import numpy as np
import pytest

import pytensor
import pytensor.tensor as pt
from pytensor import scalar as ps
from pytensor.configdefaults import config
from pytensor.graph import utils
from pytensor.graph.basic import Apply
from pytensor.graph.op import Op
from pytensor.graph.type import Type
from pytensor.link.c.op import COp
from pytensor.tensor.math import _allclose, dot
from pytensor.tensor.type import fmatrix, iscalar, matrix, vector


@pytest.fixture(scope="module", autouse=True)
def set_pytensor_flags():
    with config.change_flags(compute_test_value="raise"):
        yield


class IncOneC(COp):
    """
    An Op with only a C (c_code) implementation
    """

    __props__ = ()

    def make_node(self, input):
        input = ps.as_scalar(input)
        output = input.type()
        return Apply(self, [input], [output])

    def c_code_cache_version(self):
        return (1,)

    def c_code(self, node, name, inputs, outputs, sub):
        (x,) = inputs
        (z,) = outputs
        return f"{z} = {x} + 1;"

    def perform(self, *args, **kwargs):
        raise NotImplementedError()


class TestComputeTestValue:
    def test_destroy_map(self):
        class SomeType(Type):
            def filter(self, data, strict=False, allow_downcast=None):
                return data

        class InplaceOp(Op):
            __props__ = ()

            def __init__(self, inplace):
                if inplace:
                    self.destroy_map = {0: [0]}

                super().__init__()

            def make_node(self, input):
                return Apply(self, [input], [input.type()])

            def perform(self, node, inputs, outputs):
                outputs[0][0] = inputs[0]

        test_input = SomeType()()
        orig_object = object()
        test_input.tag.test_value = orig_object

        res = InplaceOp(False)(test_input)
        assert res.tag.test_value is orig_object

        res = InplaceOp(True)(test_input)
        assert res.tag.test_value is not orig_object

    def test_variable_only(self):
        x = matrix("x")
        x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
        y = matrix("y")
        y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)

        # should work
        z = dot(x, y)
        assert hasattr(z.tag, "test_value")
        f = pytensor.function([x, y], z)
        assert _allclose(f(x.tag.test_value, y.tag.test_value), z.tag.test_value)

        # this test should fail
        y.tag.test_value = np.random.random((6, 5)).astype(config.floatX)
        with pytest.raises(ValueError):
            dot(x, y)

    def test_compute_flag(self):
        x = matrix("x")
        y = matrix("y")
        y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)

        # should skip computation of test value
        with config.change_flags(compute_test_value="off"):
            z = dot(x, y)
            assert not hasattr(z.tag, "test_value")

        # should fail when asked by user
        with pytest.raises(ValueError), config.change_flags(compute_test_value="raise"):
            dot(x, y)

        # test that a warning is raised if required
        with pytest.warns(UserWarning), config.change_flags(compute_test_value="warn"):
            dot(x, y)

    def test_string_var(self):
        x = matrix("x")
        x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
        y = matrix("y")
        y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)

        z = pytensor.shared(np.random.random((5, 6)).astype(config.floatX))

        # should work
        out = dot(dot(x, y), z)
        assert hasattr(out.tag, "test_value")
        tf = pytensor.function([x, y], out)
        assert _allclose(tf(x.tag.test_value, y.tag.test_value), out.tag.test_value)

        def f(x, y, z):
            return dot(dot(x, y), z)

        # this test should fail
        z.set_value(np.random.random((7, 6)).astype(config.floatX))
        with pytest.raises(ValueError):
            f(x, y, z)

    def test_shared(self):
        x = matrix("x")
        x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
        y = pytensor.shared(np.random.random((4, 6)).astype(config.floatX), "y")

        # should work
        z = dot(x, y)
        assert hasattr(z.tag, "test_value")
        f = pytensor.function([x], z)
        assert _allclose(f(x.tag.test_value), z.tag.test_value)

        # this test should fail
        y.set_value(np.random.random((5, 6)).astype(config.floatX))
        with pytest.raises(ValueError):
            dot(x, y)

    def test_ndarray(self):
        x = np.random.random((2, 3)).astype(config.floatX)
        y = pytensor.shared(np.random.random((3, 6)).astype(config.floatX), "y")

        # should work
        z = dot(x, y)
        assert hasattr(z.tag, "test_value")
        f = pytensor.function([], z)
        assert _allclose(f(), z.tag.test_value)

        # this test should fail
        x = np.random.random((2, 4)).astype(config.floatX)
        with pytest.raises(ValueError):
            dot(x, y)

    def test_empty_elemwise(self):
        x = pytensor.shared(np.random.random((0, 6)).astype(config.floatX), "x")

        # should work
        z = (x + 2) * 3
        assert hasattr(z.tag, "test_value")
        f = pytensor.function([], z)
        assert _allclose(f(), z.tag.test_value)

    def test_constant(self):
        x = pt.constant(np.random.random((2, 3)), dtype=config.floatX)
        y = pytensor.shared(np.random.random((3, 6)).astype(config.floatX), "y")

