text: # Copyright 2019 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.
  import copy
  import warnings
  from datetime import datetime, timedelta
  from typing import Dict, List, Optional, Tuple, Type, Union
  
  from google.protobuf.duration_pb2 import Duration
  
  from feast import utils
  from feast.base_feature_view import BaseFeatureView
  from feast.data_source import DataSource
  from feast.entity import Entity
  from feast.feature import Feature
  from feast.feature_view_projection import FeatureViewProjection
  from feast.protos.feast.core.FeatureView_pb2 import FeatureView as FeatureViewProto
  from feast.protos.feast.core.FeatureView_pb2 import (
      FeatureViewMeta as FeatureViewMetaProto,
  )
  from feast.protos.feast.core.FeatureView_pb2 import (
      FeatureViewSpec as FeatureViewSpecProto,
  )
  from feast.protos.feast.core.FeatureView_pb2 import (
      MaterializationInterval as MaterializationIntervalProto,
  )
  from feast.usage import log_exceptions
  from feast.value_type import ValueType
  
  warnings.simplefilter("once", DeprecationWarning)
  
  # DUMMY_ENTITY is a placeholder entity used in entityless FeatureViews
  DUMMY_ENTITY_ID = "__dummy_id"
  DUMMY_ENTITY_NAME = "__dummy"
  DUMMY_ENTITY_VAL = ""
  DUMMY_ENTITY = Entity(
      name=DUMMY_ENTITY_NAME, join_key=DUMMY_ENTITY_ID, value_type=ValueType.STRING,
  )
  
  
  class FeatureView(BaseFeatureView):
      """
      A FeatureView defines a logical grouping of serveable features.
  
      Args:
          name: Name of the group of features.
          entities: The entities to which this group of features is associated.
          ttl: The amount of time this group of features lives. A ttl of 0 indicates that
              this group of features lives forever. Note that large ttl's or a ttl of 0
              can result in extremely computationally intensive queries.
          input: The source of data where this group of features is stored.
          batch_source (optional): The batch source of data where this group of features
              is stored.
          stream_source (optional): The stream source of data where this group of features
              is stored.
          features (optional): The set of features defined as part of this FeatureView.
          tags (optional): A dictionary of key-value pairs used for organizing
              FeatureViews.
      """
  
      entities: List[str]
      tags: Optional[Dict[str, str]]
      ttl: timedelta
      online: bool
      input: DataSource
      batch_source: DataSource
      stream_source: Optional[DataSource]
      materialization_intervals: List[Tuple[datetime, datetime]]
  
      @log_exceptions
      def __init__(
          self,
          name: str,
          entities: List[str],
          ttl: Union[Duration, timedelta],
          input: Optional[DataSource] = None,
          batch_source: Optional[DataSource] = None,
          stream_source: Optional[DataSource] = None,
          features: Optional[List[Feature]] = None,
          tags: Optional[Dict[str, str]] = None,
          online: bool = True,
      ):
          """
          Creates a FeatureView object.
  
          Raises:
              ValueError: A field mapping conflicts with an Entity or a Feature.
          """
          if input is not None:
              warnings.warn(
                  (
                      "The argument 'input' is being deprecated. Please use 'batch_source' "
                      "instead. Feast 0.13 and onwards will not support the argument 'input'."
                  ),
                  DeprecationWarning,
              )
  
          _input = input or batch_source
          assert _input is not None
  
          _features = features or []
  
          cols = [entity for entity in entities] + [feat.name for feat in _features]
          for col in cols:
              if _input.field_mapping is not None and col in _input.field_mapping.keys():
                  raise ValueError(
                      f"The field {col} is mapped to {_input.field_mapping[col]} for this data source. "
                      f"Please either remove this field mapping or use {_input.field_mapping[col]} as the "
                      f"Entity or Feature name."
                  )
  
          super().__init__(name, _features)
          self.entities = entities if entities else [DUMMY_ENTITY_NAME]
          self.tags = tags if tags is not None else {}
  
          if isinstance(ttl, Duration):
              self.ttl = timedelta(seconds=int(ttl.seconds))
          else:
              self.ttl = ttl
  
          self.online = online
          self.input = _input
          self.batch_source = _input
          self.stream_source = stream_source
  
          self.materialization_intervals = []
  
