"""Bug Prediction Workflow — Agent SDK Native.

Delegates bug prediction to three specialized Claude Agent SDK
subagents (pattern-scanner, risk-correlator, prevention-advisor)
and synthesizes their findings into a unified WorkflowResult.

Prior to v4.2.0 this was a mixin-based multi-stage pipeline. The
SDK-native implementation replaces that with `claude_agent_sdk.query()`
and `AgentDefinition` subagents while preserving the same public
interface (`BugPredictionWorkflow`, `bug-predict` slug) and
re-exports used by downstream code.

Copyright 2025-2026 Smart-AI-Memory
Licensed under the Apache License, Version 2.0
"""

import logging
from datetime import datetime
from pathlib import Path
from typing import Any

import claude_agent_sdk

from .agent_sdk_adapter import (
    AgentRunResult,
    AgentSDKResultAdapter,
    build_result_text,
    collect_agent_output,
    get_max_budget_usd,
    get_subagent_model,
)
from .base import BaseWorkflow, ModelTier
from .bug_predict_patterns import (
    _has_problematic_exception_handlers,  # noqa: F401 — re-exported
    _is_acceptable_broad_exception,  # noqa: F401 — re-exported
    _is_dangerous_eval_usage,  # noqa: F401 — re-exported
    _is_security_policy_line,  # noqa: F401 — re-exported
    _load_bug_predict_config,  # noqa: F401 — re-exported
    _remove_docstrings,  # noqa: F401 — re-exported
    _should_exclude_file,  # noqa: F401 — re-exported
)
from .bug_predict_report import (
    format_bug_predict_report,  # noqa: F401 — re-exported
    main,  # noqa: F401 — re-exported
)
from .data_classes import CostReport, WorkflowResult, WorkflowStage
from .step_config import WorkflowStepConfig

logger = logging.getLogger(__name__)

# Depth → max agent turns mapping
_DEPTH_MAX_TURNS: dict[str, int] = {
    "quick": 10,
    "standard": 20,
    "deep": 40,
}

_SUBAGENT_NAMES = [
    "pattern-scanner",
    "risk-correlator",
    "prevention-advisor",
]

# Preserved for backward compatibility (executor pattern)
BUG_PREDICT_STEPS = {
    "recommend": WorkflowStepConfig(
        name="recommend",
        task_type="final_review",
        tier_hint="premium",
        description="Generate bug prevention recommendations",
        max_tokens=2000,
    ),
}

_SYSTEM_PROMPT = """\
You are a bug prediction orchestrator. You coordinate three specialized \
subagents to produce a unified bug prediction report. Be thorough but \
concise. Cite file paths and line numbers when possible.\
"""

_TASK_PROMPT_TEMPLATE = """\
Analyze the codebase at {path} using the three specialized subagents \
below. Each subagent should focus on its domain and report findings \
as structured markdown.

After all subagents finish, synthesize their findings into a single \
report with these sections:

## Summary
Overall risk score (0-100) and a 2-3 sentence executive summary of \
predicted bug hotspots.

## Bugs
Predicted bugs organized by severity (HIGH, MEDIUM, LOW). Each entry \
should include file path, line number, pattern type, and description.

## Suggestions
Actionable prevention strategies ordered by priority. Include specific \
refactoring advice and testing recommendations.\
"""


class BugPredictionWorkflow(BaseWorkflow):
    """SDK-native bug prediction with three specialized subagents.

    Delegates all analysis to Claude Agent SDK subagents:
    - **pattern-scanner** — null refs, type mismatches, eval/exec,
      broad exceptions, resource leaks
    - **risk-correlator** — correlates findings with complexity
      and change frequency
    - **prevention-advisor** — prioritized mitigation strategies

    The orchestrator synthesizes findings into a unified report.

    Usage::

        workflow = BugPredictionWorkflow()
        result = await workflow.execute(path="src/", depth="standard")
    """

    name = "bug-predict"
    description = "Agent SDK-powered bug prediction with 3 specialized subagents"
    stages = ["agent-predict"]
    tier_map = {"agent-predict": ModelTier.CAPABLE}

    def __init__(self, **kwargs: Any) -> None:
        """Initialize workflow.

        Args:
            **kwargs: Passed to BaseWorkflow.__init__().
        """
        super().__init__(**kwargs)

    async def execute(self, **kwargs: Any) -> WorkflowResult:
        """Execute the Agent SDK bug prediction.

        Args:
            **kwargs: Keyword arguments.
                path (str): Required. Directory or file to scan.
                depth (str): Prediction depth — "quick", "standard",
                    or "deep". Defaults to "standard".

