"""Test file for hnumpy tracing"""

import networkx as nx
import numpy
import pytest

from hdk.common.data_types.floats import Float
from hdk.common.data_types.integers import Integer
from hdk.common.data_types.values import ClearValue, EncryptedValue
from hdk.common.representation import intermediate as ir
from hdk.hnumpy import tracing

OPERATIONS_TO_TEST = [ir.Add, ir.Sub, ir.Mul]


@pytest.mark.parametrize(
    "operation",
    OPERATIONS_TO_TEST,
)
@pytest.mark.parametrize(
    "x",
    [
        pytest.param(EncryptedValue(Integer(64, is_signed=False)), id="x: Encrypted uint"),
        pytest.param(
            EncryptedValue(Integer(64, is_signed=True)),
            id="x: Encrypted int",
        ),
        pytest.param(
            ClearValue(Integer(64, is_signed=False)),
            id="x: Clear uint",
        ),
        pytest.param(
            ClearValue(Integer(64, is_signed=True)),
            id="x: Clear int",
        ),
    ],
)
@pytest.mark.parametrize(
    "y",
    [
        pytest.param(EncryptedValue(Integer(64, is_signed=False)), id="y: Encrypted uint"),
        pytest.param(
            EncryptedValue(Integer(64, is_signed=True)),
            id="y: Encrypted int",
        ),
        pytest.param(
            ClearValue(Integer(64, is_signed=False)),
            id="y: Clear uint",
        ),
        pytest.param(
            ClearValue(Integer(64, is_signed=True)),
            id="y: Clear int",
        ),
    ],
)
def test_hnumpy_tracing_binary_op(operation, x, y, test_helpers):
    "Test hnumpy tracing a binary operation (in the supported ops)"

    # Remark that the functions here have a common structure (which is
    # 2x op y), such that creating further the ref_graph is easy, by
    # hand
    def simple_add_function(x, y):
        z = x + x
        return z + y

    def simple_sub_function(x, y):
        z = x + x
        return z - y

    def simple_mul_function(x, y):
        z = x + x
        return z * y

    assert operation in OPERATIONS_TO_TEST, f"unknown operation {operation}"
    if operation == ir.Add:
        function_to_compile = simple_add_function
    elif operation == ir.Sub:
        function_to_compile = simple_sub_function
    elif operation == ir.Mul:
        function_to_compile = simple_mul_function

    op_graph = tracing.trace_numpy_function(function_to_compile, {"x": x, "y": y})

    ref_graph = nx.MultiDiGraph()

    input_x = ir.Input(x, input_name="x", program_input_idx=0)
    input_y = ir.Input(y, input_name="y", program_input_idx=1)

    add_node_z = ir.Add(
        (
            input_x.outputs[0],
            input_x.outputs[0],
        )
    )

    returned_final_node = operation(
        (
            add_node_z.outputs[0],
            input_y.outputs[0],
        )
    )

    ref_graph.add_node(input_x, content=input_x)
    ref_graph.add_node(input_y, content=input_y)
    ref_graph.add_node(add_node_z, content=add_node_z)
    ref_graph.add_node(returned_final_node, content=returned_final_node)

    ref_graph.add_edge(input_x, add_node_z, input_idx=0)
    ref_graph.add_edge(input_x, add_node_z, input_idx=1)

    ref_graph.add_edge(add_node_z, returned_final_node, input_idx=0)
    ref_graph.add_edge(input_y, returned_final_node, input_idx=1)

    assert test_helpers.digraphs_are_equivalent(ref_graph, op_graph.graph)


