatureTableMeta as FeatureTableMetaProto,
  )
  from feast.protos.feast.core.FeatureTable_pb2 import (
      FeatureTableSpec as FeatureTableSpecProto,
  )
  from feast.usage import log_exceptions
  from feast.value_type import ValueType
  
  
  class FeatureTable:
      """
      Represents a collection of features and associated metadata.
      """
  
      @log_exceptions
      def __init__(
          self,
          name: str,
          entities: List[str],
          features: List[Feature],
          batch_source: DataSource = None,
          stream_source: Optional[Union[KafkaSource, KinesisSource]] = None,
          max_age: Optional[Duration] = None,
          labels: Optional[MutableMapping[str, str]] = None,
      ):
          self._name = name
          self._entities = entities
          self._features = features
          self._batch_source = batch_source
          self._stream_source = stream_source
  
          self._labels: MutableMapping[str, str]
          if labels is None:
              self._labels = dict()
          else:
              self._labels = labels
  
          self._max_age = max_age
          self._created_timestamp: Optional[Timestamp] = None
          self._last_updated_timestamp: Optional[Timestamp] = None
  
      def __str__(self):
          return str(MessageToJson(self.to_proto()))
  
      def __hash__(self) -> int:
          return hash((id(self), self.name))
  
      def __eq__(self, other):
          if not isinstance(other, FeatureTable):
              raise TypeError(
                  "Comparisons should only involve FeatureTable class objects."
              )
  
          if (
              self.labels != other.labels
              or self.name != other.name
              or self.max_age != other.max_age
          ):
              return False
  
          if sorted(self.entities) != sorted(other.entities):
              return False
          if sorted(self.features) != sorted(other.features):
              return False
          if self.batch_source != other.batch_source:
              return False
          if self.stream_source != other.stream_source:
              return False
  
          return True
  
      @property
      def name(self):
          """
          Returns the name of this feature table
          """
          return self._name
  
      @name.setter
      def name(self, name: str):
          """
          Sets the name of this feature table
          """
          self._name = name
  
      @property
      def entities(self) -> List[str]:
          """
          Returns the entities of this feature table
          """
          return self._entities
  
      @entities.setter
      def entities(self, entities: List[str]):
          """
          Sets the entities of this feature table
          """
          self._entities = entities
  
      @property
      def features(self):
          """
          Returns the features of this feature table
          """
          return self._features
  
      @features.setter
      def features(self, features: List[Feature]):
          """
          Sets the features of this feature table
          """
          self._features = features
  
      @property
      def batch_source(self):
          """
          Returns the batch source of this feature table
          """
          return self._batch_source
  
      @batch_source.setter
      def batch_source(self, batch_source: DataSource):
          """
          Sets the batch source of this feature table
          """
          self._batch_source = batch_source
  
      @property
      def stream_source(self):
          """
          Returns the stream source of this feature table
          """
          return self._stream_source
  
      @stream_source.setter
      def stream_source(self, stream_source: Union[KafkaSource, KinesisSource]):
          """
          Sets the stream source of this feature table
          """
          self._stream_source = stream_source
  
      @property
      def max_age(self):
          """
          Returns the maximum age of this feature table. This is the total maximum
          amount of staleness that will be allowed during feature retrieval for
          each specific feature that is looked up.
          """
          return self._max_age
  
      @max_age.setter
      def max_age(self, max_age: Duration):
          """
          Set the maximum age for this feature table
          """
          self._max_age = max_age
  
      @property
      def labels(self):
          """
          Returns the labels of this feature table. This is the user defined metadata
          defined as a dictionary.
          """
          return self._labels
  
      @labels.setter
      def labels(self, labels: MutableMapping[str, str]):
          """
          Set the labels for this feature table
          """
          self._labels = labels
  
      @property
      def created_timestamp(self):
          """
          Returns the created_timestamp of this feature table
          """
          return self._created_timestamp
  
      @property
      def last_updated_timestamp(self):
          """
          Returns the last_updated_timestamp of this feature table
          """
          return self._last_updated_timestamp
  
      def add_feature(self, feature: Feature):
          """
          Adds a new feature to the feature table.
          """
          self.features.append(feature)
  
      def is_valid(self):
          """
          Validates the state of a feature table locally. Raises an exception
          if feature table is invalid.
          """
  
          if not self.name:
              raise ValueError("No name found in feature table.")
  
          if not self.entities:
              raise ValueError("No entities found in feature table {self.name}.")
  
