from __future__ import annotations

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

from behave import given, then, when

from cleveragents.domain.models.core import (
    Context,
    OperationType,
    Plan,
    Project,
)
from cleveragents.providers.llm.openai_provider import OpenAIChatProvider


def _register_cleanup(context, cleanup):
    if hasattr(context, "add_cleanup"):
        context.add_cleanup(cleanup)
    else:
        cleanup_handlers = getattr(context, "_cleanup_handlers", [])
        cleanup_handlers.append(cleanup)
        context._cleanup_handlers = cleanup_handlers


def _parse_kwargs_string(kwargs_string: str) -> dict[str, Any]:
    if not kwargs_string:
        return {}
    result: dict[str, Any] = {}
    for entry in kwargs_string.split(","):
        entry = entry.strip()
        if not entry:
            continue
        if "=" not in entry:
            result[entry] = True
            continue
        key, value = entry.split("=", 1)
        key = key.strip()
        value = value.strip()
        try:
            parsed_value = ast.literal_eval(value)
        except Exception:
            parsed_value = value
        result[key] = parsed_value
    return result


def _setup_plan_generation_graph(context) -> MagicMock:
    patcher = patch(
        "cleveragents.providers.llm.langchain_chat_provider.PlanGenerationGraph"
    )
    context.plan_generation_patcher = patcher
    mock_graph_class = patcher.start()
    _register_cleanup(context, patcher.stop)
    mock_graph_instance = MagicMock(name="PlanGenerationGraphInstance")

    default_state = {
        "generated_changes": [],
        "validation_result": {"status": "PASS"},
        "error": None,
    }
    state_override = getattr(context, "plan_generation_state_override", None)
    mock_graph_instance.invoke.return_value = state_override or default_state

    invoke_side_effect = getattr(context, "plan_generation_invoke_side_effect", None)
    if invoke_side_effect is not None:
        mock_graph_instance.invoke.side_effect = invoke_side_effect

    stream_events = getattr(context, "plan_generation_stream_events", None)
    if stream_events is None:
        mock_graph_instance.stream.return_value = iter(())
    else:
        events_copy = list(stream_events)

        def _stream(*_args, **_kwargs):
            yield from events_copy

        mock_graph_instance.stream.side_effect = _stream

    mock_graph_class.return_value = mock_graph_instance
    context.plan_generation_graph = mock_graph_instance
    context.plan_generation_graph_class = mock_graph_class
    return mock_graph_instance


@given("I have sample provider domain inputs")
def step_sample_provider_inputs(context):
    context.project = MagicMock(spec=Project)
    context.plan = MagicMock(spec=Plan)
    context.plan.prompt = "Add placeholder coverage"
    context.contexts = [MagicMock(spec=Context)]
    context.contexts[0].content = "Sample context entry"


@given('the plan generation graph returns a generated change for "{file_path}"')
def step_plan_generation_returns_change(context, file_path):
    change = {
        "plan_id": 1,
        "file_path": file_path,
        "operation": OperationType.MODIFY.value,
        "new_content": "# updated content",
    }
    context.plan_generation_state_override = {
        "generated_changes": [change],
        "validation_result": {"status": "PASS"},
        "error": None,
    }


@given("the OpenAI provider token estimator returns {token_count:d} tokens")
def step_openai_token_estimator(context, token_count):
    context.openai_token_count = token_count


@given('the plan generation graph emits streaming nodes "{node_list}"')
def step_plan_generation_stream_nodes(context, node_list):
    nodes = [node.strip() for node in node_list.split(",") if node.strip()]
    state = getattr(context, "plan_generation_state_override", {}) or {}
    events: list[dict[str, Any]] = []
    for node in nodes:
        payload: dict[str, Any] = {"status": "completed"}
        if node == "generate_plan" and isinstance(state.get("generated_changes"), list):
            payload = {
                "generated_changes": state["generated_changes"],
                "status": "completed",
            }
        elif node == "validate" and isinstance(state.get("validation_result"), dict):
            payload = {
                "validation_result": state["validation_result"],
                "status": "completed",
            }
        events.append({node: payload})
    context.plan_generation_stream_events = events


