"""
Main SDG pipeline orchestration.

Stages:
  1. Load HF dataset → extract instruction_seed
  2. Init VLLMEngine
  3. Generate N candidates per prompt, static-check, collect valid samples
  4. Iterative UQ (max_rounds): cycle → factual → correctness, early-exit
  5. Write output JSONL + stats.json + config.yaml
"""

from __future__ import annotations

import json
import math
import sys
from dataclasses import dataclass, field, asdict
from pathlib import Path

from datasets import load_dataset
from tqdm import tqdm

from sdg.config import SDGConfig
from sdg.inference import VLLMEngine
from sdg.prompts import REASONING_INSTRUCTION
from sdg.validation import (
    collect_valid_samples,
    validate_batch_correctness,
    validate_batch_cycle,
    validate_batch_factual,
    validate_batch_simple,
)

# Valid values for SDGConfig.selection_policy
VALID_SELECTION_POLICIES = {"first_valid", "all_valid"}


# =============================================================================
# TeeStream — duplicate stdout to a log file
# =============================================================================

class TeeStream:
    """Write to both the original stream and a log file."""

    def __init__(self, log_path: Path, original_stream, share_file=None):
        self.log_file = share_file if share_file is not None else open(log_path, "w")
        self._owns_file = share_file is None
        self.original = original_stream

    def write(self, data):
        self.original.write(data)
        self.log_file.write(data)
        self.log_file.flush()

    def flush(self):
        self.original.flush()
        self.log_file.flush()

    def fileno(self):
        return self.original.fileno()

    def isatty(self):
        return self.original.isatty()

    def close(self):
        if self._owns_file:
            self.log_file.close()


# =============================================================================
# Row state tracker
# =============================================================================

@dataclass
class RowState:
    row_id: int
    instruction_seed: str
    valid_samples: list[str] = field(default_factory=list)
    valid_indices: list[int] = field(default_factory=list)
    passed_sample: str | None = None  # first_valid: single sample that passed all checks
    passed_samples: list[str] = field(default_factory=list)  # all_valid: all samples that passed
    passed_round: int | None = None
    best_stage: int = 0  # deepest check reached: 0=none, 1=cycle, 2=factual, 3=correctness


# =============================================================================
# Dataset loading
# =============================================================================

def load_seeds(config: SDGConfig) -> list[str]:
    """Load instruction seeds from HuggingFace dataset."""
    print(f"Loading dataset: {config.dataset_hf_id} (config={config.dataset_hf_config}, split={config.dataset_split}, revision={config.dataset_hf_revision})")
    ds = load_dataset(config.dataset_hf_id, name=config.dataset_hf_config, split=config.dataset_split, revision=config.dataset_hf_revision)

    if config.limit:
        ds = ds.select(range(min(config.limit, len(ds))))
        print(f"  Limited to {len(ds)} rows")

    seeds: list[str] = []
    for row in ds:
        if config.dataset_format == "openthoughts4":
            convos = row.get("conversations", [])
            if convos:
                seeds.append(convos[0]["value"])
        elif config.dataset_format == "messages":
            msgs = row.get("messages", [])
            if msgs:
                seeds.append(msgs[0]["content"])
        else:
            raise ValueError(f"Unknown dataset_format: {config.dataset_format}")

    print(f"  Extracted {len(seeds)} instruction seeds")
    return seeds


# =============================================================================
# Stage 3: Generation + static check
# =============================================================================

def generate_candidates(
    engine: VLLMEngine,
    seeds: list[str],
    config: SDGConfig,
) -> list[RowState]:
    """Generate N candidates per seed, run static check, build RowState list."""
    rows: list[RowState] = []
    total = len(seeds)
    processed = 0

    for batch_start in range(0, total, config.gen_batch_size):
        batch_seeds = seeds[batch_start : batch_start + config.gen_batch_size]
        if config.check_boxed:
            prompts = [f"{seed}\n{REASONING_INSTRUCTION}\n" for seed in batch_seeds]
        else:
            prompts = [f"{seed}\n" for seed in batch_seeds]

