"""
TeamForge Environment
Full OpenEnv-compliant environment simulating an autonomous software team.

Interface:
  env = TeamForgeEnv()
  obs = env.reset(task_id)
  obs = env.step(action)
  state = env.state()
"""

from __future__ import annotations

import re
import subprocess
import sys
import time
from pathlib import Path
from typing import Any, Dict, List, Optional

from models import (
    Action,
    ActionStatus,
    Commit,
    EditFile,
    EpisodeResult,
    FileSnapshot,
    GenerateReview,
    LintResult,
    Observation,
    PhaseState,
    PlanStep,
    ReflectionArtifact,
    RequestIteration,
    ReviewArtifact,
    RunLint,
    RunTests,
    SelfReflect,
    TaskDifficulty,
    TestResult,
)
from sandbox.git_sandbox import GitSandbox
from tasks.task_registry import get_task
from reward import RewardCalculator
from grader import grade_episode


class TeamForgeEnv:
    """
    OpenEnv-compliant environment for autonomous software team simulation.

    An episode represents one attempt to complete a software engineering task.
    The agent issues structured actions; the environment executes them against
    a real Git repository and returns observations with dense rewards.
    """

    def __init__(self, log_dir: Optional[str] = None):
        self._sandbox = GitSandbox()
        self._reward_calc = RewardCalculator()
        self._obs: Optional[Observation] = None
        self._task_module: Any = None
        self._log_dir = log_dir
        self._logs: List[str] = []

        # Episode state
        self._step_number = 0
        self._cumulative_reward = 0.001
        self._plan: List[PlanStep] = []
        self._reviews: List[ReviewArtifact] = []
        self._reflections: List[ReflectionArtifact] = []
        self._last_test_result: Optional[TestResult] = None
        self._last_lint_result: Optional[LintResult] = None

    # ─────────────────────────────────────────────
    # OpenEnv INTERFACE
    # ─────────────────────────────────────────────

    def reset(self, task_id: str) -> Observation:
        """
        Start a new episode for the given task.
        Tears down any previous sandbox and initialises a fresh git repo.

        Args:
            task_id: One of easy_bugfix_chunk_list | medium_refactor_stats |
                     hard_lru_cache_performance

        Returns:
            Initial observation with full repo snapshot.
        """
        self._log(f"[START] task={task_id}")

        # Clean up previous episode
        self._sandbox.teardown()
        self._sandbox = GitSandbox()

        # Load task
        self._task_module = get_task(task_id)
        self._reward_calc = RewardCalculator()

        # Detect test files and register with reward calculator
        test_files = [
            p for p in self._task_module.INITIAL_FILES
            if "test" in p.lower()
        ]
        self._reward_calc.set_test_files(test_files)

        # Reset episode state
        self._step_number = 0
        self._cumulative_reward = 0.1
        self._plan = []
        self._reviews = []
        self._reflections = []
        self._last_test_result = None
        self._last_lint_result = None
        self._logs = [f"[START] task={task_id}"]

        # Initialise git sandbox with task files
        self._sandbox.init(self._task_module.INITIAL_FILES)

        # Build initial observation
        self._obs = self._build_observation(
            action_type=None,
            status=ActionStatus.SUCCESS,
            output="Environment initialized.",
            reward=0.1,
            done=False,
        )
        return self._obs

    def step(self, action: Action) -> Observation:
        """
        Execute one action and return the resulting observation.

        Args:
            action: A typed Action model (PlanStep, EditFile, RunTests, …)

        Returns:
            Updated Observation with reward, done flag, and all state.
        """
        if self._obs is None:
            raise RuntimeError("Call reset() before step()")

        self._step_number += 1
        action_type = action.type
        self._log(f"[STEP {self._step_number}] action={action_type}")

        # ── Max steps guard ──
        max_steps = self._task_module.MAX_STEPS
        if self._step_number > max_steps:
            return self._finalize(reason="Max steps exceeded")

        # ── Dispatch action ──
        status = ActionStatus.SUCCESS
        output = ""
        edited_file: Optional[str] = None
        tests_passed: Optional[int] = None
        lint_violations: Optional[int] = None

        try:
            if isinstance(action, PlanStep):
                output = self._handle_plan_step(action)
            elif isinstance(action, EditFile):
                output, edited_file = self._handle_edit_file(action)
            elif isinstance(action, RunTests):
                output = self._handle_run_tests(action)
                tests_passed = (self._last_test_result.passed
                                if self._last_test_result else 0)
            elif isinstance(action, RunLint):
                output = self._handle_run_lint(action)
                lint_violations = (self._last_lint_result.violations
                                   if self._last_lint_result else 0)
            elif isinstance(action, GenerateReview):
                output = self._handle_generate_review(action)
            elif isinstance(action, Commit):
                output = self._handle_commit(action)
            elif isinstance(action, SelfReflect):
                output = self._handle_self_reflect(action)
            elif isinstance(action, RequestIteration):
                output = self._handle_request_iteration(action)
            else:
                status = ActionStatus.FAILURE
                output = f"Unknown action type: {action_type}"

        except Exception as exc:
            status = ActionStatus.FAILURE
            output = f"Action failed with exception: {exc}"
            self._log(f"[ERROR] {exc}")