@given('the plan generation graph raises ValueError "{message}"')
def step_plan_generation_raises_value_error(context, message):
    context.plan_generation_invoke_side_effect = ValueError(message)


@given('the plan generation graph raises RuntimeError "{message}"')
def step_plan_generation_runtime_error(context, message):
    context.plan_generation_invoke_side_effect = RuntimeError(message)


@given('I set the OpenAI provider organization "{organization}"')
def step_set_openai_provider_org(context, organization):
    if organization.lower() == "none":
        context.openai_provider_org = None
    else:
        context.openai_provider_org = organization


@given('I set the OpenAI provider extra kwargs "{kwargs_string}"')
def step_set_openai_provider_kwargs(context, kwargs_string):
    context.openai_provider_kwargs = _parse_kwargs_string(kwargs_string)


@given(
    'I create an OpenAI chat provider with API key "{api_key}" and model "{model_id}"'
)
@when(
    'I create an OpenAI chat provider with API key "{api_key}" and model "{model_id}"'
)
def step_create_openai_provider(context, api_key, model_id):
    patcher = patch("cleveragents.providers.llm.openai_provider.ChatOpenAI")
    context.chat_openai_patcher = patcher
    mock_chat_openai_class = patcher.start()
    _register_cleanup(context, patcher.stop)
    mock_chat_openai_instance = MagicMock(name="ChatOpenAIInstance")
    mock_chat_openai_instance.get_num_tokens = MagicMock(return_value=0)
    token_override = getattr(context, "openai_token_count", None)
    if token_override is not None:
        mock_chat_openai_instance.get_num_tokens.return_value = token_override
    mock_chat_openai_class.return_value = mock_chat_openai_instance

    organization = getattr(context, "openai_provider_org", None)
    extra_kwargs = dict(getattr(context, "openai_provider_kwargs", {}))

    context.provider = OpenAIChatProvider(
        api_key=api_key,
        model=model_id,
        organization=organization,
        **extra_kwargs,
    )
    context.chat_openai_class = mock_chat_openai_class
    context.chat_openai_instance = mock_chat_openai_instance


@when("I attempt to create an OpenAI chat provider without an API key")
def step_openai_provider_without_api_key(context):
    context.openai_provider_error = None
    try:
        OpenAIChatProvider(api_key="", model="gpt-4o-mini")
    except Exception as exc:  # pragma: no cover - defensive logging only
        context.openai_provider_error = exc


@when("I request plan generation from the OpenAI provider")
def step_request_plan_generation(context):
    _setup_plan_generation_graph(context)

    context.response = context.provider.generate_changes(
        context.project,
        context.plan,
        context.contexts,
    )
    context.chat_openai_call = context.chat_openai_class.call_args
    context.plan_generation_graph_call = context.plan_generation_graph_class.call_args


@when("I stream plan generation from the OpenAI provider")
def step_stream_plan_generation(context):
    _setup_plan_generation_graph(context)
    context.streamed_events = list(
        context.provider.stream_changes(
            context.project,
            context.plan,
            context.contexts,
        )
    )
    context.chat_openai_call = context.chat_openai_class.call_args
    context.plan_generation_graph_call = context.plan_generation_graph_class.call_args


@then(
    'the OpenAI provider should construct ChatOpenAI with api key "{api_key}" and model "{model_id}"'
)
def step_assert_chat_openai_constructor(context, api_key, model_id):
    assert context.chat_openai_call is not None, "ChatOpenAI should have been called"
    call_args, call_kwargs = context.chat_openai_call
    assert call_args == (), "ChatOpenAI should be called with keyword arguments"
    assert call_kwargs["api_key"] == api_key
    assert call_kwargs["model"] == model_id

    assert context.plan_generation_graph_call is not None, (
        "PlanGenerationGraph should receive the ChatOpenAI instance"
    )
    _, graph_kwargs = context.plan_generation_graph_call
    assert graph_kwargs["llm"] is context.chat_openai_instance


