# /// script
# requires-python = ">=3.12"
# dependencies = [
#   "transformers",
#   "datasets",
#   "accelerate",
#   "soundfile",
#   "librosa",
#   "supabase",
#   "huggingface_hub",
#   "evaluate",
#   "jiwer",
#   "peft",
#   "optuna",
#   "ctranslate2",
#   "torchao>=0.16.0",
#   "pandas",
# ]
# ///
"""Optuna LoRA Search — Whisper Tiny

Downloads recordings for USER_ID from Supabase, runs Optuna hyperparameter
search (15 trials × 300 steps), retrains best config for 3000 steps on all
recordings, pushes checkpoints to Hub every 500 steps (resumable), converts
to CTranslate2 float16 (faster-whisper compatible), then marks model_versions
status='ready'.

Required env vars (injected by trigger-training via HF Jobs):
  SUPABASE_URL              Supabase project URL
  SUPABASE_SERVICE_ROLE_KEY service role key (bypasses RLS)
  USER_ID                   UUID of the user to train for
  HF_TOKEN                  HF write token
  HF_REPO_ID                target repo, e.g. "yourname/logos-voice-abc12345"
"""

import gc
import os
import re
import random
import shutil
import subprocess
import sys

os.environ["TQDM_DISABLE"] = "1"  # must be before tqdm-using library imports

subprocess.run(['apt-get', 'install', '-y', '-q', 'ffmpeg'], check=False)

import librosa
import optuna
import pandas as pd
import soundfile as sf
import torch
from datasets import Dataset
from dataclasses import dataclass
from typing import Any
from huggingface_hub import login, create_repo, list_repo_files, snapshot_download, upload_folder
from peft import LoraConfig, TaskType, get_peft_model
from supabase import create_client
from transformers import (
    Trainer,
    TrainerCallback,
    TrainingArguments,
    WhisperConfig,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)
import evaluate

# ── Config ────────────────────────────────────────────────────────────────────
SUPABASE_URL              = os.environ['SUPABASE_URL']
SUPABASE_SERVICE_ROLE_KEY = os.environ['SUPABASE_SERVICE_ROLE_KEY']
USER_ID                   = os.environ['USER_ID']
HF_TOKEN                  = os.environ['HF_TOKEN']
HF_REPO_ID                = os.environ['HF_REPO_ID']
MODEL_NAME                = 'openai/whisper-tiny'
WORK_DIR                  = '/tmp/logos_training'
TRIAL_STEPS               = 300
FINAL_STEPS               = 3000

os.makedirs(WORK_DIR, exist_ok=True)
sb = create_client(SUPABASE_URL, SUPABASE_SERVICE_ROLE_KEY)
login(token=HF_TOKEN)

# ── Find or create model_versions training row ────────────────────────────────
_existing = (
    sb.table('model_versions')
    .select('id, version')
    .eq('user_id', USER_ID)
    .eq('status', 'training')
    .limit(1)
    .execute()
)
if _existing.data:
    _mv_id      = _existing.data[0]['id']
    _mv_version = _existing.data[0]['version']
    print(f'Using existing model_versions row v{_mv_version}')
else:
    _latest = (
        sb.table('model_versions')
        .select('version')
        .eq('user_id', USER_ID)
        .order('version', desc=True)
        .limit(1)
        .execute()
    )
    _mv_version = ((_latest.data[0]['version']) if _latest.data else 0) + 1
    _result = (
        sb.table('model_versions')
        .insert({'user_id': USER_ID, 'version': _mv_version, 'status': 'training'})
        .execute()
    )
    _mv_id = _result.data[0]['id']
    print(f'Inserted model_versions row v{_mv_version}')

def _fail(msg: str):
    sb.table('model_versions').update({'status': 'failed'}).eq('id', _mv_id).execute()
    sys.exit(f'FATAL: {msg}')