@pytest.mark.parametrize(
    "function_to_trace,op_graph_expected_output_type,input_and_expected_output_tuples",
    [
        (
            lambda x: x.astype(numpy.int32),
            Integer(32, is_signed=True),
            [
                (14, numpy.int32(14)),
                (1.5, numpy.int32(1)),
                (2.0, numpy.int32(2)),
                (-1.5, numpy.int32(-1)),
                (2 ** 31 - 1, numpy.int32(2 ** 31 - 1)),
                (-(2 ** 31), numpy.int32(-(2 ** 31))),
            ],
        ),
        (
            lambda x: x.astype(numpy.uint32),
            Integer(32, is_signed=False),
            [
                (14, numpy.uint32(14)),
                (1.5, numpy.uint32(1)),
                (2.0, numpy.uint32(2)),
                (2 ** 32 - 1, numpy.uint32(2 ** 32 - 1)),
            ],
        ),
        (
            lambda x: x.astype(numpy.int64),
            Integer(64, is_signed=True),
            [
                (14, numpy.int64(14)),
                (1.5, numpy.int64(1)),
                (2.0, numpy.int64(2)),
                (-1.5, numpy.int64(-1)),
                (2 ** 63 - 1, numpy.int64(2 ** 63 - 1)),
                (-(2 ** 63), numpy.int64(-(2 ** 63))),
            ],
        ),
        (
            lambda x: x.astype(numpy.uint64),
            Integer(64, is_signed=False),
            [
                (14, numpy.uint64(14)),
                (1.5, numpy.uint64(1)),
                (2.0, numpy.uint64(2)),
                (2 ** 64 - 1, numpy.uint64(2 ** 64 - 1)),
            ],
        ),
        (
            lambda x: x.astype(numpy.float64),
            Float(64),
            [
                (14, numpy.float64(14.0)),
                (1.5, numpy.float64(1.5)),
                (2.0, numpy.float64(2.0)),
                (-1.5, numpy.float64(-1.5)),
            ],
        ),
        (
            lambda x: x.astype(numpy.float32),
            Float(32),
            [
                (14, numpy.float32(14.0)),
                (1.5, numpy.float32(1.5)),
                (2.0, numpy.float32(2.0)),
                (-1.5, numpy.float32(-1.5)),
            ],
        ),
    ],
)
def test_tracing_astype(
    function_to_trace, op_graph_expected_output_type, input_and_expected_output_tuples
):
    """Test function for NPTracer.astype"""
    for input_, expected_output in input_and_expected_output_tuples:
        input_value = (
            EncryptedValue(Integer(64, is_signed=True))
            if isinstance(input_, int)
            else EncryptedValue(Float(64))
        )

        op_graph = tracing.trace_numpy_function(function_to_trace, {"x": input_value})
        output_node = op_graph.output_nodes[0]
        assert op_graph_expected_output_type == output_node.outputs[0].data_type

        node_results = op_graph.evaluate({0: numpy.array(input_)})
        evaluated_output = node_results[output_node]
        assert isinstance(evaluated_output, type(expected_output))
        assert expected_output == evaluated_output


@pytest.mark.parametrize(
    "function_to_trace",
    [
        # We cannot call trace_numpy_function on some numpy function as getting the signature for
        # these functions fails, so we wrap it in a lambda
        # pylint: disable=unnecessary-lambda
        pytest.param(lambda x: numpy.rint(x)),
        pytest.param(lambda x: numpy.sin(x)),
        # The next test case is only for coverage purposes, to trigger the unsupported method
        # exception handling
        pytest.param(
            lambda x: numpy.add.reduce(x),
            marks=pytest.mark.xfail(strict=True, raises=NotImplementedError),
        ),
        # pylint: enable=unnecessary-lambda
    ],
)
@pytest.mark.parametrize(
    "inputs,expected_output_node,expected_output_value",
    [
        pytest.param(
            {"x": EncryptedValue(Integer(7, is_signed=False))},
            ir.ArbitraryFunction,
            EncryptedValue(Float(64)),
        ),
        pytest.param(
            {"x": EncryptedValue(Integer(32, is_signed=True))},
            ir.ArbitraryFunction,
            EncryptedValue(Float(64)),
        ),
        pytest.param(
            {"x": EncryptedValue(Integer(64, is_signed=True))},
            ir.ArbitraryFunction,
            EncryptedValue(Float(64)),
        ),
        pytest.param(
            {"x": EncryptedValue(Integer(128, is_signed=True))},
            ir.ArbitraryFunction,
            None,
            marks=pytest.mark.xfail(strict=True, raises=NotImplementedError),
        ),
        pytest.param(
            {"x": EncryptedValue(Float(64))},
            ir.ArbitraryFunction,
            EncryptedValue(Float(64)),
        ),
    ],
)
def test_trace_hnumpy_supported_ufuncs(
    function_to_trace, inputs, expected_output_node, expected_output_value
):
    """Function to trace supported numpy ufuncs"""
    op_graph = tracing.trace_numpy_function(function_to_trace, inputs)

    assert len(op_graph.output_nodes) == 1
    assert isinstance(op_graph.output_nodes[0], expected_output_node)
    assert len(op_graph.output_nodes[0].outputs) == 1
    assert op_graph.output_nodes[0].outputs[0] == expected_output_value


@pytest.mark.parametrize(
    "np_ufunc,expected_tracing_func",
    [
        pytest.param(numpy.rint, tracing.NPTracer.rint),
        pytest.param(numpy.sin, tracing.NPTracer.sin),
        # There is a need to test the case where the function fails, I chose numpy.conjugate which
        # works on complex types, as we don't talk about complex types for now this looks like a
        # good long term candidate to check for an unsupported function
        pytest.param(
            numpy.conjugate, None, marks=pytest.mark.xfail(strict=True, raises=NotImplementedError)
        ),
    ],
)
def test_nptracer_get_tracing_func_for_np_ufunc(np_ufunc, expected_tracing_func):
    """Test NPTracer get_tracing_func_for_np_ufunc"""
    assert tracing.NPTracer.get_tracing_func_for_np_ufunc(np_ufunc) == expected_tracing_func