"""
TeamForge Grader
Deterministic scoring of a completed episode.

Final score formula (0.0–1.0):
  score = 0.40 * test_pass_rate
        + 0.25 * lint_score
        + 0.20 * efficiency_score
        + 0.10 * review_quality
        + 0.05 * reflection_quality

Anti-exploit measures:
  - Test tampering detection (trivial pass/assert True bodies)
  - Implementation existence check (no empty stubs)
  - Minimum review length requirement
  - Lint-fix-only exploit detection
"""

from __future__ import annotations

import ast
import math
import re
import subprocess
import sys
from pathlib import Path
from typing import List, Optional

from models import EpisodeResult, ReviewArtifact, ReflectionArtifact


# ─────────────────────────────────────────────
# SCORING CONFIG
# ─────────────────────────────────────────────
_SCORE_MIN = 0.10   # deep interior to avoid all boundary issues
_SCORE_MAX = 0.90   # deep interior to avoid all boundary issues


def _clamp(score: float) -> float:
    """Ensure score is strictly within the open interval (0, 1)."""
    return round(max(_SCORE_MIN, min(_SCORE_MAX, score)), 4)


# ─────────────────────────────────────────────
# ANTI-EXPLOIT GUARDS
# ─────────────────────────────────────────────

def _detect_test_tampering(repo_path: str) -> bool:
    """
    Detect trivially-passing test rewrites.
    Flags: single-statement test with `pass`, `return`, or `assert True`.
    """
    for test_file in Path(repo_path).rglob("test_*.py"):
        try:
            src  = test_file.read_text()
            tree = ast.parse(src)
            for node in ast.walk(tree):
                if isinstance(node, ast.FunctionDef) and node.name.startswith("test_"):
                    body = node.body
                    if len(body) == 1:
                        stmt = body[0]
                        if isinstance(stmt, (ast.Pass, ast.Return)):
                            return True
                        if isinstance(stmt, ast.Assert):
                            val = stmt.test
                            if isinstance(val, ast.Constant) and val.value is True:
                                return True
        except Exception:
            pass
    return False


def _implementation_exists(repo_path: str) -> bool:
    """Verify at least some non-test code was written (>5 non-blank lines)."""
    total = 0
    for f in Path(repo_path).rglob("*.py"):
        if "test" in f.name or "__pycache__" in str(f):
            continue
        try:
            lines = [l for l in f.read_text().splitlines() if l.strip()]
            total += len(lines)
        except Exception:
            pass
    return total >= 5


# ─────────────────────────────────────────────
# SUB-SCORERS
# ─────────────────────────────────────────────

def score_tests(repo_path: str, timeout: int = 60) -> tuple[float, str]:
    """Run pytest and return (pass_rate 0-1, output)."""
    result = subprocess.run(
        [sys.executable, "-m", "pytest", "--tb=short", "-q", "--no-header"],
        cwd=repo_path,
        capture_output=True,
        text=True,
        timeout=timeout,
    )
    output = result.stdout + result.stderr

    passed = failed = errors = 0
    m_p = re.search(r"(\d+) passed", output)
    m_f = re.search(r"(\d+) failed", output)
    m_e = re.search(r"(\d+) error",  output)
    if m_p: passed = int(m_p.group(1))
    if m_f: failed = int(m_f.group(1))
    if m_e: errors = int(m_e.group(1))

    total = passed + failed + errors
    if total == 0:
        return _SCORE_MIN, output

    pass_rate = passed / total
    return _clamp(pass_rate), output


def score_lint(repo_path: str) -> tuple[float, str]:
    """Run ruff and return (lint_score 0-1, output)."""
    result = subprocess.run(
        [sys.executable, "-m", "ruff", "check", "."],
        cwd=repo_path,
        capture_output=True,
        text=True,
    )
    output = result.stdout + result.stderr

    if result.returncode == 0:
        return _SCORE_MAX, "No lint violations."

    violations = len([
        ln for ln in output.splitlines()
        if re.match(r".+:\d+:\d+:", ln)
    ])
    # Strictly interior [SCORE_MIN, SCORE_MAX]
    raw_score = 1.0 - violations * 0.07
    return _clamp(raw_score), output


def score_review_quality(
    reviews: List[ReviewArtifact],
    required_keywords: List[str],
) -> float:
    """Keyword-based review quality with minimum length requirement."""
    if not reviews:
        return _SCORE_MIN

    combined = " ".join(r.text.lower() for r in reviews)

    # Anti-exploit: minimum meaningful length
    if len(combined.strip()) < 40:
        return _SCORE_MIN + 0.05

    # Keyword coverage
    if not required_keywords:
        kw_score = 0.5
    else:
        found    = sum(1 for kw in required_keywords if kw.lower() in combined)
        kw_score = found / len(required_keywords)

    # Length bonus (up to 0.2 for thorough reviews)
    avg_len      = sum(len(r.text) for r in reviews) / len(reviews)
    length_bonus = min(0.2, avg_len / 600)

