"""Step definitions for Runtime Executor API BDD tests.

Given steps (environment, config, credentials, limits, pricing) are defined
in ``runtime_config_steps.py`` (extracted to stay under the 500-line file
limit, CONTRIBUTING.md General Principles).
"""

from __future__ import annotations

from typing import Any
from unittest.mock import AsyncMock, MagicMock, patch

from behave import given, then, when
from behave.api.async_step import async_run_until_complete

from cleveractors.core.exceptions import ConfigurationError, ExecutionError
from cleveractors.result import ActorResult, NodeUsage
from cleveractors.runtime import (
    Executor,
    create_executor,
)


@given("the LLM agent is configured to fail")
def step_llm_fail(context: Any) -> None:
    context._llm_should_fail = True


@when("I call create_executor")
def step_call_create_executor(context: Any) -> None:
    context.test_executor = create_executor(
        config_dict=context.config_dict,
        credentials=context.credentials,
        limits=context.limits if hasattr(context, "limits") else None,
        pricing=context.pricing if hasattr(context, "pricing") else None,
    )


@when("I call create_executor with None limits and pricing")
def step_call_create_executor_none(context: Any) -> None:
    context.test_executor = create_executor(
        config_dict=context.config_dict,
        credentials=context.credentials,
        limits=None,
        pricing=None,
    )


@when("I execute the actor with message {msg}")
@async_run_until_complete
async def step_execute_actor(context: Any, msg: str) -> None:
    msg = msg.strip('"')
    executor = create_executor(
        config_dict=context.config_dict,
        credentials=context.credentials,
        limits=context.limits if hasattr(context, "limits") and context.limits else {},
        pricing=context.pricing
        if hasattr(context, "pricing") and context.pricing
        else {},
    )
    context.test_executor = executor

    with (
        patch("cleveractors.runtime_dispatch.TemplateRenderer") as mock_renderer,
        patch("cleveractors.agents.llm.LLMAgent") as mock_llm,
        patch("cleveractors.runtime_dispatch.AgentFactory") as mock_factory,
        patch("cleveractors.runtime_dispatch.PureLangGraph") as mock_pure_graph,
    ):
        mock_renderer_instance = MagicMock()
        mock_renderer.return_value = mock_renderer_instance

        if getattr(context, "_llm_should_fail", False):
            mock_llm_instance = MagicMock()
            mock_llm_instance.process_message = AsyncMock(
                side_effect=Exception("LLM API error")
            )
            mock_llm_instance.cleanup = AsyncMock()
            mock_llm_instance._last_token_usage = (0, 0)
        else:
            mock_llm_instance = MagicMock()
            mock_llm_instance.process_message = AsyncMock(
                return_value="Mock LLM response"
            )
            mock_llm_instance.cleanup = AsyncMock()
            # Simulate real token usage from LLMAgent._last_token_usage (AC2)
            mock_llm_instance._last_token_usage = (100, 50)

        mock_llm.return_value = mock_llm_instance

        mock_graph_instance = MagicMock()
        # PureLangGraph.execute() returns a 3-tuple (AC4): (response, state, node_usages)
        mock_graph_instance.execute = AsyncMock(
            return_value=(
                "Mock graph response",
                {},
                [("entry", "openai", "gpt-3.5-turbo", 100, 50)],
            )
        )
        mock_pure_graph.return_value = mock_graph_instance

        mock_factory_instance = MagicMock()
        mock_factory_instance.create_agent = MagicMock(return_value=mock_llm_instance)
        mock_factory.return_value = mock_factory_instance

        try:
            context.test_result = await executor.execute(msg)
            context.test_error = None
        except Exception as e:
            context.test_error = e
            context.test_result = None


@then("an Executor instance should be returned")
def step_assert_executor(context: Any) -> None:
    assert context.test_executor is not None
    assert isinstance(context.test_executor, Executor)


@then("the executor config should match the config dict")
def step_assert_config(context: Any) -> None:
    assert context.test_executor.config == context.config_dict


@then("the executor credentials should match the credentials dict")
def step_assert_credentials(context: Any) -> None:
    assert context.test_executor.credentials == context.credentials


@then("the executor limits should match the limits dict")
def step_assert_limits(context: Any) -> None:
    assert context.test_executor.limits == context.limits


@then("the executor pricing should match the pricing dict")
def step_assert_pricing(context: Any) -> None:
    assert context.test_executor.pricing == context.pricing


@then("the executor limits should be an empty dictionary")
def step_assert_limits_empty(context: Any) -> None:
    assert context.test_executor.limits == {}


@then("the executor pricing should be an empty dictionary")
def step_assert_pricing_empty(context: Any) -> None:
    assert context.test_executor.pricing == {}


@then("the execution should return an ActorResult")
def step_assert_actor_result(context: Any) -> None:
    assert context.test_result is not None
    assert isinstance(context.test_result, ActorResult)


@then("the ActorResult response should be non-empty")
def step_assert_response(context: Any) -> None:
    assert context.test_result.response is not None
    assert len(context.test_result.response) > 0


@then("the ActorResult should have token usage tracked")
def step_assert_tokens(context: Any) -> None:
    assert context.test_result.prompt_tokens == 100, (
        f"Expected prompt_tokens == 100, got {context.test_result.prompt_tokens}"
    )
    assert context.test_result.completion_tokens == 50, (
        f"Expected completion_tokens == 50, got {context.test_result.completion_tokens}"
    )


@then("the ActorResult should have at least one node usage entry")
def step_assert_nodes(context: Any) -> None:
    assert len(context.test_result.nodes) >= 1


@then("the ActorResult should have zero prompt tokens for tool agents")
def step_assert_zero_tokens(context: Any) -> None:
    assert context.test_result.prompt_tokens == 0
    assert context.test_result.completion_tokens == 0


@then("the node usage IDs should be prefixed with the default actor name")
def step_assert_node_prefix(context: Any) -> None:
    for node in context.test_result.nodes:
        assert node.node_id.startswith("default.")


@then("a ConfigurationError should be raised for runtime")
def step_assert_config_error(context: Any) -> None:
    assert context.test_error is not None
    assert isinstance(context.test_error, ConfigurationError)


@then("the error message should mention the unknown actor type")
def step_assert_error_unknown_type(context: Any) -> None:
    assert "unknown_type" in str(context.test_error).lower()


@then(
    "an ExecutionError should be raised for runtime with the original cause suppressed"
)
def step_assert_execution_error_cause_suppressed(context: Any) -> None:
    assert context.test_error is not None
    assert isinstance(context.test_error, ExecutionError)
    assert context.test_error.__cause__ is None, (
        f"Expected ExecutionError with suppressed cause, "
        f"but got __cause__={context.test_error.__cause__!r}"
    )


@then("a ConfigurationError should be raised for runtime about no actors")
def step_assert_no_actors_error(context: Any) -> None:
    assert context.test_error is not None
    assert isinstance(context.test_error, ConfigurationError)
    assert "no actors" in str(context.test_error).lower(), (
        f"Expected error to mention 'no actors', got: {context.test_error}"
    )


@when("I import from cleaveractors.runtime_types")
def step_import_runtime_types(context):
    from cleveractors.runtime_types import ActorResult, NodeUsage

    context.rt_actor_result = ActorResult
    context.rt_node_usage = NodeUsage


@then("ActorResult and NodeUsage should be importable")
def step_assert_rt_imports(context):
    assert context.rt_actor_result is not None
    assert context.rt_node_usage is not None