        # generate N samples per prompt
        all_samples = engine.generate_multi(prompts)

        for i, (seed, samples) in enumerate(zip(batch_seeds, all_samples)):
            valid, indices = collect_valid_samples(
                samples, config.is_instruction_tuned, config.check_boxed
            )
            if valid:
                rows.append(
                    RowState(
                        row_id=batch_start + i,
                        instruction_seed=seed,
                        valid_samples=valid,
                        valid_indices=indices,
                    )
                )

        processed += len(batch_seeds)
        print(f"[Generation] {processed}/{total} examples ({processed/total*100:.1f}%) | {len(rows)} valid")

    print(
        f"  {len(rows)} / {len(seeds)} rows have at least one valid sample"
    )
    return rows


# =============================================================================
# Stage 4: Iterative UQ
# =============================================================================

@dataclass
class RoundStats:
    round_idx: int = 0
    pending: int = 0
    exhausted: int = 0
    cycle_passed: int = 0
    cycle_failed: int = 0
    factual_passed: int = 0
    factual_failed: int = 0
    correctness_passed: int = 0
    correctness_failed: int = 0
    newly_passed: int = 0


def _validate_round(
    engine: VLLMEngine,
    round_items: list[tuple[RowState, str]],
    config: SDGConfig,
    stats: RoundStats,
) -> tuple[list[tuple[RowState, str]], list[RowState], list[RowState], list[RowState]]:
    """Run cycle → factual → correctness on a batch of (row, sample) pairs.

    Returns:
        (correctness_passed_items, cycle_failed_rows, factual_failed_rows, correctness_failed_rows)
    """
    # ── cycle consistency ────────────────────────────────────────
    cycle_items = [
        (row.row_id, row.instruction_seed, sample)
        for row, sample in round_items
    ]
    cycle_results = validate_batch_cycle(
        engine, cycle_items, config.val_batch_size
    )

    cycle_passed_items: list[tuple[RowState, str]] = []
    cycle_failed_rows: list[RowState] = []
    for row, sample in round_items:
        if cycle_results.get(row.row_id, False):
            cycle_passed_items.append((row, sample))
            stats.cycle_passed += 1
            row.best_stage = max(row.best_stage, 1)
        else:
            cycle_failed_rows.append(row)
            stats.cycle_failed += 1

    print(
        f"  Cycle: {stats.cycle_passed} passed, {stats.cycle_failed} failed"
    )

    # ── factual check (cycle-passed only) ────────────────────────
    factual_passed_items: list[tuple[RowState, str]] = []
    factual_failed_rows: list[RowState] = []
    if cycle_passed_items:
        factual_items = [
            (row.row_id, row.instruction_seed, sample)
            for row, sample in cycle_passed_items
        ]
        factual_results = validate_batch_factual(
            engine, factual_items, config.val_batch_size
        )
        for row, sample in cycle_passed_items:
            if factual_results.get(row.row_id, False):
                factual_passed_items.append((row, sample))
                stats.factual_passed += 1
                row.best_stage = max(row.best_stage, 2)
            else:
                factual_failed_rows.append(row)
                stats.factual_failed += 1
    print(
        f"  Factual: {stats.factual_passed} passed, {stats.factual_failed} failed"
    )

    # ── correctness check (factual-passed only) ─────────────────
    correctness_passed_items: list[tuple[RowState, str]] = []
    correctness_failed_rows: list[RowState] = []
    if factual_passed_items:
        correctness_items = [
            (row.row_id, row.instruction_seed, sample)
            for row, sample in factual_passed_items
        ]
        correctness_results = validate_batch_correctness(
            engine, correctness_items, config.val_batch_size
        )
        for row, sample in factual_passed_items:
            if correctness_results.get(row.row_id, False):
                correctness_passed_items.append((row, sample))
                stats.correctness_passed += 1
                row.best_stage = max(row.best_stage, 3)
            else:
                correctness_failed_rows.append(row)
                stats.correctness_failed += 1
    print(
        f"  Correctness: {stats.correctness_passed} passed, "
        f"{stats.correctness_failed} failed"
    )

    return correctness_passed_items, cycle_failed_rows, factual_failed_rows, correctness_failed_rows


def run_uq_rounds(
    engine: VLLMEngine,
    rows: list[RowState],
    config: SDGConfig,
) -> tuple[list[RowState], list[RowState], list[RoundStats]]:
    """
    Iterative UQ loop (first_valid policy).

    Tests one sample per row per round.  Once a row passes all three checks
    it is moved to passed_rows and not tested again.

