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import re

import numpy as np
import pytensor.tensor as at
import pytest
import scipy.stats.distributions as sp

from pytensor.scalar import Exp, exp
from pytensor.tensor.random.basic import NormalRV

import pymc as pm

from pymc.logprob.abstract import (
    MeasurableElemwise,
    MeasurableVariable,
    UnmeasurableVariable,
    _get_measurable_outputs,
    assign_custom_measurable_outputs,
    logcdf,
    noop_measurable_outputs_fn,
)


def assert_equal_hash(classA, classB):
    assert hash(classA) == hash(classA.id_obj)
    assert hash(classB) == hash(classB.id_obj)
    assert classA == classB
    assert hash(classA) == hash(classB)


@pytest.mark.parametrize(
    "op, id_obj, class_dict",
    [
        (None, None, UnmeasurableVariable.__dict__),
        (None, (1, 2), UnmeasurableVariable.__dict__),
        (
            NormalRV,
            (NormalRV, noop_measurable_outputs_fn),
            UnmeasurableVariable.__dict__,
        ),
    ],
)
def test_unmeasurable_variable_class(op, id_obj, class_dict):
    A_dict = class_dict.copy()
    B_dict = class_dict.copy()

    if id_obj is not None:
        A_dict["id_obj"] = id_obj
        B_dict["id_obj"] = id_obj

    if op is None:
        parent_classes = (UnmeasurableVariable,)
    else:
        parent_classes = (op, UnmeasurableVariable)

    A = type("A", parent_classes, A_dict)
    B = type("B", parent_classes, B_dict)

    assert_equal_hash(A, B)


def test_unmeasurable_meta_hash_reassignment():
    A_dict = UnmeasurableVariable.__dict__.copy()
    B_dict = UnmeasurableVariable.__dict__.copy()

    A_dict["id_obj"] = (1, 2)
    B_dict["id_obj"] = (1, 3)

    A = type("A", (UnmeasurableVariable,), A_dict)
    B = type("B", (UnmeasurableVariable,), B_dict)

    assert A != B
    assert hash(A) != hash(B)

    A.id_obj = (1, 3)

    assert_equal_hash(A, B)


def test_assign_custom_measurable_outputs():
    srng = at.random.RandomStream(seed=2320)

    X_rv = srng.normal(-10.0, 0.1, name="X")
    Y_rv = srng.normal(10.0, 0.1, name="Y")

    # manually checking assign_custom_measurable_outputs
    unmeasurable_X = assign_custom_measurable_outputs(X_rv.owner).op
    unmeasurable_Y = assign_custom_measurable_outputs(Y_rv.owner).op

    assert_equal_hash(unmeasurable_X.__class__, unmeasurable_Y.__class__)
    assert unmeasurable_X.__class__.__name__.startswith("Unmeasurable")
    assert unmeasurable_X.__class__ in _get_measurable_outputs.registry

    # passing unmeasurable_X into assign_custom_measurable_outputs does nothing

    unmeas_X_rv = unmeasurable_X(-5, 0.1, name="unmeas_X")

    unmeasurable_X2_node = assign_custom_measurable_outputs(unmeas_X_rv.owner)
    unmeasurable_X2 = unmeasurable_X2_node.op

    assert unmeasurable_X2_node == unmeas_X_rv.owner
    assert_equal_hash(unmeasurable_X.__class__, unmeasurable_X2.__class__)

    with pytest.raises(ValueError):
        assign_custom_measurable_outputs(unmeas_X_rv.owner, lambda x: x)


def test_measurable_elemwise():
    # Default does not accept any scalar_op
    with pytest.raises(TypeError, match=re.escape("scalar_op exp is not valid")):
        MeasurableElemwise(exp)

    class TestMeasurableElemwise(MeasurableElemwise):
        valid_scalar_types = (Exp,)

    measurable_exp_op = TestMeasurableElemwise(scalar_op=exp)
    measurable_exp = measurable_exp_op(0.0)
    assert isinstance(measurable_exp.owner.op, MeasurableVariable)


def test_logcdf_helper():
    value = at.vector("value")
    x = pm.Normal.dist(0, 1)

    x_logcdf = logcdf(x, value)
    np.testing.assert_almost_equal(x_logcdf.eval({value: [0, 1]}), sp.norm(0, 1).logcdf([0, 1]))

    x_logcdf = logcdf(x, [0, 1])
    np.testing.assert_almost_equal(x_logcdf.eval(), sp.norm(0, 1).logcdf([0, 1]))


def test_logcdf_transformed_argument():
    with pm.Model() as m:
        sigma = pm.HalfFlat("sigma")
        x = pm.Normal("x", 0, sigma)
        pm.Potential("norm_term", -logcdf(x, 1.0))

    sigma_value_log = -1.0
    sigma_value = np.exp(sigma_value_log)
    x_value = 0.5

    observed = m.compile_logp(jacobian=False)({"sigma_log__": sigma_value_log, "x": x_value})
    expected = pm.logp(
        pm.TruncatedNormal.dist(0, sigma_value, lower=None, upper=1.0), x_value
    ).eval()
    assert np.isclose(observed, expected)
