"""Quality suite workflow — data quality expectations and validation.

Reuses:
    - ``databridge.profiler.generate_expectation_suite(...)``
    - ``databridge.profiler.validate(...)``
    - ``databridge.profiler.get_validation_results(...)``

IKE integration (optional):
    - P5 Learning Cycle: Run 7-step self-learning after quality validation
    - P4 Scoring: Score quality findings with calibrated confidence
"""

from __future__ import annotations

from typing import Any, Dict, List, Optional

from databridge_core.sdk.core.context import SDKContext
from databridge_core.sdk.core.exceptions import NotImplementedStubError
from databridge_core.sdk.core.types import CompiledArtifact, Result
from databridge_core.sdk.workflows.base import BaseWorkflow
from databridge_core.sdk.workflows.result import WorkflowResult
from databridge_core.sdk.workflows.step import workflow_step


class QualitySuiteWorkflow(BaseWorkflow):
    """Generate and run data quality expectation suites.

    When IKE is available, validation results feed into IKE's P5
    self-learning cycle (quality gates -> falsification -> clustering
    -> retuning -> regression -> audit -> bandit update) to
    continuously improve detection thresholds.

    Reuses:
        - ``databridge.profiler.generate_expectation_suite(source_path, suite_name)``
        - ``databridge.profiler.validate(source_path, suite_name)``
        - ``databridge.profiler.get_validation_results(suite_name)``
    """

    name = "quality_governance.quality_suite"
    category = "quality_governance"
    description = "Generate and validate data quality expectation suites"
    required_params = ["source_path"]
    optional_params = {
        "suite_name": "auto",
        "run_validation": True,
        "ike_learning_cycle": True,
    }

    def validate(self, params: Dict[str, Any]) -> Dict[str, Any]:
        self._check_required(params)
        return self._apply_defaults(params)

    def execute(
        self, params: Dict[str, Any], ctx: Optional[SDKContext] = None
    ) -> Result:
        steps = [self._generate_suite(params, ctx)]
        if params.get("run_validation"):
            steps.append(self._run_validation(params, ctx))
        # IKE P5: Run self-learning cycle after validation
        if params.get("ike_learning_cycle") and ctx and ctx.ike.available:
            steps.append(self._ike_learning_cycle(params, ctx))
        # Graph integration
        if ctx and ctx.graphs.available:
            steps.append(self._graph_record_lineage(params, ctx))
            steps.append(self._graph_record_evidence(params, ctx))
            steps.append(self._graph_index_vectors(params, ctx))
            steps.append(self._graph_ingest_kb(params, ctx))
        return WorkflowResult.from_steps(self.name, steps)

    def compile_to_snowflake(
        self, params: Dict[str, Any], ctx: Optional[SDKContext] = None
    ) -> List[CompiledArtifact]:
        raise NotImplementedStubError(
            self.name,
            "compile_to_snowflake",
            "Generate Snowflake Task with quality check SQL "
            "(NULL counts, range checks, uniqueness constraints)",
        )

    @workflow_step("generate_suite", "Generate expectation suite")
    def _generate_suite(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # REUSE: databridge.profiler.generate_expectation_suite(
        #     source_path=params["source_path"],
        #     suite_name=params.get("suite_name", "auto"),
        # )
        raise NotImplementedStubError(
            self.name,
            "generate_suite",
            "databridge.profiler.generate_expectation_suite(source_path, suite_name)",
        )

    @workflow_step("run_validation", "Run quality validation")
    def _run_validation(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # REUSE: databridge.profiler.validate(
        #     source_path=params["source_path"],
        #     suite_name=params.get("suite_name", "auto"),
        # )
        raise NotImplementedStubError(
            self.name,
            "run_validation",
            "databridge.profiler.validate(source_path, suite_name)",
        )

    @workflow_step("ike_learning_cycle", "Run IKE P5 self-learning cycle")
    def _ike_learning_cycle(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # IKE P5: Run the 7-step self-learning cycle using validation
        # results as feedback input:
        #   1. Quality gates — check minimum data density
        #   2. Falsification — search for counterexamples
        #   3. Clustering — group failure patterns
        #   4. Retuning — adjust detection thresholds
        #   5. Regression — verify no quality regression
        #   6. Audit — log all changes for SOX compliance
        #   7. Bandit update — update Thompson Sampling priors
        #
        # ctx.ike.run_learning_cycle()
        raise NotImplementedStubError(
            self.name,
            "ike_learning_cycle",
            "ctx.ike.run_learning_cycle() — IKE P5 7-step self-learning pipeline",
        )

    # --- Graph integration ---

    @workflow_step("graph_record_lineage", "Record Validation→TABLE edges in lineage")
    def _graph_record_lineage(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.record_lineage_node("sdk", f"quality_{suite_name}", "EXTERNAL")
        # ctx.graphs.record_lineage_edge("sdk", quality_node, source_table, "DIRECT")
        raise NotImplementedStubError(
            self.name,
            "graph_record_lineage",
            "ctx.graphs.record_lineage_node/edge — Validation→TABLE edges",
        )

    @workflow_step("graph_record_evidence", "Record validation evidence in evidence trail")
    def _graph_record_evidence(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.record_recon_evidence(
        #     recon_id=f"quality_{suite_name}", match_rate=pass_rate,
        #     orphans=0, conflicts=fail_count,
        #     source_file=params["source_path"], target_file=suite_name,
        # )
        raise NotImplementedStubError(
            self.name,
            "graph_record_evidence",
            "ctx.graphs.record_recon_evidence — validation results as evidence",
        )

    @workflow_step("graph_index_vectors", "Index validation results in vector store")
    def _graph_index_vectors(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.index_content(
        #     doc_id=f"quality_{suite}", content=validation_summary,
        #     source_type="quality_validation", metadata={...},
        # )
        raise NotImplementedStubError(
            self.name,
            "graph_index_vectors",
            "ctx.graphs.index_content(doc_id, content, 'quality_validation', metadata)",
        )

    @workflow_step("graph_ingest_kb", "Ingest quality stats into KB graph")
    def _graph_ingest_kb(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.ingest_kb_nodes(
        #     nodes=[{"id": suite_name, "type": "Table", "properties": {"pass_rate": ...}}],
        # )
        raise NotImplementedStubError(
            self.name,
            "graph_ingest_kb",
            "ctx.graphs.ingest_kb_nodes(nodes) — quality stats as KB nodes",
        )
