import numpy as np
import pytest
from cons import car, cdr
from cons.core import ConsError
from etuples import apply, etuple, etuplize
from etuples.core import ExpressionTuple
from unification import reify, unify, var
from unification.variable import Var

import aesara.scalar as aes
import aesara.tensor as at
from aesara.graph.basic import Apply, Constant, equal_computations
from aesara.graph.op import Op
from aesara.graph.unify import ConstrainedVar, convert_strs_to_vars
from aesara.tensor.type import TensorType
from tests.graph.utils import MyType


class CustomOp(Op):

    __props__ = ("a",)

    def __init__(self, a):
        self.a = a

    def make_node(self, *inputs):
        return Apply(self, list(inputs), [at.vector()])

    def perform(self, node, inputs, outputs):
        raise NotImplementedError()


class CustomOpNoPropsNoEq(Op):
    def __init__(self, a):
        self.a = a

    def make_node(self, *inputs):
        return Apply(self, list(inputs), [at.vector()])

    def perform(self, node, inputs, outputs):
        raise NotImplementedError()


class CustomOpNoProps(CustomOpNoPropsNoEq):
    def __eq__(self, other):
        return type(self) == type(other) and self.a == other.a

    def __hash__(self):
        return hash((type(self), self.a))


def test_cons():
    x_at = at.vector("x")
    y_at = at.vector("y")

    z_at = x_at + y_at

    res = car(z_at)
    assert res == z_at.owner.op

    res = cdr(z_at)
    assert res == [x_at, y_at]

    with pytest.raises(ConsError):
        car(x_at)

    with pytest.raises(ConsError):
        cdr(x_at)

    op1 = CustomOp(1)

    assert car(op1) == CustomOp
    assert cdr(op1) == (1,)

    tt1 = TensorType("float32", [True, False])

    assert car(tt1) == TensorType
    assert cdr(tt1) == ("float32", (1, None))

    op1_np = CustomOpNoProps(1)

    with pytest.raises(ConsError):
        car(op1_np)

    with pytest.raises(ConsError):
        cdr(op1_np)

    atype_at = aes.float64
    car_res = car(atype_at)
    cdr_res = cdr(atype_at)
    assert car_res is type(atype_at)
    assert cdr_res == [atype_at.dtype]

    atype_at = at.lvector
    car_res = car(atype_at)
    cdr_res = cdr(atype_at)
    assert car_res is type(atype_at)
    assert cdr_res == [atype_at.dtype, atype_at.shape]


def test_etuples():
    x_at = at.vector("x")
    y_at = at.vector("y")

    z_at = etuple(x_at, y_at)

    res = apply(at.add, z_at)

    assert res.owner.op == at.add
    assert res.owner.inputs == [x_at, y_at]

    w_at = etuple(at.add, x_at, y_at)

    res = w_at.evaled_obj
    assert res.owner.op == at.add
    assert res.owner.inputs == [x_at, y_at]

    # This `Op` doesn't expand into an `etuple` (i.e. it's "atomic")
    op1_np = CustomOpNoProps(1)

    res = apply(op1_np, z_at)
    assert res.owner.op == op1_np

    q_at = op1_np(x_at, y_at)
    res = etuplize(q_at)
    assert res[0] == op1_np

    with pytest.raises(TypeError):
        etuplize(op1_np)

    class MyMultiOutOp(Op):
        def make_node(self, *inputs):
            outputs = [MyType()(), MyType()()]
            return Apply(self, list(inputs), outputs)

        def perform(self, node, inputs, outputs):
            outputs[0] = np.array(inputs[0])
            outputs[1] = np.array(inputs[0])

    x_at = at.vector("x")
    op1_np = MyMultiOutOp()
    res = apply(op1_np, etuple(x_at))
    assert len(res) == 2
    assert res[0].owner.op == op1_np
    assert res[1].owner.op == op1_np


def test_unify_Variable():
    x_at = at.vector("x")
    y_at = at.vector("y")

    z_at = x_at + y_at

    # `Variable`, `Variable`
    s = unify(z_at, z_at)
    assert s == {}

    # These `Variable`s have no owners
    v1 = MyType()()
    v2 = MyType()()

    assert v1 != v2

    s = unify(v1, v2)
    assert s is False

    op_lv = var()
    z_pat_et = etuple(op_lv, x_at, y_at)

    # `Variable`, `ExpressionTuple`
    s = unify(z_at, z_pat_et, {})

    assert op_lv in s
    assert s[op_lv] == z_at.owner.op

    res = reify(z_pat_et, s)

    assert isinstance(res, ExpressionTuple)
    assert equal_computations([res.evaled_obj], [z_at])

    z_et = etuple(at.add, x_at, y_at)

    # `ExpressionTuple`, `ExpressionTuple`
    s = unify(z_et, z_pat_et, {})

    assert op_lv in s
    assert s[op_lv] == z_et[0]

    res = reify(z_pat_et, s)

    assert isinstance(res, ExpressionTuple)
    assert equal_computations([res.evaled_obj], [z_et.evaled_obj])

