from dataclasses import dataclass
from typing import Optional, Sequence, List, Tuple

from tools.codegen.api.types import *
from tools.codegen.model import *

# Represents a saved attribute involved in backward calculation.
# Note that it can be a derived property of an input argument, e.g.:
# we could save `other.scalar_type()` instead of the entire `other` tensor.
@dataclass(frozen=True)
class SavedAttribute:
    # Name of the saved attribute.
    # Suffix is appended if it's derived property, e.g.: `other_scalar_type`
    name: str

    # The cpp type string.
    # TODO: change from raw string to model.Type
    type: str

    # The expression to read the derived property at save time, e.g.:
    # `other.scalar_type()`.
    expr: str

# Represents a backward formula that calculates derivatives for one
# or more tensors.
@dataclass(frozen=True)
class Derivative:
    # The formula string (legit C++ expression).
    # Note that expressions against input arguments have been replaced with the
    # corresponding saved attributes.
    # E.g.:
    #  raw formula: `mul_tensor_backward(grad, self, other.scalar_type())`
    #         here: `mul_tensor_backward(grad, self, other_scalar_type)`
    formula: str

    # Names of the arguments for which this formula calculates derivatives.
    var_names: Tuple[str, ...]

    # Saved inputs that are referenced by the formula.
    saved_inputs: Tuple[SavedAttribute, ...]

    # Saved outputs that are referenced by the formula.
    saved_outputs: Tuple[SavedAttribute, ...]

# Represents differentiability info for a NativeFunction.
@dataclass(frozen=True)
class DifferentiabilityInfo:
    # The base name read from derivatives.yaml.
    name: str

    # The matching native function.
    #
    # There can be multiple NativeFunction having the same base name:
    #  - different overloads with different types of input arguments;
    #  - in-place/out/functional variants of the same function;
    #
    # We first use the schema string (under the 'name' key) in derivatives.yaml
    # to find the NativeFunction having the same schema string.
    # Then we find the in-place/out/functional variants of the matching function.
    # Among these variants, we choose the one having the same name as the
    # derivatives.yaml entry. If there is no exact match, then we choose the
    # in-place variant.
    # TODO: maybe the logic to search for all variants is no longer necessary?
    func: NativeFunction

    # The name of the generated autograd function.
    # It's set only if we will calculate a derivative, i.e.
    # 'args_with_derivatives' is not empty.
    op: Optional[str]

    # The derivatives formulae for this function.
    derivatives: Sequence[Derivative]

    # The union of 'saved_inputs' of all 'derivatives'.
    all_saved_inputs: Sequence[SavedAttribute]

    # The union of 'saved_outputs' of all 'derivatives'.
    all_saved_outputs: Sequence[SavedAttribute]

    # The function's input arguments for which it calculates derivatives.
    # It's the union of 'var_names' of all 'derivatives', sorted by the
    # argument order in the function schema.
    args_with_derivatives: Sequence[Binding]

    # Names of arguments whose derivative formula is 'non_differentiable'.
    non_differentiable_arg_names: Sequence[str]

    # Raw data read from derivatives.yaml.
    output_differentiability: Optional[List[bool]]

    @property
    def has_derivatives(self) -> bool:
        return len(self.args_with_derivatives) > 0

# Represents a differentiable `Argument`.
# How is it different from the `Argument` type?
# - It's processed Arguments which are differentiable and only used in the
#   context of the autograd codegen;
# - It can represent SelfArgument or regular Argument but not TensorOptionsArgument;
@dataclass(frozen=True)
class DifferentiableInput:
    name: str
    type: Type

    # TODO: only to keep it byte-for-byte compatible with the old codegen, should remove.
    cpp_type: str

# Represents a differentiable `Return`.
# How it it different from the `Return` type?
# - The name in `Return` is optional. Here it is always populated using the same
#   `cpp.return_names()` method.
#   TODO: some cpp naming logic (e.g. resolving name conflict) might be irrelevant?
# - It's processed Returns which are differentiable, in compliance with the
#   `output_differentiability` field defined in derivatives.yaml (if specified),
#   and are only used in the context of the autograd codegen;
@dataclass(frozen=True)
class DifferentiableOutput:
    name: str
    type: Type

    # TODO: only to keep it byte-for-byte compatible with the old codegen, should remove.
    cpp_type: str