      # Note: Python requires redefining hash in child classes that override __eq__
      def __hash__(self):
          return super().__hash__()
  
      def __copy__(self):
          fv = FeatureView(
              name=self.name,
              entities=self.entities,
              ttl=self.ttl,
              input=self.input,
              batch_source=self.batch_source,
              stream_source=self.stream_source,
              features=self.features,
              tags=self.tags,
              online=self.online,
          )
          fv.projection = copy.copy(self.projection)
          return fv
  
      def __eq__(self, other):
          if not isinstance(other, FeatureView):
              raise TypeError(
                  "Comparisons should only involve FeatureView class objects."
              )
  
          if not super().__eq__(other):
              return False
  
          if (
              self.tags != other.tags
              or self.ttl != other.ttl
              or self.online != other.online
          ):
              return False
  
          if sorted(self.entities) != sorted(other.entities):
              return False
          if self.batch_source != other.batch_source:
              return False
          if self.stream_source != other.stream_source:
              return False
  
          return True
  
      def ensure_valid(self):
          """
          Validates the state of this feature view locally.
  
          Raises:
              ValueError: The feature view does not have a name or does not have entities.
          """
          super().ensure_valid()
  
          if not self.entities:
              raise ValueError("Feature view has no entities.")
  
      @property
      def proto_class(self) -> Type[FeatureViewProto]:
          return FeatureViewProto
  
      def with_name(self, name: str):
          """
          Renames this feature view by returning a copy of this feature view with an alias
          set for the feature view name. This rename operation is only used as part of query
          operations and will not modify the underlying FeatureView.
  
          Args:
              name: Name to assign to the FeatureView copy.
  
          Returns:
              A copy of this FeatureView with the name replaced with the 'name' input.
          """
          cp = self.__copy__()
          cp.projection.name_alias = name
  
          return cp
  
      def with_join_key_map(self, join_key_map: Dict[str, str]):
          """
          Sets the join_key_map by returning a copy of this feature view with that field set.
          This join_key mapping operation is only used as part of query operations and will
          not modify the underlying FeatureView.
  
          Args:
              join_key_map: A map of join keys in which the left is the join_key that
                  corresponds with the feature data and the right corresponds with the entity data.
  
          Returns:
              A copy of this FeatureView with the join_key_map replaced with the 'join_key_map' input.
  
          Examples:
              Join a location feature data table to both the origin column and destination
              column of the entity data.
  
              temperatures_feature_service = FeatureService(
                  name="temperatures",
                  features=[
                      location_stats_feature_view
                          .with_name("origin_stats")
                          .with_join_key_map(
                              {"location_id": "origin_id"}
                          ),
                      location_stats_feature_view
                          .with_name("destination_stats")
                          .with_join_key_map(
                              {"location_id": "destination_id"}
                          ),
                  ],
              )
          """
          cp = self.__copy__()
          cp.projection.join_key_map = join_key_map
  
          return cp
  
      def with_projection(self, feature_view_projection: FeatureViewProjection):
          """
          Sets the feature view projection by returning a copy of this feature view
          with its projection set to the given projection. A projection is an
          object that stores the modifications to a feature view that is used during
          query operations.
  
          Args:
              feature_view_projection: The FeatureViewProjection object to link to this
                  OnDemandFeatureView.
  
          Returns:
              A copy of this FeatureView with its projection replaced with the 'feature_view_projection'
              argument.
          """
          if feature_view_projection.name != self.name:
              raise ValueError(
                  f"The projection for the {self.name} FeatureView cannot be applied because it differs in name. "
                  f"The projection is named {feature_view_projection.name} and the name indicates which "
                  "FeatureView the projection is for."
              )
  
          for feature in feature_view_projection.features:
              if feature not in self.features:
                  raise ValueError(
                      f"The projection for {self.name} cannot be applied because it contains {feature.name} which the "
                      "FeatureView doesn't have."
                  )
  
          cp = self.__copy__()
          cp.projection = feature_view_projection
  
          return cp
  
      def to_proto(self) -> FeatureViewProto:
          """
          Converts a feature view object to its protobuf representation.
  
