from __future__ import annotations

import copy
import threading
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, Dict, Iterable, Optional, Sequence, Tuple

# https://github.com/dbt-labs/dbt-core/issues/10098
# Needed for Mashumaro serialization of RunResult below
# TODO: investigate alternative approaches to restore conditional import
# if TYPE_CHECKING:
import agate

from dbt.artifacts.resources import CompiledResource
from dbt.artifacts.schemas.base import (
    ArtifactMixin,
    BaseArtifactMetadata,
    get_artifact_schema_version,
    schema_version,
)
from dbt.artifacts.schemas.batch_results import BatchResults
from dbt.artifacts.schemas.results import (
    BaseResult,
    ExecutionResult,
    NodeResult,
    ResultNode,
    RunStatus,
)
from dbt.exceptions import scrub_secrets
from dbt_common.clients.system import write_json
from dbt_common.constants import SECRET_ENV_PREFIX


@dataclass
class RunResult(NodeResult):
    agate_table: Optional["agate.Table"] = field(
        default=None, metadata={"serialize": lambda x: None, "deserialize": lambda x: None}
    )
    batch_results: Optional[BatchResults] = None

    @property
    def skipped(self):
        return self.status == RunStatus.Skipped

    @classmethod
    def from_node(cls, node: ResultNode, status: RunStatus, message: Optional[str]):
        thread_id = threading.current_thread().name
        return RunResult(
            status=status,
            thread_id=thread_id,
            execution_time=0,
            timing=[],
            message=message,
            node=node,
            adapter_response={},
            failures=None,
            batch_results=None,
        )


@dataclass
class RunResultsMetadata(BaseArtifactMetadata):
    dbt_schema_version: str = field(
        default_factory=lambda: str(RunResultsArtifact.dbt_schema_version)
    )


@dataclass
class RunResultOutput(BaseResult):
    unique_id: str
    compiled: Optional[bool]
    compiled_code: Optional[str]
    relation_name: Optional[str]
    batch_results: Optional[BatchResults] = None


def process_run_result(result: RunResult) -> RunResultOutput:

    compiled = isinstance(result.node, CompiledResource)

    return RunResultOutput(
        unique_id=result.node.unique_id,
        status=result.status,
        timing=result.timing,
        thread_id=result.thread_id,
        execution_time=result.execution_time,
        message=result.message,
        adapter_response=result.adapter_response,
        failures=result.failures,
        batch_results=result.batch_results,
        compiled=result.node.compiled if compiled else None,  # type:ignore
        compiled_code=result.node.compiled_code if compiled else None,  # type:ignore
        relation_name=result.node.relation_name if compiled else None,  # type:ignore
    )


@dataclass
class RunExecutionResult(
    ExecutionResult,
):
    results: Sequence[RunResult]
    args: Dict[str, Any] = field(default_factory=dict)
    generated_at: datetime = field(default_factory=datetime.utcnow)

    def write(self, path: str):
        writable = RunResultsArtifact.from_execution_results(
            results=self.results,
            elapsed_time=self.elapsed_time,
            generated_at=self.generated_at,
            args=self.args,
        )
        writable.write(path)


@dataclass
@schema_version("run-results", 6)
class RunResultsArtifact(ExecutionResult, ArtifactMixin):
    results: Sequence[RunResultOutput]
    args: Dict[str, Any] = field(default_factory=dict)

    @classmethod
    def from_execution_results(
        cls,
        results: Sequence[RunResult],
        elapsed_time: float,
        generated_at: datetime,
        args: Dict,
    ):
        processed_results = [
            process_run_result(result) for result in results if isinstance(result, RunResult)
        ]
        meta = RunResultsMetadata(
            dbt_schema_version=str(cls.dbt_schema_version),
            generated_at=generated_at,
        )

        secret_vars = [
            v for k, v in args["vars"].items() if k.startswith(SECRET_ENV_PREFIX) and v.strip()
        ]

        scrubbed_args = copy.deepcopy(args)

        # scrub secrets in invocation command
        scrubbed_args["invocation_command"] = scrub_secrets(
            scrubbed_args["invocation_command"], secret_vars
        )

        # scrub secrets in vars dict
        scrubbed_args["vars"] = {
            k: scrub_secrets(v, secret_vars) for k, v in scrubbed_args["vars"].items()
        }

        return cls(
            metadata=meta, results=processed_results, elapsed_time=elapsed_time, args=scrubbed_args
        )

    @classmethod
    def compatible_previous_versions(cls) -> Iterable[Tuple[str, int]]:
        return [
            ("run-results", 4),
            ("run-results", 5),
        ]

    @classmethod
    def upgrade_schema_version(cls, data):
        """This overrides the "upgrade_schema_version" call in VersionedSchema (via
        ArtifactMixin) to modify the dictionary passed in from earlier versions of the run_results.
        """
        run_results_schema_version = get_artifact_schema_version(data)
        # If less than the current version (v5), preprocess contents to match latest schema version
        if run_results_schema_version <= 5:
            # In v5, we added 'compiled' attributes to each result entry
            # Going forward, dbt expects these to be populated
            for result in data["results"]:
                result["compiled"] = False
                result["compiled_code"] = ""
                result["relation_name"] = ""
        return cls.from_dict(data)

    def write(self, path: str):
        write_json(path, self.to_dict(omit_none=False))