    # `ExpressionTuple`, `Variable`
    s = unify(z_et, x_at, {})
    assert s is False

    # This `Op` doesn't expand into an `ExpressionTuple`
    op1_np = CustomOpNoProps(1)

    q_at = op1_np(x_at, y_at)

    a_lv = var()
    b_lv = var()
    # `Variable`, `ExpressionTuple`
    s = unify(q_at, etuple(op1_np, a_lv, b_lv))

    assert s[a_lv] == x_at
    assert s[b_lv] == y_at


def test_unify_Op():
    # These `Op`s expand into `ExpressionTuple`s
    op1 = CustomOp(1)
    op2 = CustomOp(1)

    # `Op`, `Op`
    s = unify(op1, op2)
    assert s == {}

    # `ExpressionTuple`, `Op`
    s = unify(etuplize(op1), op2)
    assert s == {}

    # These `Op`s don't expand into `ExpressionTuple`s
    op1_np = CustomOpNoProps(1)
    op2_np = CustomOpNoProps(1)

    s = unify(op1_np, op2_np)
    assert s == {}

    # Same, but this one also doesn't implement `__eq__`
    op1_np_neq = CustomOpNoPropsNoEq(1)

    s = unify(op1_np_neq, etuplize(op1))
    assert s is False


def test_unify_Constant():
    # Make sure `Constant` unification works
    c1_at = at.as_tensor(np.r_[1, 2])
    c2_at = at.as_tensor(np.r_[1, 2])

    # `Constant`, `Constant`
    s = unify(c1_at, c2_at)
    assert s == {}


def test_unify_Type():
    t1 = TensorType(np.float64, (True, False))
    t2 = TensorType(np.float64, (True, False))

    # `Type`, `Type`
    s = unify(t1, t2)
    assert s == {}

    # `Type`, `ExpressionTuple`
    s = unify(t1, etuple(TensorType, "float64", (1, None)))
    assert s == {}

    from aesara.scalar.basic import ScalarType

    st1 = ScalarType(np.float64)
    st2 = ScalarType(np.float64)

    s = unify(st1, st2)
    assert s == {}


def test_ConstrainedVar():
    cvar = ConstrainedVar(lambda x: isinstance(x, str))

    assert repr(cvar).startswith("ConstrainedVar(")
    assert repr(cvar).endswith(f", {cvar.token})")

    s = unify(cvar, 1)
    assert s is False

    s = unify(1, cvar)
    assert s is False

    s = unify(cvar, "hi")
    assert s[cvar] == "hi"

    s = unify("hi", cvar)
    assert s[cvar] == "hi"

    x_lv = var()
    s = unify(cvar, x_lv)
    assert s == {cvar: x_lv}

    s = unify(cvar, x_lv, {x_lv: "hi"})
    assert s[cvar] == "hi"

    s = unify(x_lv, cvar, {x_lv: "hi"})
    assert s[cvar] == "hi"

    s_orig = {cvar: "hi", x_lv: "hi"}
    s = unify(x_lv, cvar, s_orig)
    assert s == s_orig

    s_orig = {cvar: "hi", x_lv: "bye"}
    s = unify(x_lv, cvar, s_orig)
    assert s is False

    x_at = at.vector("x")
    y_at = at.vector("y")
    op1_np = CustomOpNoProps(1)
    r_at = etuple(op1_np, x_at, y_at)

    def constraint(x):
        return isinstance(x, tuple)

    a_lv = ConstrainedVar(constraint)
    res = reify(etuple(op1_np, a_lv), {a_lv: r_at})

    assert res[1] == r_at


def test_convert_strs_to_vars():

    res = convert_strs_to_vars("a")
    assert isinstance(res, Var)
    assert res.token == "a"

    x_at = at.vector()
    y_at = at.vector()
    res = convert_strs_to_vars((("a", x_at), y_at))
    assert res == etuple(etuple(var("a"), x_at), y_at)

    def constraint(x):
        return isinstance(x, str)

    res = convert_strs_to_vars(
        (({"pattern": "a", "constraint": constraint}, x_at), y_at)
    )
    assert res == etuple(etuple(ConstrainedVar(constraint, "a"), x_at), y_at)

    # Make sure constrained logic variables are the same across distinct uses
    # of their string names
    res = convert_strs_to_vars(({"pattern": "a", "constraint": constraint}, "a"))
    assert res[0] is res[1]

    var_map = {"a": var("a")}
    res = convert_strs_to_vars(("a",), var_map=var_map)
    assert res[0] is var_map["a"]

    # Make sure numbers and NumPy arrays are converted
    val = np.r_[1, 2]
    res = convert_strs_to_vars((val,))
    assert isinstance(res[0], Constant)
    assert np.array_equal(res[0].data, val)