        # ── Compute reward ──
        reward = self._reward_calc.compute(
            action_type=action_type,
            action_success=(status == ActionStatus.SUCCESS),
            action_output=output,
            tests_passed=tests_passed,
            lint_violations=lint_violations,
            edited_file=edited_file,
        )
        self._cumulative_reward += reward

        # ── Check done conditions ──
        done = self._check_done()
        self._log(f"[STEP {self._step_number}] reward={reward:.4f} done={done}")

        self._obs = self._build_observation(
            action_type=action_type,
            status=status,
            output=output,
            reward=reward,
            done=done,
        )
        return self._obs

    def state(self) -> Dict[str, Any]:
        """
        Return current environment state as a plain dict.
        Useful for serialisation and logging.
        """
        if self._obs is None:
            return {"status": "not_started"}
        return {
            "task_id": self._obs.task_id,
            "step": self._step_number,
            "phase": self._obs.phase.value,
            "cumulative_reward": self._cumulative_reward,
            "tests_passed": (self._last_test_result.passed
                             if self._last_test_result else 0),
            "tests_failed": (self._last_test_result.failed
                             if self._last_test_result else 0),
            "lint_violations": (self._last_lint_result.violations
                                if self._last_lint_result else 0),
            "commits": len(self._sandbox.get_log()),
            "plan_steps": len(self._plan),
            "reviews": len(self._reviews),
            "reflections": len(self._reflections),
            "done": self._obs.done,
        }

    def grade(self) -> EpisodeResult:
        """Run the deterministic grader and return an EpisodeResult."""
        required_kw = getattr(
            self._task_module, "REQUIRED_KEYWORDS_IN_REVIEW", []
        )
        return grade_episode(
            repo_path=str(self._sandbox.repo_path),
            task_id=self._task_module.TASK_ID,
            total_steps=self._step_number,
            max_steps=self._task_module.MAX_STEPS,
            reviews=self._reviews,
            reflections=self._reflections,
            required_keywords=required_kw,
        )

    # ─────────────────────────────────────────────
    # ACTION HANDLERS
    # ─────────────────────────────────────────────

    def _handle_plan_step(self, action: PlanStep) -> str:
        self._plan.append(action)
        return (
            f"Plan step {action.step_number} recorded: {action.description} "
            f"[effort={action.estimated_effort}]"
        )

    def _handle_edit_file(self, action: EditFile) -> tuple[str, str]:
        self._sandbox.write_file(action.file_path, action.content)
        size = len(action.content.encode())
        return (
            f"Wrote {size} bytes to {action.file_path}. Reason: {action.reason}",
            action.file_path,
        )

    def _handle_run_tests(self, action: RunTests) -> str:
        cmd = [
            sys.executable, "-m", "pytest",
            "--tb=short", "-q", "--no-header",
            f"--timeout={action.timeout_seconds}",
        ]
        if action.test_path:
            cmd.append(action.test_path)

        start = time.perf_counter()
        result = subprocess.run(
            cmd,
            cwd=str(self._sandbox.repo_path),
            capture_output=True,
            text=True,
            timeout=action.timeout_seconds + 5,
        )
        elapsed = time.perf_counter() - start
        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))

        self._last_test_result = TestResult(
            passed=passed,
            failed=failed,
            errors=errors,
            output=output[:2000],
            duration_seconds=elapsed,
        )
        return output[:2000]

    def _handle_run_lint(self, action: RunLint) -> str:
        cmd = [sys.executable, "-m", "ruff", "check"]
        if action.fix:
            cmd.append("--fix")
        if action.file_path:
            cmd.append(action.file_path)
        else:
            cmd.append(".")

        result = subprocess.run(
            cmd,
            cwd=str(self._sandbox.repo_path),
            capture_output=True,
            text=True,
        )
        output = result.stdout + result.stderr
        violations = len([
            ln for ln in output.splitlines()
            if re.match(r".+:\d+:\d+:", ln)
        ])
        score = max(0.001, min(0.999, 1.0 - violations * 0.05))
        self._last_lint_result = LintResult(
            violations=violations,
            output=output[:2000],
            score=score,
        )
        return output[:2000] or "No lint violations found."

