from typing import Union, Optional
import os

from deepeval.test_run.api import (
    LLMApiTestCase,
    ConversationalApiTestCase,
    TurnApi,
    TraceApi,
)
from deepeval.test_case import (
    LLMTestCase,
    ConversationalTestCase,
    Turn,
)
from deepeval.constants import PYTEST_RUN_TEST_NAME


def create_api_turn(turn: Turn, index: int) -> TurnApi:
    retrieval_context = None
    if turn.retrieval_context:
        retrieval_context = [
            rc.context if hasattr(rc, "context") else rc
            for rc in turn.retrieval_context
        ]
    return TurnApi(
        role=turn.role,
        content=turn.content,
        user_id=turn.user_id,
        retrievalContext=retrieval_context,
        toolsCalled=turn.tools_called,
        order=index,
    )


def create_api_test_case(
    test_case: Union[LLMTestCase, ConversationalTestCase],
    trace: Optional[TraceApi] = None,
    index: Optional[int] = None,
) -> Union[LLMApiTestCase, ConversationalApiTestCase]:

    if isinstance(test_case, ConversationalTestCase):
        order = (
            test_case._dataset_rank
            if test_case._dataset_rank is not None
            else index
        )
        if test_case.name:
            name = test_case.name
        else:
            name = os.getenv(
                PYTEST_RUN_TEST_NAME, f"conversational_test_case_{order}"
            )

        api_test_case = ConversationalApiTestCase(
            name=name,
            success=True,
            metricsData=[],
            runDuration=0,
            evaluationCost=None,
            order=order,
            scenario=test_case.scenario,
            expectedOutcome=test_case.expected_outcome,
            userDescription=test_case.user_description,
            context=test_case.context,
            tags=test_case.tags,
            comments=test_case.comments,
            imagesMapping=test_case._get_images_mapping(),
            metadata=test_case.metadata,
        )

        api_test_case.turns = [
            create_api_turn(
                turn=turn,
                index=index,
            )
            for index, turn in enumerate(test_case.turns)
        ]

        return api_test_case
    else:
        order = (
            test_case._dataset_rank
            if test_case._dataset_rank is not None
            else index
        )

        success = True
        if test_case.name is not None:
            name = test_case.name
        else:
            name = os.getenv(PYTEST_RUN_TEST_NAME, f"test_case_{order}")
        metrics_data = []

        retrieval_context = None
        if test_case.retrieval_context:
            retrieval_context = [
                rc.context if hasattr(rc, "context") else rc
                for rc in test_case.retrieval_context
            ]

        api_test_case = LLMApiTestCase(
            name=name,
            input=test_case.input,
            actualOutput=test_case.actual_output,
            expectedOutput=test_case.expected_output,
            retrievalContext=retrieval_context,
            context=test_case.context,
            imagesMapping=test_case._get_images_mapping(),
            toolsCalled=test_case.tools_called,
            expectedTools=test_case.expected_tools,
            tokenCost=test_case.token_cost,
            completionTime=test_case.completion_time,
            success=success,
            metricsData=metrics_data,
            runDuration=None,
            evaluationCost=None,
            order=order,
            metadata=test_case.metadata,
            comments=test_case.comments,
            tags=test_case.tags,
            trace=trace,
        )
        # llm_test_case_lookup_map[instance_id] = api_test_case
        return api_test_case