"""Data models for cross-eval pipeline."""
from __future__ import annotations

from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional


@dataclass
class AgentConfig:
    """Definition of a single agent."""

    name: str
    command: str
    args: list[str] = field(default_factory=list)
    system_prompt: Optional[str] = None
    reasoning_effort: Optional[str] = None
    stdin_mode: bool = False


@dataclass
class StepConfig:
    """One step in the pipeline."""

    name: str
    agent: str  # reference to agents key
    role: str  # "generate" or "review"
    prompt_template: str  # "default:<role>" or file path
    output_key: str
    verdict: bool = False
    verdict_pattern: str = r"VERDICT:\s*PASS"
    context_override: dict[str, str] = field(default_factory=dict)
    parallel: bool = False  # Can run concurrently with adjacent parallel steps


@dataclass
class PhaseConfig:
    """One phase in a multi-phase pipeline (e.g. review-fix)."""

    name: str
    steps: list[StepConfig] = field(default_factory=list)
    max_iterations: int = 10
    consecutive_pass: int = 1  # stop after N consecutive PASSes


@dataclass
class PipelineConfig:
    """Full cross-eval configuration."""

    output_dir: Path = field(default_factory=lambda: Path("output"))
    max_iterations: int = 3
    min_iterations: int = 1
    verbose: bool = False
    language: str = "en"  # "en" or "ko"
    inputs: dict[str, Path | str] = field(default_factory=dict)
    agents: dict[str, AgentConfig] = field(default_factory=dict)
    coders: list[str] = field(default_factory=list)
    reviewers: list[str] = field(default_factory=list)
    seniors: list[str] = field(default_factory=list)
    pipeline: list[StepConfig] = field(default_factory=list)
    phases: list[PhaseConfig] = field(default_factory=list)
    preset_name: str = "custom"
    _config_path: Optional[Path] = field(default=None, repr=False)
    _config_mtime: Optional[float] = field(default=None, repr=False)


@dataclass
class AgentResult:
    """Result from an agent invocation."""

    output: str
    exit_code: int
    agent_name: str
    step_name: str
    duration_seconds: float


@dataclass
class ReviewMetrics:
    """Parsed metrics from a single review output."""

    # Severity counts
    critical: int = 0
    major: int = 0
    minor: int = 0

    # Category counts
    over_engineering: int = 0
    omission: int = 0

    # Assessment counts
    confirmed: int = 0
    dismissed: int = 0


@dataclass
class IterationResult:
    """Results from a single iteration."""

    iteration: int
    step_results: dict[str, AgentResult] = field(default_factory=dict)
    step_outputs: dict[str, str] = field(default_factory=dict)
    verdict: Optional[str] = None
    feedback: Optional[str] = None
    phase_name: Optional[str] = None
    repeated_aggregate_warning: Optional[str] = None
    review_metrics: Optional[ReviewMetrics] = None


@dataclass
class PipelineResult:
    """Results from the entire pipeline run."""

    iterations: list[IterationResult] = field(default_factory=list)
    final_verdict: str = "MAX_ITERATIONS_REACHED"
    total_duration: float = 0.0
    run_dir: Optional[Path] = None
    repeated_aggregate_warnings: list[str] = field(default_factory=list)