    Returns:
        (passed_rows, remaining_rows, per_round_stats)
    """
    passed_rows: list[RowState] = []
    pending_rows: list[RowState] = rows[:]
    exhausted_rows: list[RowState] = []
    all_stats: list[RoundStats] = []

    for round_idx in range(config.num_generations):
        if not pending_rows:
            print(f"Round {round_idx}: no pending rows — done early")
            break

        stats = RoundStats(round_idx=round_idx, pending=len(pending_rows))
        print(f"\n{'='*60}")
        print(f"Round {round_idx}: {len(pending_rows)} pending rows")
        print(f"{'='*60}")

        # ── extract sample at round_idx ──────────────────────────────
        round_items: list[tuple[RowState, str]] = []  # (row, current_sample)
        skipped: list[RowState] = []

        for row in pending_rows:
            if round_idx < len(row.valid_samples):
                round_items.append((row, row.valid_samples[round_idx]))
            else:
                skipped.append(row)  # exhausted samples

        if not round_items:
            print(f"  No rows have a sample at index {round_idx} — stopping")
            break

        stats.exhausted = len(skipped)
        exhausted_rows.extend(skipped)
        print(f"  {len(round_items)} rows have sample at index {round_idx}")
        if skipped:
            print(f"  {len(skipped)} rows exhausted all samples (dropped)")

        # ── validate ─────────────────────────────────────────────────
        correctness_passed, cycle_failed, factual_failed, correctness_failed = (
            _validate_round(engine, round_items, config, stats)
        )

        newly_passed: list[RowState] = []
        for row, sample in correctness_passed:
            row.passed_sample = sample
            row.passed_round = round_idx
            newly_passed.append(row)

        stats.newly_passed = len(newly_passed)
        passed_rows.extend(newly_passed)

        # ── remaining pending = all failures from this round ─────────
        pending_rows = cycle_failed + factual_failed + correctness_failed

        print(
            f"  Round {round_idx} summary: {stats.newly_passed} newly passed, "
            f"{len(pending_rows)} still pending"
        )
        all_stats.append(stats)

    # remaining = still pending after last round + exhausted across all rounds
    all_failed = pending_rows + exhausted_rows
    return passed_rows, all_failed, all_stats


def _validate_round_simple(
    engine: VLLMEngine,
    round_items: list[tuple[RowState, str]],
    config: SDGConfig,
    stats: RoundStats,
) -> tuple[list[tuple[RowState, str]], list[RowState]]:
    """Single-prompt yes/no validation. Reuses correctness_* stat fields."""
    items = [
        (row.row_id, row.instruction_seed, sample)
        for row, sample in round_items
    ]
    results = validate_batch_simple(engine, items, config.val_batch_size)

    passed_items: list[tuple[RowState, str]] = []
    failed_rows: list[RowState] = []
    for row, sample in round_items:
        if results.get(row.row_id, False):
            passed_items.append((row, sample))
            stats.correctness_passed += 1
            row.best_stage = max(row.best_stage, 3)
        else:
            failed_rows.append(row)
            stats.correctness_failed += 1
            # treat simple-fail as a correctness-stage failure for reporting
            row.best_stage = max(row.best_stage, 2)

    print(
        f"  Simple: {stats.correctness_passed} passed, "
        f"{stats.correctness_failed} failed"
    )
    return passed_items, failed_rows


def run_simple_rounds(
    engine: VLLMEngine,
    rows: list[RowState],
    config: SDGConfig,
) -> tuple[list[RowState], list[RowState], list[RoundStats]]:
    """Iterative simple validation loop (first_valid policy)."""
    passed_rows: list[RowState] = []
    pending_rows: list[RowState] = rows[:]
    exhausted_rows: list[RowState] = []
    all_stats: list[RoundStats] = []

    for round_idx in range(config.num_generations):
        if not pending_rows:
            print(f"Round {round_idx}: no pending rows — done early")
            break

        stats = RoundStats(round_idx=round_idx, pending=len(pending_rows))
        print(f"\n{'='*60}")
        print(f"Round {round_idx}: {len(pending_rows)} pending rows")
        print(f"{'='*60}")

        round_items: list[tuple[RowState, str]] = []
        skipped: list[RowState] = []
        for row in pending_rows:
            if round_idx < len(row.valid_samples):
                round_items.append((row, row.valid_samples[round_idx]))
            else:
                skipped.append(row)

