"""Unit tests for ``node_generator`` and ``edge_generator``.

Both take a populated ``networkx.DiGraph`` and yield the tuples BioCypher
expects. The label-normalisation logic (capitalize, ``resource`` ->
``resourceType``, skipping dummy/search/meta/link) is the interesting part.
"""

import networkx as nx
import pytest

import import_fhir_to_nx_diGraph as pipeline


def _emit(generator):
    return list(generator)


# --- node_generator --------------------------------------------------------

def test_resource_node_uses_resource_type_as_label():
    g = nx.DiGraph()
    g.add_node("p1", label="resource", resourceType="Patient", unique_id="P-1")
    (node_id, label, props), = _emit(pipeline.node_generator(g))
    assert node_id == "P-1"
    assert label == "Patient"


def test_label_is_capitalized():
    g = nx.DiGraph()
    g.add_node("o1", label="observation")
    (_, label, _), = _emit(pipeline.node_generator(g))
    assert label == "Observation"


def test_falls_back_to_node_key_without_unique_id():
    g = nx.DiGraph()
    g.add_node("node-key", label="observation")
    (node_id, _, _), = _emit(pipeline.node_generator(g))
    assert node_id == "node-key"


@pytest.mark.parametrize("label", ["dummy", "Dummy"])
def test_dummy_nodes_are_skipped(label):
    g = nx.DiGraph()
    g.add_node("d", label=label)
    assert _emit(pipeline.node_generator(g)) == []


@pytest.mark.parametrize("label", ["search", "meta", "link"])
def test_metadata_nodes_are_skipped(label):
    g = nx.DiGraph()
    g.add_node("m", label=label)
    assert _emit(pipeline.node_generator(g)) == []


def test_generator_mutates_graph_label_in_place():
    # Documented side effect: the generator rewrites each node's 'label' to
    # its normalised form. Worth pinning because anything that iterates the
    # graph afterwards sees the mutated value.
    g = nx.DiGraph()
    g.add_node("o1", label="observation")
    _emit(pipeline.node_generator(g))
    assert g.nodes["o1"]["label"] == "Observation"


def test_node_without_label_raises():
    # KNOWN SHARP EDGE: a node missing both 'label' and 'resourceType' makes
    # label None, and None.capitalize() raises. Pinning it as expected
    # behaviour; flip this test if you decide such nodes should be skipped.
    g = nx.DiGraph()
    g.add_node("orphan")
    with pytest.raises(AttributeError):
        _emit(pipeline.node_generator(g))


# --- edge_generator --------------------------------------------------------

def test_edge_label_combines_endpoint_labels():
    g = nx.DiGraph()
    g.add_node("a", label="patient")
    g.add_node("b", label="observation")
    g.add_edge("a", "b", id="e1")
    (edge_id, source, target, label, _), = _emit(pipeline.edge_generator(g))
    assert (edge_id, source, target, label) == (
        "e1", "a", "b", "Patient_to_Observation",
    )


def test_edge_resource_endpoints_use_resource_type():
    g = nx.DiGraph()
    g.add_node("a", label="resource", resourceType="Patient")
    g.add_node("b", label="resource", resourceType="Encounter")
    g.add_edge("a", "b", id="e1")
    (_, _, _, label, _), = _emit(pipeline.edge_generator(g))
    assert label == "Patient_to_Encounter"


def test_edge_without_id_gets_generated_uuid():
    g = nx.DiGraph()
    g.add_node("a", label="patient")
    g.add_node("b", label="observation")
    g.add_edge("a", "b")
    (edge_id, *_), = _emit(pipeline.edge_generator(g))
    assert isinstance(edge_id, str) and len(edge_id) == 36  # uuid4 string


def test_edge_uses_unique_id_for_endpoints_when_present():
    g = nx.DiGraph()
    g.add_node("a", label="patient", unique_id="P-1")
    g.add_node("b", label="observation", unique_id="O-1")
    g.add_edge("a", "b", id="e1")
    (_, source, target, _, _), = _emit(pipeline.edge_generator(g))
    assert (source, target) == ("P-1", "O-1")