          Returns:
              A FeatureViewProto protobuf.
          """
          meta = FeatureViewMetaProto(materialization_intervals=[])
          if self.created_timestamp:
              meta.created_timestamp.FromDatetime(self.created_timestamp)
          if self.last_updated_timestamp:
              meta.last_updated_timestamp.FromDatetime(self.last_updated_timestamp)
          for interval in self.materialization_intervals:
              interval_proto = MaterializationIntervalProto()
              interval_proto.start_time.FromDatetime(interval[0])
              interval_proto.end_time.FromDatetime(interval[1])
              meta.materialization_intervals.append(interval_proto)
  
          ttl_duration = None
          if self.ttl is not None:
              ttl_duration = Duration()
              ttl_duration.FromTimedelta(self.ttl)
  
          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 = FeatureViewSpecProto(
              name=self.name,
              entities=self.entities,
              features=[feature.to_proto() for feature in self.features],
              tags=self.tags,
              ttl=(ttl_duration if ttl_duration is not None else None),
              online=self.online,
              batch_source=batch_source_proto,
              stream_source=stream_source_proto,
          )
  
          return FeatureViewProto(spec=spec, meta=meta)
  
      @classmethod
      def from_proto(cls, feature_view_proto: FeatureViewProto):
          """
          Creates a feature view from a protobuf representation of a feature view.
  
          Args:
              feature_view_proto: A protobuf representation of a feature view.
  
          Returns:
              A FeatureViewProto object based on the feature view protobuf.
          """
          batch_source = DataSource.from_proto(feature_view_proto.spec.batch_source)
          stream_source = (
              DataSource.from_proto(feature_view_proto.spec.stream_source)
              if feature_view_proto.spec.HasField("stream_source")
              else None
          )
          feature_view = cls(
              name=feature_view_proto.spec.name,
              entities=[entity for entity in feature_view_proto.spec.entities],
              features=[
                  Feature(
                      name=feature.name,
                      dtype=ValueType(feature.value_type),
                      labels=dict(feature.labels),
                  )
                  for feature in feature_view_proto.spec.features
              ],
              tags=dict(feature_view_proto.spec.tags),
              online=feature_view_proto.spec.online,
              ttl=(
                  None
                  if feature_view_proto.spec.ttl.seconds == 0
                  and feature_view_proto.spec.ttl.nanos == 0
                  else feature_view_proto.spec.ttl
              ),
              batch_source=batch_source,
              stream_source=stream_source,
          )
  
          # FeatureViewProjections are not saved in the FeatureView proto.
          # Create the default projection.
          feature_view.projection = FeatureViewProjection.from_definition(feature_view)
  
          if feature_view_proto.meta.HasField("created_timestamp"):
              feature_view.created_timestamp = (
                  feature_view_proto.meta.created_timestamp.ToDatetime()
              )
          if feature_view_proto.meta.HasField("last_updated_timestamp"):
              feature_view.last_updated_timestamp = (
                  feature_view_proto.meta.last_updated_timestamp.ToDatetime()
              )
  
          for interval in feature_view_proto.meta.materialization_intervals:
              feature_view.materialization_intervals.append(
                  (
                      utils.make_tzaware(interval.start_time.ToDatetime()),
                      utils.make_tzaware(interval.end_time.ToDatetime()),
                  )
              )
  
          return feature_view
  
      @property
      def most_recent_end_time(self) -> Optional[datetime]:
          """
          Retrieves the latest time up to which the feature view has been materialized.
  
          Returns:
              The latest time, or None if the feature view has not been materialized.
          """
          if len(self.materialization_intervals) == 0:
              return None
          return max([interval[1] for interval in self.materialization_intervals])
  