        if not round_items:
            print(f"  No rows have a sample at index {round_idx} — stopping")
            break

        stats.exhausted = len(skipped)
        exhausted_rows.extend(skipped)
        print(f"  {len(round_items)} rows have sample at index {round_idx}")
        if skipped:
            print(f"  {len(skipped)} rows exhausted all samples (dropped)")

        passed_items, failed_rows = _validate_round_simple(
            engine, round_items, config, stats
        )

        newly_passed: list[RowState] = []
        for row, sample in passed_items:
            row.passed_sample = sample
            row.passed_round = round_idx
            newly_passed.append(row)

        stats.newly_passed = len(newly_passed)
        passed_rows.extend(newly_passed)
        pending_rows = failed_rows

        print(
            f"  Round {round_idx} summary: {stats.newly_passed} newly passed, "
            f"{len(pending_rows)} still pending"
        )
        all_stats.append(stats)

    all_failed = pending_rows + exhausted_rows
    return passed_rows, all_failed, all_stats


def run_simple_rounds_all_valid(
    engine: VLLMEngine,
    rows: list[RowState],
    config: SDGConfig,
) -> tuple[list[RowState], list[RowState], list[RoundStats]]:
    """Iterative simple validation loop (all_valid policy)."""
    all_stats: list[RoundStats] = []

    for round_idx in range(config.num_generations):
        round_items: list[tuple[RowState, str]] = []
        for row in rows:
            if round_idx < len(row.valid_samples):
                round_items.append((row, row.valid_samples[round_idx]))

        if not round_items:
            print(f"Round {round_idx}: no rows have a sample at this index — stopping")
            break

        stats = RoundStats(round_idx=round_idx, pending=len(round_items))
        print(f"\n{'='*60}")
        print(f"Round {round_idx}: {len(round_items)} rows")
        print(f"{'='*60}")

        passed_items, _ = _validate_round_simple(
            engine, round_items, config, stats
        )

        for row, sample in passed_items:
            row.passed_samples.append(sample)
            if row.passed_round is None:
                row.passed_round = round_idx

        stats.newly_passed = len(passed_items)
        print(
            f"  Round {round_idx} summary: {stats.newly_passed} samples passed"
        )
        all_stats.append(stats)

    passed_rows = [row for row in rows if row.passed_samples]
    failed_rows = [row for row in rows if not row.passed_samples]
    return passed_rows, failed_rows, all_stats


def run_uq_rounds_all_valid(
    engine: VLLMEngine,
    rows: list[RowState],
    config: SDGConfig,
) -> tuple[list[RowState], list[RowState], list[RoundStats]]:
    """
    Iterative UQ loop (all_valid policy).

    Tests every valid sample for every row.  A row is never removed early;
    all samples that pass all three checks are collected in row.passed_samples.

    Returns:
        (passed_rows, failed_rows, per_round_stats)
    """
    all_stats: list[RoundStats] = []

    for round_idx in range(config.num_generations):
        # ── collect rows that have a sample at this index ────────────
        round_items: list[tuple[RowState, str]] = []
        for row in rows:
            if round_idx < len(row.valid_samples):
                round_items.append((row, row.valid_samples[round_idx]))

        if not round_items:
            print(f"Round {round_idx}: no rows have a sample at this index — stopping")
            break

        stats = RoundStats(round_idx=round_idx, pending=len(round_items))
        print(f"\n{'='*60}")
        print(f"Round {round_idx}: {len(round_items)} rows")
        print(f"{'='*60}")

        # ── validate ─────────────────────────────────────────────────
        correctness_passed, _, _, _ = _validate_round(
            engine, round_items, config, stats,
        )

        for row, sample in correctness_passed:
            row.passed_samples.append(sample)
            if row.passed_round is None:
                row.passed_round = round_idx

        stats.newly_passed = len(correctness_passed)
        print(
            f"  Round {round_idx} summary: {stats.newly_passed} samples passed"
        )
        all_stats.append(stats)

    passed_rows = [row for row in rows if row.passed_samples]
    failed_rows = [row for row in rows if not row.passed_samples]
    return passed_rows, failed_rows, all_stats


