import pytest from pydantic import TypeAdapter, ValidationError from invokeai.app.services.session_queue.session_queue_common import ( Batch, BatchDataCollection, BatchDatum, NodeFieldValue, calc_session_count, create_session_nfv_tuples, prepare_values_to_insert, ) from invokeai.app.services.shared.graph import Graph, GraphExecutionState from .test_nodes import PromptTestInvocation @pytest.fixture def batch_data_collection() -> BatchDataCollection: return [ [ # zipped BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi", "Grape sushi"]), BatchDatum(node_path="2", field_name="prompt", items=["Strawberry sushi", "Blueberry sushi"]), ], [ BatchDatum(node_path="3", field_name="prompt", items=["Orange sushi", "Apple sushi"]), ], ] @pytest.fixture def batch_graph() -> Graph: g = Graph() g.add_node(PromptTestInvocation(id="1", prompt="Chevy")) g.add_node(PromptTestInvocation(id="2", prompt="Toyota")) g.add_node(PromptTestInvocation(id="3", prompt="Subaru")) g.add_node(PromptTestInvocation(id="4", prompt="Nissan")) return g def test_create_sessions_from_batch_with_runs(batch_data_collection, batch_graph): b = Batch(graph=batch_graph, data=batch_data_collection, runs=2) t = list(create_session_nfv_tuples(batch=b, maximum=1000)) # 2 list[BatchDatum] * length 2 * 2 runs = 8 assert len(t) == 8 assert t[0][0].graph.get_node("1").prompt == "Banana sushi" assert t[0][0].graph.get_node("2").prompt == "Strawberry sushi" assert t[0][0].graph.get_node("3").prompt == "Orange sushi" assert t[0][0].graph.get_node("4").prompt == "Nissan" assert t[1][0].graph.get_node("1").prompt == "Banana sushi" assert t[1][0].graph.get_node("2").prompt == "Strawberry sushi" assert t[1][0].graph.get_node("3").prompt == "Apple sushi" assert t[1][0].graph.get_node("4").prompt == "Nissan" assert t[2][0].graph.get_node("1").prompt == "Grape sushi" assert t[2][0].graph.get_node("2").prompt == "Blueberry sushi" assert t[2][0].graph.get_node("3").prompt == "Orange sushi" assert t[2][0].graph.get_node("4").prompt == "Nissan" assert t[3][0].graph.get_node("1").prompt == "Grape sushi" assert t[3][0].graph.get_node("2").prompt == "Blueberry sushi" assert t[3][0].graph.get_node("3").prompt == "Apple sushi" assert t[3][0].graph.get_node("4").prompt == "Nissan" # repeat for second run assert t[4][0].graph.get_node("1").prompt == "Banana sushi" assert t[4][0].graph.get_node("2").prompt == "Strawberry sushi" assert t[4][0].graph.get_node("3").prompt == "Orange sushi" assert t[4][0].graph.get_node("4").prompt == "Nissan" assert t[5][0].graph.get_node("1").prompt == "Banana sushi" assert t[5][0].graph.get_node("2").prompt == "Strawberry sushi" assert t[5][0].graph.get_node("3").prompt == "Apple sushi" assert t[5][0].graph.get_node("4").prompt == "Nissan" assert t[6][0].graph.get_node("1").prompt == "Grape sushi" assert t[6][0].graph.get_node("2").prompt == "Blueberry sushi" assert t[6][0].graph.get_node("3").prompt == "Orange sushi" assert t[6][0].graph.get_node("4").prompt == "Nissan" assert t[7][0].graph.get_node("1").prompt == "Grape sushi" assert t[7][0].graph.get_node("2").prompt == "Blueberry sushi" assert t[7][0].graph.get_node("3").prompt == "Apple sushi" assert t[7][0].graph.get_node("4").prompt == "Nissan" def test_create_sessions_from_batch_without_runs(batch_data_collection, batch_graph): b = Batch(graph=batch_graph, data=batch_data_collection) t = list(create_session_nfv_tuples(batch=b, maximum=1000)) # 2 list[BatchDatum] * length 2 * 1 runs = 8 assert len(t) == 4 def test_create_sessions_from_batch_without_batch(batch_graph): b = Batch(graph=batch_graph, runs=2) t = list(create_session_nfv_tuples(batch=b, maximum=1000)) # 2 runs assert len(t) == 2 def test_create_sessions_from_batch_without_batch_or_runs(batch_graph): b = Batch(graph=batch_graph) t = list(create_session_nfv_tuples(batch=b, maximum=1000)) # 