# ── Download recordings from Supabase ─────────────────────────────────────────
response = (
    sb.table('training_recordings')
    .select('audio_url, phrase_id, training_phrases(text)')
    .eq('user_id', USER_ID)
    .execute()
)
print(f'Found {len(response.data)} recordings in Supabase')

all_recordings = []
for row in response.data:
    phrase_text = (row.get('training_phrases') or {}).get('text', '').strip()
    audio_url   = row['audio_url']
    phrase_id   = row['phrase_id']
    if not phrase_text or not audio_url:
        continue

    storage_path = None
    for marker in ('/object/public/training-audio/', '/object/authenticated/training-audio/'):
        idx = audio_url.find(marker)
        if idx != -1:
            storage_path = audio_url[idx + len(marker):]
            break
    if storage_path is None:
        print(f'  Skipping unexpected URL: {audio_url}')
        continue

    src_ext    = 'wav' if storage_path.lower().endswith('.wav') else 'webm'
    raw_path   = f'{WORK_DIR}/{phrase_id}_raw.{src_ext}'
    local_path = f'{WORK_DIR}/{phrase_id}.wav'

    try:
        raw_bytes = sb.storage.from_('training-audio').download(storage_path)
        with open(raw_path, 'wb') as f:
            f.write(raw_bytes)
        audio_array, _ = librosa.load(raw_path, sr=16000, mono=True)
        sf.write(local_path, audio_array, 16000)
        os.remove(raw_path)
        all_recordings.append({'phrase': phrase_text, 'audio_path': local_path})
    except Exception as e:
        print(f'  Failed {storage_path}: {e}')

print(f'Downloaded and converted {len(all_recordings)} recordings')
if len(all_recordings) < 100:
    _fail(f'Need at least 100 recordings, found {len(all_recordings)}')

# ── Fixed validation split ─────────────────────────────────────────────────────
random.seed(42)
shuffled         = all_recordings.copy()
random.shuffle(shuffled)
val_recordings   = shuffled[:50]
train_recordings = shuffled[50:]
print(f'Total: {len(all_recordings)}  Train: {len(train_recordings)}  Val: {len(val_recordings)}')

# ── Preprocess training data once (reused across all trials) ──────────────────
processor = WhisperProcessor.from_pretrained(MODEL_NAME)
processor.tokenizer.set_prefix_tokens(language='english', task='transcribe')

def preprocess(batch):
    audio_array, sr = librosa.load(batch['audio_path'], sr=16000, mono=True)
    batch['input_features'] = processor.feature_extractor(
        audio_array, sampling_rate=sr
    ).input_features[0]
    batch['labels'] = processor.tokenizer(batch['phrase']).input_ids
    return batch

train_dataset   = Dataset.from_pandas(pd.DataFrame(train_recordings))
train_processed = train_dataset.map(preprocess, remove_columns=train_dataset.column_names)
print('Train dataset preprocessed:', train_processed)

# ── Data collator ─────────────────────────────────────────────────────────────
@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
    processor: Any
    decoder_start_token_id: int

    def __call__(self, features):
        input_features = [{'input_features': f['input_features']} for f in features]
        batch = self.processor.feature_extractor.pad(input_features, return_tensors='pt')
        label_features = [{'input_ids': f['labels']} for f in features]
        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors='pt')
        labels = labels_batch['input_ids'].masked_fill(
            labels_batch.attention_mask.ne(1), -100
        )
        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
            labels = labels[:, 1:]
        batch['labels'] = labels
        return batch

_cfg = WhisperConfig.from_pretrained(MODEL_NAME)
data_collator = DataCollatorSpeechSeq2SeqWithPadding(
    processor=processor,
    decoder_start_token_id=_cfg.decoder_start_token_id,
)