        # should work
        z = dot(x, y)
        assert hasattr(z.tag, "test_value")
        f = pytensor.function([], z)
        assert _allclose(f(), z.tag.test_value)

        # this test should fail
        x = pt.constant(np.random.random((2, 4)), dtype=config.floatX)
        with pytest.raises(ValueError):
            dot(x, y)

    def test_incorrect_type(self):
        x = vector("x")
        with pytest.raises(TypeError):
            # Incorrect shape for test value
            x.tag.test_value = np.empty((2, 2))

        x = fmatrix("x")
        with pytest.raises(TypeError):
            # Incorrect dtype (float64) for test value
            x.tag.test_value = np.random.random((3, 4))

    def test_overided_function(self):
        # We need to test those as they mess with Exception
        # And we don't want the exception to be changed.
        x = matrix()
        x.tag.test_value = np.zeros((2, 3), dtype=config.floatX)
        y = matrix()
        y.tag.test_value = np.zeros((2, 2), dtype=config.floatX)
        with pytest.raises(ValueError):
            x.__mul__(y)

    def test_scan(self):
        # Test the compute_test_value mechanism Scan.
        k = iscalar("k")
        A = vector("A")
        k.tag.test_value = 3
        A.tag.test_value = np.random.random((5,)).astype(config.floatX)

        def fx(prior_result, A):
            return prior_result * A

        # Symbolic description of the result
        result, updates = pytensor.scan(
            fn=fx, outputs_info=pt.ones_like(A), non_sequences=A, n_steps=k
        )

        # We only care about A**k, but scan has provided us with A**1 through A**k.
        # Discard the values that we don't care about. Scan is smart enough to
        # notice this and not waste memory saving them.
        final_result = result[-1]
        assert hasattr(final_result.tag, "test_value")

    def test_scan_err1(self):
        # This test should fail when building fx for the first time
        k = iscalar("k")
        A = matrix("A")
        k.tag.test_value = 3
        A.tag.test_value = np.random.random((5, 3)).astype(config.floatX)

        def fx(prior_result, A):
            return dot(prior_result, A)

        with pytest.raises(ValueError) as e:
            pytensor.scan(
                fn=fx, outputs_info=pt.ones_like(A), non_sequences=A, n_steps=k
            )

        assert str(e.traceback[0].path).endswith("test_compute_test_value.py")
        # We should be in the "fx" function defined above
        assert e.traceback[2].name == "fx"

    def test_scan_err2(self):
        # This test should not fail when building fx for the first time,
        # but when calling the scan's perform()
        k = iscalar("k")
        A = matrix("A")
        k.tag.test_value = 3
        A.tag.test_value = np.random.random((5, 3)).astype(config.floatX)

        def fx(prior_result, A):
            return dot(prior_result, A)

        with pytest.raises(ValueError):
            pytensor.scan(
                fn=fx, outputs_info=pt.ones_like(A.T), non_sequences=A, n_steps=k
            )

        with pytest.raises(ValueError, match="^could not broadcast input"):
            pytensor.scan(
                fn=fx, outputs_info=pt.ones_like(A.T), non_sequences=A, n_steps=k
            )

    def test_no_c_code(self):
        class IncOnePython(COp):
            """
            An Op with only a Python (perform) implementation
            """

            __props__ = ()

            def make_node(self, input):
                input = ps.as_scalar(input)
                output = input.type()
                return Apply(self, [input], [output])

            def perform(self, node, inputs, outputs):
                (input,) = inputs
                (output,) = outputs
                output[0] = input + 1

        i = ps.int32("i")
        i.tag.test_value = 3

        o = IncOnePython()(i)

        # Check that the c_code function is not implemented
        with pytest.raises((NotImplementedError, utils.MethodNotDefined)):
            o.owner.op.c_code(o.owner, "o", ["x"], "z", {"fail": ""})

        assert hasattr(o.tag, "test_value")
        assert o.tag.test_value == 4

    @pytest.mark.skipif(
        not config.cxx, reason="G++ not available, so we need to skip this test."
    )
    def test_no_perform(self):
        i = ps.int32("i")
        i.tag.test_value = 3

        # Class IncOneC is defined outside of the TestComputeTestValue
        # so it can be pickled and unpickled
        o = IncOneC()(i)

        # Check that the perform function is not implemented
        with pytest.raises((NotImplementedError, utils.MethodNotDefined)):
            o.owner.op.perform(o.owner, 0, [None])

        assert hasattr(o.tag, "test_value")
        assert o.tag.test_value == 4

    def test_disabled_during_compilation(self):
        # We test that it is disabled when we include deep copy in the code
        # This don't test that it is disabled during optimization, but the code do it.
        init_Mu1 = pytensor.shared(np.zeros((5,), dtype=config.floatX)).dimshuffle(
            "x", 0
        )

        pytensor.function([], outputs=[init_Mu1])