# =============================================================================
# Stage 5: Write outputs
# =============================================================================

def _build_stats(
    passed_rows: list[RowState],
    remaining_rows: list[RowState],
    round_stats: list[RoundStats],
    total_seeds: int,
    rows_after_static: int,
    config: SDGConfig | None = None,
) -> dict:
    """Build a well-organized stats dict for stats.json."""
    static_failed = total_seeds - rows_after_static

    # Aggregate check totals across all rounds
    total_cycle_passed = sum(s.cycle_passed for s in round_stats)
    total_cycle_failed = sum(s.cycle_failed for s in round_stats)
    total_factual_passed = sum(s.factual_passed for s in round_stats)
    total_factual_failed = sum(s.factual_failed for s in round_stats)
    total_correctness_passed = sum(s.correctness_passed for s in round_stats)
    total_correctness_failed = sum(s.correctness_failed for s in round_stats)
    total_exhausted = sum(s.exhausted for s in round_stats)

    pass_rate = (len(passed_rows) / total_seeds * 100) if total_seeds else 0.0

    total_passed_samples = sum(len(r.passed_samples) for r in passed_rows)

    # Example-level failure breakdown: categorize each failed example by the
    # deepest validation stage it ever reached across all rounds/samples.
    #   best_stage 0 → never passed cycle (stuck at cycle)
    #   best_stage 1 → passed cycle but never factual
    #   best_stage 2 → passed factual but never correctness
    ex_failed_cycle = sum(1 for r in remaining_rows if r.best_stage == 0)
    ex_failed_factual = sum(1 for r in remaining_rows if r.best_stage == 1)
    ex_failed_correctness = sum(1 for r in remaining_rows if r.best_stage == 2)

    return {
        "summary": {
            "total_examples": total_seeds,
            "total_passed": len(passed_rows),
            "total_passed_samples": total_passed_samples,
            "total_failed": total_seeds - len(passed_rows),
            "pass_rate_pct": round(pass_rate, 2),
        },
        "checks": {
            "static_check": {
                "passed": rows_after_static,
                "failed": static_failed,
            },
            "cycle_consistency": {
                "passed": total_cycle_passed,
                "failed": total_cycle_failed,
            },
            "factual_accuracy": {
                "passed": total_factual_passed,
                "failed": total_factual_failed,
            },
            "correctness": {
                "passed": total_correctness_passed,
                "failed": total_correctness_failed,
            },
        },
        "failure_breakdown": {
            "failed_static_check": static_failed,
            "failed_cycle_consistency": total_cycle_failed,
            "failed_factual_accuracy": total_factual_failed,
            "failed_correctness": total_correctness_failed,
            "exhausted_samples": total_exhausted,
            "total_failed": total_seeds - len(passed_rows),
        },
        "failure_breakdown_examples": {
            "failed_static_check": static_failed,
            "failed_cycle_consistency": ex_failed_cycle,
            "failed_factual_accuracy": ex_failed_factual,
            "failed_correctness": ex_failed_correctness,
            "total_failed": total_seeds - len(passed_rows),
        },
        "rounds": [asdict(s) for s in round_stats],
    }


def write_outputs(
    passed_rows: list[RowState],
    remaining_rows: list[RowState],
    round_stats: list[RoundStats],
    total_seeds: int,
    rows_after_static: int,
    config: SDGConfig,
    output_path: Path | None = None,
) -> None:
    out = output_path if output_path is not None else config.output_path
    out.mkdir(parents=True, exist_ok=True)

    # ── output.jsonl ─────────────────────────────────────────────────
    jsonl_path = out / "output.jsonl"
    total_records = 0
    with open(jsonl_path, "w") as f:
        for row in passed_rows:
            if config.check_boxed:
                user_content = row.instruction_seed.strip() + "\n" + REASONING_INSTRUCTION + "\n"
            else:
                user_content = row.instruction_seed.strip() + "\n"
            if config.selection_policy == "all_valid" and row.passed_samples:
                samples = row.passed_samples
            else:
                samples = [row.passed_sample]
            for sample in samples:
                record = {
                    "messages": [
                        {"role": "user", "content": user_content},
                        {"role": "assistant", "content": sample},
                    ]
                }
                f.write(json.dumps(record, ensure_ascii=False) + "\n")
                total_records += 1
    print(f"Wrote {total_records} records ({len(passed_rows)} examples) to {jsonl_path}")