# ── Evaluation ────────────────────────────────────────────────────────────────
wer_metric = evaluate.load('wer')

def score_on_my_recordings(model, verbose=False):
    model.eval()
    predictions, references = [], []
    with torch.no_grad():
        for rec in val_recordings:
            audio_array, sr = librosa.load(rec['audio_path'], sr=16000, mono=True)
            inputs = processor.feature_extractor(audio_array, sampling_rate=sr, return_tensors='pt')
            # Match the model's param dtype (LoRA training keeps weights in fp32 under
            # fp16=True autocast, so the model is NOT half) — force-casting inputs to
            # .half() crashes the encoder conv1d with a Half/float bias mismatch.
            input_features = inputs.input_features.to(device=model.device, dtype=next(model.parameters()).dtype)
            predicted_ids = model.generate(
                input_features=input_features,
                language='english',
                task='transcribe',
                max_new_tokens=128,
            )
            pred = processor.tokenizer.batch_decode(
                predicted_ids, skip_special_tokens=True
            )[0].strip().lower()
            references.append(rec['phrase'].lower())
            predictions.append(pred)
    wer      = wer_metric.compute(predictions=predictions, references=references)
    accuracy = max(0.0, 1.0 - wer)
    pairs    = list(zip(references, predictions))
    if verbose:
        for ref, pred in pairs:
            print(f'  [{"OK" if ref == pred else "XX"}] REF: {ref}')
            print(f'         PRD: {pred}')
    return accuracy, wer, pairs

# ── PEFT/Whisper compatibility patch ─────────────────────────────────────────
def _patch_whisper_peft(model):
    whisper = model.base_model.model
    def _forward(**kwargs):
        kwargs.pop('input_ids', None)
        return whisper(**kwargs)
    model.forward = _forward
    return model

# ── Progress callback ─────────────────────────────────────────────────────────
class StepProgressCallback(TrainerCallback):
    def on_step_end(self, args, state, control, **kwargs):
        if state.global_step % 25 == 0 and state.global_step > 0:
            print(f"  step {state.global_step}/{state.max_steps}", flush=True)

# ── Hub checkpoint callback (final training only) ─────────────────────────────
class HubCheckpointCallback(TrainerCallback):
    def on_save(self, args, state, control, **kwargs):
        ckpt_dir = f'{args.output_dir}/checkpoint-{state.global_step}'
        if not os.path.exists(ckpt_dir):
            return
        try:
            upload_folder(
                folder_path=ckpt_dir,
                repo_id=HF_REPO_ID,
                path_in_repo=f'checkpoint-{state.global_step}',
                repo_type='model',
                token=HF_TOKEN,
            )
            print(f'  [hub] pushed checkpoint-{state.global_step}', flush=True)
        except Exception as e:
            print(f'  [hub] checkpoint push failed: {e}', flush=True)

# ── Optuna search ─────────────────────────────────────────────────────────────
TARGET_MODULE_PRESETS = {
    'minimal':   ['q_proj', 'v_proj'],
    'attention': ['q_proj', 'k_proj', 'v_proj', 'out_proj'],
    'full':      ['q_proj', 'k_proj', 'v_proj', 'out_proj', 'fc1', 'fc2'],
}

def objective(trial):
    r             = trial.suggest_categorical('r', [8, 16, 32])
    lora_dropout  = trial.suggest_categorical('lora_dropout', [0.0, 0.05, 0.1])
    learning_rate = trial.suggest_float('learning_rate', 5e-5, 1e-3, log=True)  # tiny tolerates higher LR
    modules_key   = trial.suggest_categorical('target_modules', ['minimal', 'attention', 'full'])
    warmup_steps  = trial.suggest_categorical('warmup_steps', [0, 50, 100])
    weight_decay  = trial.suggest_categorical('weight_decay', [0.0, 0.01, 0.1])

    print(f'\n=== Trial {trial.number} | r={r} dropout={lora_dropout} '
          f'lr={learning_rate:.2e} modules={modules_key} warmup={warmup_steps} wd={weight_decay} ===')

    base = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME)
    base.config.suppress_tokens = None
    base.config.forced_decoder_ids = None
    base.config.use_cache = False

    peft_model = get_peft_model(base, LoraConfig(
        r=r, lora_alpha=r * 2,
        target_modules=TARGET_MODULE_PRESETS[modules_key],
        lora_dropout=lora_dropout, bias='none',
        task_type=TaskType.SEQ_2_SEQ_LM,
    ))
    peft_model.enable_input_require_grads()
    peft_model.print_trainable_parameters()
    _patch_whisper_peft(peft_model)

