"""OpenLineage event emitter for dbt test results.

This module constructs OpenLineage events with embedded test results using the
dataQualityAssertions dataset facet, and emits them to Correlator or any
OpenLineage-compatible backend.

The emitter handles:
    - Grouping test results by dataset
    - Constructing dataQualityAssertions facets
    - Building complete OpenLineage RunEvent structures
    - Emitting events via OpenLineage Python client
    - HTTP error handling and retries

OpenLineage Specification:
    - Core spec: https://openlineage.io/docs/spec/object-model
    - dataQualityAssertions facet: https://openlineage.io/docs/spec/facets/dataset-facets/data-quality-assertions
    - Run cycle: https://openlineage.io/docs/spec/run-cycle
"""

from typing import Any, Optional

from .parser import Manifest, RunResults


def construct_event(
    run_results: RunResults,
    manifest: Manifest,
    namespace: str,
    job_name: str,
) -> Any:  # Will be: RunEvent from openlineage.client.run
    """Construct OpenLineage event with dataQualityAssertions facet.

    Groups test results by dataset and creates an OpenLineage RunEvent with
    embedded test results in the dataQualityAssertions dataset facet.

    Process:
        1. Group test results by dataset using manifest
        2. For each dataset, create dataQualityAssertions facet with assertions
        3. Construct RunEvent with proper run/job/dataset structure
        4. Set eventType to COMPLETE, producer to dbt-correlator

    Args:
        run_results: Parsed dbt run_results.json.
        manifest: Parsed dbt manifest.json.
        namespace: OpenLineage namespace (e.g., "dbt", "production").
        job_name: Job name for OpenLineage job (e.g., "dbt_test_run").

    Returns:
        OpenLineage RunEvent with dataQualityAssertions facets.

    Example:
        >>> event = construct_event(run_results, manifest, "dbt", "dbt_test_run")
        >>> print(event.eventType)  # "COMPLETE"
        >>> print(event.inputs[0].facets["dataQualityAssertions"])

    Note:
        Implementation in Task 1.3: OpenLineage Event Emitter.
        Will use openlineage-python client types:
            - RunEvent, RunState, Run, Job, Dataset
            - DataQualityAssertionsDatasetFacet, Assertion
    """
    raise NotImplementedError(
        "construct_event() will be implemented in Task 1.3: OpenLineage Event Emitter"
    )


def emit_event(
    event: Any,  # Will be: RunEvent
    openlineage_url: str,
    api_key: Optional[str] = None,
) -> None:
    """Emit OpenLineage event to Correlator or compatible backend.

    Sends the OpenLineage event via HTTP POST to the specified endpoint.
    Uses the official openlineage-python client for emission with proper
    authentication and error handling.

    Args:
        event: OpenLineage RunEvent to emit.
        openlineage_url: OpenLineage API endpoint URL.
        api_key: Optional API key for authentication.

    Raises:
        ConnectionError: If unable to connect to OpenLineage endpoint.
        TimeoutError: If request times out.
        ValueError: If event is invalid or response indicates error.

    Example:
        >>> event = construct_event(...)
        >>> emit_event(event, "http://localhost:8080/api/v1/lineage")

    Note:
        Implementation in Task 1.3: OpenLineage Event Emitter.
        Will use OpenLineageClient from openlineage-python.
    """
    raise NotImplementedError(
        "emit_event() will be implemented in Task 1.3: OpenLineage Event Emitter"
    )


def group_tests_by_dataset(
    run_results: RunResults,
    manifest: Manifest,
) -> dict[str, list[dict[str, Any]]]:
    """Group test results by their target dataset.

    Analyzes test nodes to determine which dataset each test validates,
    then groups tests by dataset for facet construction.

    Handles:
        - Single test referencing one dataset
        - Tests with multiple refs (creates multiple dataset entries)
        - Source tests vs model tests
        - Tests without clear dataset reference (logs warning)

    Args:
        run_results: Parsed dbt run_results.json.
        manifest: Parsed dbt manifest.json.

    Returns:
        Dictionary mapping dataset URN to list of test results.
        Key: Dataset URN (namespace:name)
        Value: List of test result dictionaries

    Example:
        >>> grouped = group_tests_by_dataset(run_results, manifest)
        >>> for dataset_urn, tests in grouped.items():
        ...     print(f"Dataset: {dataset_urn}, Tests: {len(tests)}")

    Note:
        Implementation in Task 1.3: OpenLineage Event Emitter.
    """
    raise NotImplementedError(
        "group_tests_by_dataset() will be implemented in Task 1.3: OpenLineage Event Emitter"
    )