    # ── stats.json ───────────────────────────────────────────────────
    stats_path = out / "stats.json"
    stats_data = _build_stats(
        passed_rows, remaining_rows, round_stats,
        total_seeds, rows_after_static, config,
    )
    with open(stats_path, "w") as f:
        json.dump(stats_data, f, indent=2)
    print(f"Wrote stats to {stats_path}")

    # ── config.yaml copy ─────────────────────────────────────────────
    config.to_yaml(out / "config.yaml")
    print(f"Wrote config to {out / 'config.yaml'}")


# =============================================================================
# Public entry point
# =============================================================================

def run_pipeline(
    config: SDGConfig,
    shard_id: int | None = None,
    num_shards: int | None = None,
) -> None:
    """Run the full SDG pipeline end-to-end."""
    if config.selection_policy not in VALID_SELECTION_POLICIES:
        raise ValueError(
            f"Invalid selection_policy={config.selection_policy!r}, "
            f"must be one of {VALID_SELECTION_POLICIES}"
        )

    is_sharded = shard_id is not None and num_shards is not None

    print(f"Run name: {config.run_name}")
    print(f"Output:   {config.output_path}")
    if is_sharded:
        print(f"Shard:    {shard_id}/{num_shards}")

    # ── Per-shard logging via TeeStream ──────────────────────────────
    tee_out = None
    tee_err = None
    if is_sharded:
        log_dir = config.output_path / "logs"
        log_dir.mkdir(parents=True, exist_ok=True)
        log_path = log_dir / f"shard_{shard_id:03d}.log"
        tee_out = TeeStream(log_path, sys.stdout)
        tee_err = TeeStream(log_path, sys.stderr, share_file=tee_out.log_file)
        sys.stdout = tee_out
        sys.stderr = tee_err
        print(f"Logging to {log_path}")

    try:
        # Stage 1 — load data
        seeds = load_seeds(config)

        # ── Shard slicing ────────────────────────────────────────────
        if is_sharded:
            total = len(seeds)
            chunk_size = math.ceil(total / num_shards)
            start = shard_id * chunk_size
            end = min(start + chunk_size, total)
            seeds = seeds[start:end]
            print(f"Shard {shard_id}: seeds [{start}:{end}] ({len(seeds)} examples)")

        # Stage 2 — init engine
        engine = VLLMEngine(config)

        # Stage 3 — generate candidates + static check
        rows = generate_candidates(engine, seeds, config)

        # Stage 4 — iterative UQ (skip if validation_type is None)
        rows_after_static = len(rows)
        if config.validation_type is None:
            print(f"\nSkipping validation (validation_type=null)")
            for row in rows:
                if config.selection_policy == "all_valid":
                    row.passed_samples = list(row.valid_samples)
                else:
                    row.passed_sample = row.valid_samples[0]
                row.passed_round = 0
                row.best_stage = 3
            passed = rows
            remaining = []
            stats = []
        elif config.validation_type == "simple":
            if config.selection_policy == "all_valid":
                passed, remaining, stats = run_simple_rounds_all_valid(engine, rows, config)
            else:
                passed, remaining, stats = run_simple_rounds(engine, rows, config)
        elif config.selection_policy == "all_valid":
            passed, remaining, stats = run_uq_rounds_all_valid(engine, rows, config)
        else:
            passed, remaining, stats = run_uq_rounds(engine, rows, config)

        # Stage 5 — write output
        total_seeds = len(seeds)
        output_path = None
        if is_sharded:
            output_path = config.output_path / "shards" / f"shard_{shard_id:03d}"
        write_outputs(passed, remaining, stats, total_seeds, rows_after_static, config, output_path=output_path)

        # Final summary
        print(f"\n{'='*60}")
        print("PIPELINE COMPLETE")
        print(f"{'='*60}")
        print(f"Total seeds:   {total_seeds}")
        print(f"Total passed:  {len(passed)}")
        print(f"Total dropped: {total_seeds - len(passed)}")
        if total_seeds:
            print(f"Pass rate:     {len(passed) / total_seeds * 100:.1f}%")
        if output_path:
            print(f"Output dir:    {output_path}")
        else:
            print(f"Output dir:    {config.output_path}")
    finally:
        if tee_out is not None:
            sys.stdout = tee_out.original
            sys.stderr = tee_err.original
            tee_out.close()