      @classmethod
      def from_yaml(cls, yml: str):
          """
          Creates a feature table from a YAML string body or a file path
  
          Args:
              yml: Either a file path containing a yaml file or a YAML string
  
          Returns:
              Returns a FeatureTable object based on the YAML file
          """
  
          return cls.from_dict(feast_yaml.yaml_loader(yml, load_single=True))
  
      @classmethod
      def from_dict(cls, ft_dict):
          """
          Creates a feature table from a dict
  
          Args:
              ft_dict: A dict representation of a feature table
  
          Returns:
              Returns a FeatureTable object based on the feature table dict
          """
  
          feature_table_proto = json_format.ParseDict(
              ft_dict, FeatureTableProto(), ignore_unknown_fields=True
          )
  
          return cls.from_proto(feature_table_proto)
  
      @classmethod
      def from_proto(cls, feature_table_proto: FeatureTableProto):
          """
          Creates a feature table from a protobuf representation of a feature table
  
          Args:
              feature_table_proto: A protobuf representation of a feature table
  
          Returns:
              Returns a FeatureTableProto object based on the feature table protobuf
          """
  
          feature_table = cls(
              name=feature_table_proto.spec.name,
              entities=[entity for entity in feature_table_proto.spec.entities],
              features=[
                  Feature(
                      name=feature.name,
                      dtype=ValueType(feature.value_type),
                      labels=dict(feature.labels),
                  )
                  for feature in feature_table_proto.spec.features
              ],
              labels=feature_table_proto.spec.labels,
              max_age=(
                  None
                  if feature_table_proto.spec.max_age.seconds == 0
                  and feature_table_proto.spec.max_age.nanos == 0
                  else feature_table_proto.spec.max_age
              ),
              batch_source=DataSource.from_proto(feature_table_proto.spec.batch_source),
              stream_source=(
                  None
                  if not feature_table_proto.spec.stream_source.ByteSize()
                  else DataSource.from_proto(feature_table_proto.spec.stream_source)
              ),
          )
  
          feature_table._created_timestamp = feature_table_proto.meta.created_timestamp
  
          return feature_table
  
      def to_proto(self) -> FeatureTableProto:
          """
          Converts an feature table object to its protobuf representation
  
          Returns:
              FeatureTableProto protobuf
          """
  
          meta = FeatureTableMetaProto(
              created_timestamp=self.created_timestamp,
              last_updated_timestamp=self.last_updated_timestamp,
          )
  
          batch_source_proto = self.batch_source.to_proto()
          batch_source_proto.data_source_class_type = f"{self.batch_source.__class__.__module__}.{self.batch_source.__class__.__name__}"
  
          stream_source_proto = None
          if self.stream_source:
              stream_source_proto = self.stream_source.to_proto()
              stream_source_proto.data_source_class_type = f"{self.stream_source.__class__.__module__}.{self.stream_source.__class__.__name__}"
  
          spec = FeatureTableSpecProto(
              name=self.name,
              entities=self.entities,
              features=[
                  feature.to_proto() if type(feature) == Feature else feature
                  for feature in self.features
              ],
              labels=self.labels,
              max_age=self.max_age,
              batch_source=batch_source_proto,
              stream_source=stream_source_proto,
          )
  
          return FeatureTableProto(spec=spec, meta=meta)
  
      def to_spec_proto(self) -> FeatureTableSpecProto:
          """
          Converts an FeatureTableProto object to its protobuf representation.
          Used when passing FeatureTableSpecProto object to Feast request.
  
          Returns:
              FeatureTableSpecProto protobuf
          """
  
          spec = FeatureTableSpecProto(
              name=self.name,
              entities=self.entities,
              features=[
                  feature.to_proto() if type(feature) == Feature else feature
                  for feature in self.features
              ],
              labels=self.labels,
              max_age=self.max_age,
              batch_source=(
                  self.batch_source.to_proto()
                  if issubclass(type(self.batch_source), DataSource)
                  else self.batch_source
              ),
              stream_source=(
                  self.stream_source.to_proto()
                  if issubclass(type(self.stream_source), DataSource)
                  else self.stream_source
              ),
          )
  
          return spec
  
      def to_dict(self) -> Dict:
          """
          Converts feature table to dict
  
          :return: Dictionary object representation of feature table
          """
          feature_table_dict = MessageToDict(self.to_proto())
  
          # Remove meta when empty for more readable exports
          if feature_table_dict["meta"] == {}:
              del feature_table_dict["meta"]
  
          return feature_table_dict
  
      def to_yaml(self):
          """
          Converts a feature table to a YAML string.
  
          :return: Feature table string returned in YAML format
          """
          feature_table_dict = self.to_dict()
          return yaml.dump(feature_table_dict, allow_unicode=True, sort_keys=False)
  
      def _update_from_feature_table(self, feature_table):
          """
          Deep replaces one feature table with another
  
          Args:
              feature_table: Feature table to use as a source of configuration
          """
  
          self.name = feature_table.name
          self.entities = feature_table.entities
          self.features = feature_table.features
          self.labels = feature_table.labels
          self.max_age = feature_table.max_age
          self.batch_source = feature_table.batch_source
          self.stream_source = feature_table.stream_source
          self._created_timestamp = feature_table.created_timestamp
          self._last_updated_timestamp = feature_table.last_updated_timestamp
  
      def __repr__(self):
          return f"FeatureTable <{self.name}>"
  