    def _handle_generate_review(self, action: GenerateReview) -> str:
        review = ReviewArtifact(
            reviewer="agent",
            focus_areas=action.focus_areas,
            text=action.review_text,
            timestamp_step=self._step_number,
        )
        self._reviews.append(review)
        return f"Review recorded ({len(action.review_text)} chars). Focus: {action.focus_areas}"

    def _handle_commit(self, action: Commit) -> str:
        if not self._sandbox.has_changes():
            return "Nothing to commit. Working tree clean."
        sha = self._sandbox.commit(
            message=action.message,
            files=action.files if action.files else None,
        )
        if sha:
            return f"Committed: {sha} — {action.message}"
        return "Commit failed (possibly nothing to stage)."

    def _handle_self_reflect(self, action: SelfReflect) -> str:
        reflection = ReflectionArtifact(
            step=self._step_number,
            what_went_well=action.what_went_well,
            what_to_improve=action.what_to_improve,
            adjusted_plan=action.adjusted_plan,
        )
        self._reflections.append(reflection)
        return (
            f"Reflection recorded at step {self._step_number}. "
            f"Improving: {action.what_to_improve[:80]}"
        )

    def _handle_request_iteration(self, action: RequestIteration) -> str:
        issues = ", ".join(action.target_issues) if action.target_issues else "none specified"
        return f"Iteration requested: {action.reason} | Issues: {issues}"

    # ─────────────────────────────────────────────
    # HELPERS
    # ─────────────────────────────────────────────

    def _check_done(self) -> bool:
        """Episode is done if all tests pass and lint is clean."""
        if self._last_test_result is None:
            return False
        tests_ok = (
            self._last_test_result.failed == 0
            and self._last_test_result.errors == 0
            and self._last_test_result.passed > 0
        )
        lint_ok = (
            self._last_lint_result is None
            or self._last_lint_result.violations == 0
        )
        committed = len(self._sandbox.get_log()) > 1  # beyond initial commit
        return tests_ok and lint_ok and committed

    def _finalize(self, reason: str) -> Observation:
        self._log(f"[END] {reason}")
        self._obs = self._build_observation(
            action_type=None,
            status=ActionStatus.FAILURE,
            output=reason,
            reward=0.001,
            done=True,
        )
        return self._obs

    def _build_observation(
        self,
        action_type: Optional[str],
        status: ActionStatus,
        output: str,
        reward: float,
        done: bool,
    ) -> Observation:
        """Assemble a full Observation from current environment state."""
        # Repo files snapshot (only .py, .md, .toml — cap at 8 files)
        all_files = self._sandbox.get_all_files()
        snapshots = [
            FileSnapshot(
                path=p,
                content=c[:3000],  # truncate large files
                size_bytes=len(c.encode()),
            )
            for p, c in list(all_files.items())[:12]
        ]

        # Determine phase
        phase = self._infer_phase()

        return Observation(
            task_id=self._task_module.TASK_ID,
            task_description=self._task_module.DESCRIPTION,
            difficulty=TaskDifficulty(self._task_module.DIFFICULTY),
            step_number=self._step_number,
            max_steps=self._task_module.MAX_STEPS,
            phase=phase,
            repo_files=snapshots,
            git_log=self._sandbox.get_log(n=5),
            last_action_type=action_type,
            last_action_status=status,
            last_action_output=output,
            test_results=self._last_test_result,
            lint_results=self._last_lint_result,
            plan=self._plan,
            reviews=self._reviews,
            reflections=self._reflections,
            reward=reward,
            cumulative_reward=self._cumulative_reward,
            done=done,
            info={
                "sandbox_path": str(self._sandbox.repo_path),
                "task_difficulty": self._task_module.DIFFICULTY,
            },
        )

    def _infer_phase(self) -> PhaseState:
        if self._step_number == 0:
            return PhaseState.PLANNING
        if self._plan and not self._last_test_result:
            return PhaseState.CODING
        if self._last_test_result and self._last_test_result.failed > 0:
            return PhaseState.TESTING
        if self._last_test_result and self._last_test_result.failed == 0 and not self._reviews:
            return PhaseState.REVIEWING
        if self._reviews and not self._reflections:
            return PhaseState.REFLECTING
        if self._obs and self._obs.done:
            return PhaseState.DONE
        return PhaseState.CODING

    def _log(self, msg: str) -> None:
        self._logs.append(msg)
        print(msg)