        Returns:
            WorkflowResult with findings, suggestions, and metadata.
        """
        path_arg: str = kwargs.get("path", "")
        depth: str = kwargs.get("depth", "standard")

        if not path_arg:
            return self._error_result("path argument is required")

        resolved_path = str(Path(path_arg).resolve())
        max_turns = _DEPTH_MAX_TURNS.get(depth, 20)

        started_at = datetime.now()

        try:
            run_result = await self._run_agent_predict(resolved_path, max_turns, depth)
            completed_at = datetime.now()

            return AgentSDKResultAdapter.from_agent_output(
                result_text=run_result.result_text,
                subagent_names=_SUBAGENT_NAMES,
                started_at=started_at,
                completed_at=completed_at,
                metadata={
                    "path": resolved_path,
                    "depth": depth,
                    "max_turns": max_turns,
                },
                agent_run_result=run_result,
            )

        except ImportError as exc:
            logger.error("Agent SDK import failed: %s", exc)
            return self._error_result(f"Agent SDK unavailable: {exc}")
        except (ConnectionError, TimeoutError) as exc:
            logger.error("Agent SDK network error: %s", exc)
            return self._error_result(f"Agent SDK connection failed: {exc}")
        except Exception as exc:  # noqa: BLE001
            # INTENTIONAL: Catch-all for unknown SDK errors to
            # return a structured WorkflowResult rather than
            # crashing the CLI.
            logger.exception(
                "Agent SDK bug prediction failed: %s",
                type(exc).__name__,
            )
            return self._error_result(f"Agent SDK error: {type(exc).__name__}: {exc}")

    async def _run_agent_predict(
        self, resolved_path: str, max_turns: int, depth: str = "standard"
    ) -> AgentRunResult:
        """Run the Agent SDK prediction and return result text.

        Args:
            resolved_path: Absolute path to scan.
            max_turns: Maximum agent turns.

        Returns:
            AgentRunResult with findings and SDK metadata.
        """
        assistant_parts: list[str] = []
        result_parts: list[str] = []
        run_result = AgentRunResult(result_text="No results returned.")
        async for message in claude_agent_sdk.query(
            prompt=_TASK_PROMPT_TEMPLATE.format(path=resolved_path),
            options=claude_agent_sdk.ClaudeAgentOptions(
                system_prompt=_SYSTEM_PROMPT,
                cwd=resolved_path,
                max_budget_usd=get_max_budget_usd(depth),
                allowed_tools=["Read", "Glob", "Grep", "Agent"],
                permission_mode="default",
                max_turns=max_turns,
                agents={
                    "pattern-scanner": claude_agent_sdk.AgentDefinition(
                        description=("Pattern scanner that finds common " "bug patterns."),
                        prompt=(
                            "You are a bug pattern scanner. Focus "
                            "on: null references, type mismatches, "
                            "race conditions, eval/exec usage, "
                            "broad exception handlers, resource "
                            "leaks, and off-by-one errors. Report "
                            "each finding with file path, line "
                            "number, pattern type, and severity."
                        ),
                        tools=["Read", "Glob", "Grep"],
                        model=get_subagent_model("pattern-scanner"),
                    ),
                    "risk-correlator": claude_agent_sdk.AgentDefinition(
                        description=("Risk correlator that assesses bug " "likelihood."),
                        prompt=(
                            "You are a risk correlator. Analyze "
                            "findings from the pattern scanner and "
                            "correlate them with file complexity, "
                            "change frequency, and historical bug "
                            "density. Assign risk scores to each "
                            "file and identify the highest-risk "
                            "modules. Report with file path, risk "
                            "score, and contributing factors."
                        ),
                        tools=["Read", "Glob", "Grep"],
                        model=get_subagent_model("risk-correlator"),
                    ),
                    "prevention-advisor": claude_agent_sdk.AgentDefinition(
                        description=("Prevention advisor that suggests " "mitigation strategies."),
                        prompt=(
                            "You are a prevention advisor. Review "
                            "the correlated risk findings and "
                            "prioritize them by impact. Suggest "
                            "specific prevention strategies: code "
                            "refactoring, additional tests, type "
                            "annotations, error handling "
                            "improvements, and architectural "
                            "changes. Report with priority, "
                            "affected files, and actionable steps."
                        ),
                        tools=["Read", "Glob", "Grep"],
                        model=get_subagent_model("prevention-advisor"),
                    ),
                },
            ),
        ):
            sdk_result = collect_agent_output(message, assistant_parts, result_parts)
            if sdk_result is not None:
                run_result = sdk_result
        run_result.result_text = build_result_text(assistant_parts, result_parts)
        return run_result

    def _error_result(self, message: str) -> WorkflowResult:
        """Build a failed WorkflowResult with the given error message.

        Args:
            message: Human-readable error description.

        Returns:
            WorkflowResult with success=False.
        """
        now = datetime.now()
        return WorkflowResult(
            success=False,
            stages=[
                WorkflowStage(
                    name="agent-predict",
                    tier=ModelTier.CAPABLE,
                    description="Agent SDK bug prediction",
                ),
            ],
            final_output=None,
            cost_report=CostReport(
                total_cost=0.0,
                baseline_cost=0.0,
                savings=0.0,
                savings_percent=0.0,
            ),
            started_at=now,
            completed_at=now,
            total_duration_ms=0,
            provider="anthropic",
            error=message,
        )


if __name__ == "__main__":
    main()
