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# Copyright 2023 Google LLC
#
# 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
#
# http://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 __future__ import annotations
import abc
import dataclasses
import itertools
import typing
from typing import Mapping, Union
import bigframes.dtypes as dtypes
import bigframes.operations
import bigframes.operations.aggregations as agg_ops
def const(value: typing.Hashable, dtype: dtypes.ExpressionType = None) -> Expression:
return ScalarConstantExpression(value, dtype or dtypes.infer_literal_type(value))
def free_var(id: str) -> UnboundVariableExpression:
return UnboundVariableExpression(id)
@dataclasses.dataclass(frozen=True)
class Aggregation(abc.ABC):
"""Represents windowing or aggregation over a column."""
op: agg_ops.WindowOp = dataclasses.field()
@abc.abstractmethod
def output_type(
self, input_types: dict[str, dtypes.ExpressionType]
) -> dtypes.ExpressionType:
...
@dataclasses.dataclass(frozen=True)
class UnaryAggregation(Aggregation):
op: agg_ops.UnaryWindowOp = dataclasses.field()
arg: Union[
UnboundVariableExpression, ScalarConstantExpression
] = dataclasses.field()
def output_type(
self, input_types: dict[str, bigframes.dtypes.Dtype]
) -> dtypes.ExpressionType:
return self.op.output_type(self.arg.output_type(input_types))
@dataclasses.dataclass(frozen=True)
class BinaryAggregation(Aggregation):
op: agg_ops.BinaryAggregateOp = dataclasses.field()
left: Union[
UnboundVariableExpression, ScalarConstantExpression
] = dataclasses.field()
right: Union[
UnboundVariableExpression, ScalarConstantExpression
] = dataclasses.field()
def output_type(
self, input_types: dict[str, bigframes.dtypes.Dtype]
) -> dtypes.ExpressionType:
return self.op.output_type(
self.left.output_type(input_types), self.right.output_type(input_types)
)
@dataclasses.dataclass(frozen=True)
class Expression(abc.ABC):
"""An expression represents a computation taking N scalar inputs and producing a single output scalar."""
@property
def unbound_variables(self) -> typing.Tuple[str, ...]:
return ()
def rename(self, name_mapping: Mapping[str, str]) -> Expression:
return self
@property
@abc.abstractmethod
def is_const(self) -> bool:
...
@abc.abstractmethod
def output_type(
self, input_types: dict[str, dtypes.ExpressionType]
) -> dtypes.ExpressionType:
...
@abc.abstractmethod
def bind_all_variables(self, bindings: Mapping[str, Expression]) -> Expression:
"""Replace all variables with expression given in `bindings`."""
...
@property
def is_bijective(self) -> bool:
return False
@property
def is_identity(self) -> bool:
"""True for identity operation that does not transform input."""
return False
@dataclasses.dataclass(frozen=True)
class ScalarConstantExpression(Expression):
"""An expression representing a scalar constant."""
# TODO: Further constrain?
value: typing.Hashable
dtype: dtypes.ExpressionType = None
@property
def is_const(self) -> bool:
return True
def output_type(
self, input_types: dict[str, bigframes.dtypes.Dtype]
) -> dtypes.ExpressionType:
return self.dtype
def bind_all_variables(self, bindings: Mapping[str, Expression]) -> Expression:
return self
@property
def is_bijective(self) -> bool:
# () <-> value
return True
@dataclasses.dataclass(frozen=True)
class UnboundVariableExpression(Expression):
"""A variable expression representing an unbound variable."""
id: str
@property
def unbound_variables(self) -> typing.Tuple[str, ...]:
return (self.id,)
def rename(self, name_mapping: Mapping[str, str]) -> Expression:
if self.id in name_mapping:
return UnboundVariableExpression(name_mapping[self.id])
else:
return self
@property
def is_const(self) -> bool:
return False
def output_type(
self, input_types: dict[str, bigframes.dtypes.Dtype]
) -> dtypes.ExpressionType:
if self.id in input_types:
return input_types[self.id]
else:
raise ValueError(f"Type of variable {self.id} has not been fixed.")
def bind_all_variables(self, bindings: Mapping[str, Expression]) -> Expression:
if self.id in bindings.keys():
return bindings[self.id]
else:
raise ValueError(f"Variable {self.id} remains unbound")
@property
def is_bijective(self) -> bool:
return True
@property
def is_identity(self) -> bool:
return True
@dataclasses.dataclass(frozen=True)
class OpExpression(Expression):
"""An expression representing a scalar operation applied to 1 or more argument sub-expressions."""
op: bigframes.operations.RowOp
inputs: typing.Tuple[Expression, ...]
@property
def unbound_variables(self) -> typing.Tuple[str, ...]:
return tuple(
itertools.chain.from_iterable(
map(lambda x: x.unbound_variables, self.inputs)
)
)
def rename(self, name_mapping: Mapping[str, str]) -> Expression:
return OpExpression(
self.op, tuple(input.rename(name_mapping) for input in self.inputs)
)
@property
def is_const(self) -> bool:
return all(child.is_const for child in self.inputs)
def output_type(
self, input_types: dict[str, dtypes.ExpressionType]
) -> dtypes.ExpressionType:
operand_types = tuple(
map(lambda x: x.output_type(input_types=input_types), self.inputs)
)
return self.op.output_type(*operand_types)
def bind_all_variables(self, bindings: Mapping[str, Expression]) -> Expression:
return OpExpression(
self.op,
tuple(input.bind_all_variables(bindings) for input in self.inputs),
)
@property
def is_bijective(self) -> bool:
# TODO: Mark individual functions as bijective?
return False