1 run assert len(t) == 1 def test_create_sessions_from_batch_with_runs_and_max(batch_data_collection, batch_graph): b = Batch(graph=batch_graph, data=batch_data_collection, runs=2) t = list(create_session_nfv_tuples(batch=b, maximum=5)) # 2 list[BatchDatum] * length 2 * 2 runs = 8, but max is 5 assert len(t) == 5 def test_calc_session_count(batch_data_collection, batch_graph): b = Batch(graph=batch_graph, data=batch_data_collection, runs=2) # 2 list[BatchDatum] * length 2 * 2 runs = 8 assert calc_session_count(batch=b) == 8 def test_prepare_values_to_insert(batch_data_collection, batch_graph): b = Batch(graph=batch_graph, data=batch_data_collection, runs=2) values = prepare_values_to_insert(queue_id="default", batch=b, priority=0, max_new_queue_items=1000) assert len(values) == 8 GraphExecutionStateValidator = TypeAdapter(GraphExecutionState) # graph should be serialized ges = GraphExecutionStateValidator.validate_json(values[0].session) # graph values should be populated assert ges.graph.get_node("1").prompt == "Banana sushi" assert ges.graph.get_node("2").prompt == "Strawberry sushi" assert ges.graph.get_node("3").prompt == "Orange sushi" assert ges.graph.get_node("4").prompt == "Nissan" # session ids should match deserialized graph assert [v.session_id for v in values] == [GraphExecutionStateValidator.validate_json(v.session).id for v in values] # should unique session ids sids = [v.session_id for v in values] assert len(sids) == len(set(sids)) NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue]) # should have 3 node field values assert isinstance(values[0].field_values, str) assert len(NodeFieldValueValidator.validate_json(values[0].field_values)) == 3 # should have batch id and priority assert all(v.batch_id == b.batch_id for v in values) assert all(v.priority == 0 for v in values) def test_prepare_values_to_insert_with_priority(batch_data_collection, batch_graph): b = Batch(graph=batch_graph, data=batch_data_collection, runs=2) values = prepare_values_to_insert(queue_id="default", batch=b, priority=1, max_new_queue_items=1000) assert all(v.priority == 1 for v in values) def test_prepare_values_to_insert_with_max(batch_data_collection, batch_graph): b = Batch(graph=batch_graph, data=batch_data_collection, runs=2) values = prepare_values_to_insert(queue_id="default", batch=b, priority=1, max_new_queue_items=5) assert len(values) == 5 def test_cannot_create_bad_batch_items_length(batch_graph): with pytest.raises(ValidationError, match="Zipped batch items must all have the same length"): Batch( graph=batch_graph, data=[ [ BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]), # 1 item BatchDatum(node_path="2", field_name="prompt", items=["Toyota", "Nissan"]), # 2 items ], ], ) def test_cannot_create_bad_batch_items_type(batch_graph): with pytest.raises(ValidationError, match="All items in a batch must have the same type"): Batch( graph=batch_graph, data=[ [ BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi", 123]), ] ], ) def test_cannot_create_bad_batch_unique_ids(batch_graph): with pytest.raises(ValidationError, match="Each batch data must have unique node_id and field_name"): Batch( graph=batch_graph, data=[ [ BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]), ], [ BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]), ], ], ) def test_cannot_create_bad_batch_nodes_exist( batch_graph, ): with pytest.raises(ValidationError, match=r"Node .* not found in graph"): Batch( graph=batch_graph, data=[ [ BatchDatum(node_path="batman", field_name="prompt", items=["Banana sushi"]), ], ], ) def test_cannot_create_bad_batch_fields_exist( batch_graph, ): with pytest.raises(ValidationError, match=r"Field .* not found in node"): Batch( graph=batch_graph, data=[ [ BatchDatum(node_path="1", field_name="batman", items=["Banana sushi"]), ], ], )