    # Specificity bonus: mentions actual code identifiers
    code_words   = re.findall(r'\b[a-z_]{3,}\(\)', combined)
    specificity  = min(0.1, len(set(code_words)) * 0.025)

    return _clamp(kw_score * 0.7 + length_bonus + specificity)


def score_reflection_quality(reflections: List[ReflectionArtifact]) -> float:
    """Score reflections on depth and actionability."""
    if not reflections:
        return _SCORE_MIN

    total = 0.0
    for ref in reflections:
        depth = 0.0
        if len(ref.what_went_well.strip()) > 40:
            depth += 0.5
        if len(ref.what_to_improve.strip()) > 40:
            depth += 0.5
        # Bonus if adjusted_plan provided
        if ref.adjusted_plan and len(ref.adjusted_plan.strip()) > 20:
            depth = min(0.9, depth + 0.2)
        total += depth

    # Strictly (0, 1) - Safer interior
    return _clamp(total / max(1, len(reflections)))


def score_efficiency(total_steps: int, max_steps: int) -> float:
    """Reward solving in fewer steps with smooth decay curve."""
    ratio = total_steps / max_steps
    # Strictly (0, 1) - Safer interior
    return _clamp(math.exp(-2.0 * max(0, ratio - 0.25)))


# ─────────────────────────────────────────────
# MAIN GRADER
# ─────────────────────────────────────────────

def grade_episode(
    repo_path: str,
    task_id: str,
    total_steps: int,
    max_steps: int,
    reviews: List[ReviewArtifact],
    reflections: List[ReflectionArtifact],
    required_keywords: Optional[List[str]] = None,
) -> EpisodeResult:
    """
    Run full deterministic grading pipeline.
    Returns EpisodeResult with final_score in [0.0, 1.0].
    """
    log: List[str] = []
    required_keywords = required_keywords or []

    # ── Anti-exploit checks ──
    if _detect_test_tampering(repo_path):
        log.append("[GRADER] ⚠  TEST TAMPERING DETECTED — score zeroed")
        return EpisodeResult(
            task_id=task_id, total_steps=total_steps,
            final_score=_SCORE_MIN, passed=False,
            log=log + ["Test files were trivially rewritten to force passes."],
        )

    if not _implementation_exists(repo_path):
        log.append("[GRADER] ⚠  NO IMPLEMENTATION FOUND — score zeroed")
        return EpisodeResult(
            task_id=task_id, total_steps=total_steps,
            final_score=_SCORE_MIN, passed=False,
            log=log + ["No non-test code was written."],
        )

    # ── Tests ──
    test_pass_rate, test_output = score_tests(repo_path)
    log.append(f"[GRADER] test_pass_rate={test_pass_rate:.3f}")
    log.append(f"[GRADER] test_output=\n{test_output[:800]}")

    # ── Lint ──
    lint_score, lint_output = score_lint(repo_path)
    log.append(f"[GRADER] lint_score={lint_score:.3f}")

    # ── Efficiency ──
    efficiency = score_efficiency(total_steps, max_steps)
    log.append(f"[GRADER] efficiency={efficiency:.3f} ({total_steps}/{max_steps} steps)")

    # ── Review quality ──
    review_q = score_review_quality(reviews, required_keywords)
    log.append(f"[GRADER] review_quality={review_q:.3f}")

    # ── Reflection quality ──
    reflect_q = score_reflection_quality(reflections)
    log.append(f"[GRADER] reflection_quality={reflect_q:.3f}")

    # ── Final weighted score ──
    final = (
        0.40 * test_pass_rate
        + 0.25 * lint_score
        + 0.20 * efficiency
        + 0.10 * review_q
        + 0.05 * reflect_q
    )
    # Strictly (0, 1) interior range to satisfy Phase 2 validator
    final = _clamp(final)
    log.append(f"[GRADER] FINAL_SCORE={final:.4f}")

    return EpisodeResult(
        task_id=task_id,
        total_steps=total_steps,
        test_pass_rate=test_pass_rate,
        lint_score=lint_score,
        efficiency_score=efficiency,
        review_quality=review_q,
        reflection_quality=reflect_q,
        final_score=final,
        passed=test_pass_rate >= 0.9 and lint_score >= 0.7,
        log=log,
    )


def grade_task(repo_path: str, **kwargs) -> float:
    """
    OpenEnv standard grader bridge – entry point from YAML.
    Returns ONLY a float strictly between 0 and 1.
    """
    import json
    import os
    from typing import List
    from pydantic import TypeAdapter

    metadata_path = os.path.join(repo_path, "grading_metadata.json")
    
    # Default fallback values for out-of-band grading
    task_id = "unknown"
    total_steps = 1
    max_steps = 20
    reviews = []
    reflections = []
    required_keywords = []

    if os.path.exists(metadata_path):
        try:
            with open(metadata_path, "r") as f:
                meta = json.load(f)
            task_id = meta.get("task_id", task_id)
            total_steps = meta.get("total_steps", total_steps)
            max_steps = meta.get("max_steps", max_steps)
            
            # Use TypeAdapter for robust Pydantic deserialization
            from models import ReviewArtifact, ReflectionArtifact
            reviews = TypeAdapter(List[ReviewArtifact]).validate_python(meta.get("reviews", []))
            reflections = TypeAdapter(List[ReflectionArtifact]).validate_python(meta.get("reflections", []))
            required_keywords = meta.get("required_keywords", [])
        except Exception:
            pass

    try:
        result = grade_episode(
            repo_path=repo_path,
            task_id=task_id,
            total_steps=total_steps,
            max_steps=max_steps,
            reviews=reviews,
            reflections=reflections,
            required_keywords=required_keywords,
        )
        return float(result.final_score)
    except Exception:
        return _SCORE_MIN