@then(
    'the OpenAI provider should include organization "{expected_organization}" in the ChatOpenAI call'
)
def step_assert_chat_openai_org(context, expected_organization):
    assert context.chat_openai_call is not None, "ChatOpenAI should have been called"
    _, call_kwargs = context.chat_openai_call
    normalized = (
        None if expected_organization.lower() == "none" else expected_organization
    )
    assert call_kwargs.get("organization") == normalized


@then(
    'the OpenAI provider should include kwargs "{kwargs_string}" in the ChatOpenAI call'
)
def step_assert_chat_openai_kwargs(context, kwargs_string):
    assert context.chat_openai_call is not None, "ChatOpenAI should have been called"
    _, call_kwargs = context.chat_openai_call
    expected_kwargs = _parse_kwargs_string(kwargs_string)
    for key, value in expected_kwargs.items():
        assert key in call_kwargs, f"Expected {key} in ChatOpenAI kwargs"
        assert call_kwargs[key] == value, (
            f"Expected ChatOpenAI kwargs[{key!r}] to equal {value!r}"
        )


@then(
    'the OpenAI provider metadata should report name "{expected_name}" and model "{expected_model}"'
)
def step_assert_provider_metadata(context, expected_name, expected_model):
    provider = getattr(context, "provider", None)
    assert provider is not None, "Provider should exist"
    assert provider.name == expected_name
    assert provider.model_id == expected_model
    response = getattr(context, "response", None)
    if response is not None:
        assert response.model_used == expected_model


@then("the OpenAI provider response should report the requested model without errors")
def step_assert_placeholder_metadata(context):
    assert context.response is not None
    assert context.response.model_used == context.provider.model_id
    assert context.response.error_message in (None, "")


@then("the OpenAI provider response should contain no generated changes")
def step_assert_no_changes(context):
    assert context.response is not None, "Provider response should exist"
    assert len(context.response.changes) == 0, "Expected no generated changes"


@then(
    'the OpenAI provider response should include {count:d} generated change for "{file_path}"'
)
def step_assert_generated_change(context, count, file_path):
    assert context.response is not None, "Provider response should exist"
    assert len(context.response.changes) == count, (
        f"Expected {count} changes, got {len(context.response.changes)}"
    )
    assert any(
        getattr(change, "file_path", None) == file_path
        for change in context.response.changes
    ), f"Expected change for {file_path}"


@then("the OpenAI provider response token count should equal {expected:d}")
def step_assert_token_count(context, expected):
    assert context.response is not None
    assert context.response.token_count == expected


@then('the OpenAI provider response should report error "{message}"')
def step_assert_response_error(context, message):
    assert context.response is not None
    assert context.response.error_message == message


@then('the OpenAI provider streaming events should include nodes "{node_list}"')
def step_assert_stream_events(context, node_list):
    expected = [node.strip() for node in node_list.split(",") if node.strip()]
    actual = [
        next(iter(event.keys()))
        for event in getattr(context, "streamed_events", [])
        if "__end__" not in event
    ]
    assert actual == expected


@then(
    "the OpenAI provider streaming result should finish with a response containing {count:d} generated change"
)
def step_assert_stream_final_response(context, count):
    events = getattr(context, "streamed_events", [])
    assert events, "Expected streamed events"
    final_event = events[-1]
    assert "__end__" in final_event, "Expected final __end__ event"
    response = final_event["__end__"].get("response")
    assert response is not None, "Expected ProviderResponse in __end__ event"
    assert len(response.changes) == count


@then('the OpenAI provider creation should fail with error "{message}"')
def step_assert_openai_provider_error(context, message):
    error = getattr(context, "openai_provider_error", None)
    assert error is not None, "Expected the provider to raise an error"
    assert isinstance(error, ValueError)
    assert str(error) == message