    trainer = Trainer(
        args=TrainingArguments(
            output_dir=f'{WORK_DIR}/trial_{trial.number}',
            per_device_train_batch_size=16,   # tiny fits 2x the batch
            gradient_accumulation_steps=2,
            learning_rate=learning_rate,
            weight_decay=weight_decay,
            warmup_steps=warmup_steps,
            max_steps=TRIAL_STEPS,
            gradient_checkpointing=True,
            fp16=True,
            eval_strategy='no',
            save_strategy='no',
            logging_steps=50,
            push_to_hub=False,
            remove_unused_columns=False,
            label_names=['labels'],
            report_to='none',
        ),
        model=peft_model,
        train_dataset=train_processed,
        data_collator=data_collator,
        callbacks=[StepProgressCallback()],
    )
    trainer.train()

    peft_model.base_model.model.config.use_cache = True
    accuracy, wer, samples = score_on_my_recordings(peft_model.cuda())
    print(f'Trial {trial.number} → accuracy={accuracy:.3f}  WER={wer:.3f}')
    for ref, pred in samples[:5]:
        print(f'  REF: {ref}\n  PRD: {pred}')

    del peft_model, trainer, base
    gc.collect()
    torch.cuda.empty_cache()
    return accuracy

optuna.logging.set_verbosity(optuna.logging.WARNING)
study = optuna.create_study(direction='maximize', study_name='whisper_tiny_lora_search')
study.optimize(objective, n_trials=15)

print(f'\n=== Search complete — best accuracy: {study.best_value:.3f} ===')
print(f'Best params: {study.best_params}')

results = []
for t in study.trials:
    if t.value is not None:
        row = {'trial': t.number, 'accuracy': round(t.value, 3), 'wer': round(1 - t.value, 3)}
        row.update(t.params)
        results.append(row)
print(pd.DataFrame(results).sort_values('accuracy', ascending=False).to_string(index=False))

# ── Final training on all recordings ─────────────────────────────────────────
best           = study.best_params
full_dataset   = Dataset.from_pandas(pd.DataFrame(all_recordings))
full_processed = full_dataset.map(preprocess, remove_columns=full_dataset.column_names)
print('Full dataset preprocessed:', full_processed)
print(f'Training best config for {FINAL_STEPS} steps on all {len(all_recordings)} recordings...')
print(f'Best params: {best}')

# Ensure repo exists before checkpoint callback tries to push
create_repo(HF_REPO_ID, repo_type='model', private=True, exist_ok=True, token=HF_TOKEN)

# Check Hub for a resumable checkpoint from a previous interrupted run
def _find_latest_hub_checkpoint():
    try:
        files = list(list_repo_files(HF_REPO_ID, repo_type='model', token=HF_TOKEN))
        steps = [int(m.group(1)) for f in files if (m := re.match(r'checkpoint-(\d+)/', f))]
        return max(steps) if steps else None
    except Exception:
        return None

_latest_step = _find_latest_hub_checkpoint()
resume_path  = None
if _latest_step:
    print(f'Found Hub checkpoint at step {_latest_step}, downloading...')
    snapshot_download(
        repo_id=HF_REPO_ID,
        allow_patterns=f'checkpoint-{_latest_step}/**',
        local_dir=f'{WORK_DIR}/final',
        repo_type='model',
        token=HF_TOKEN,
    )
    resume_path = f'{WORK_DIR}/final/checkpoint-{_latest_step}'
    print(f'Resuming from step {_latest_step}')
else:
    print('No Hub checkpoint found, starting from scratch')

best_base = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME)
best_base.config.suppress_tokens = None
best_base.config.forced_decoder_ids = None
best_base.config.use_cache = False

best_model = get_peft_model(best_base, LoraConfig(
    r=best['r'], lora_alpha=best['r'] * 2,
    target_modules=TARGET_MODULE_PRESETS[best['target_modules']],
    lora_dropout=best['lora_dropout'], bias='none',
    task_type=TaskType.SEQ_2_SEQ_LM,
))
best_model.enable_input_require_grads()
best_model.print_trainable_parameters()
_patch_whisper_peft(best_model)

Trainer(
    args=TrainingArguments(
        output_dir=f'{WORK_DIR}/final',
        per_device_train_batch_size=16,   # tiny fits 2x the batch
        gradient_accumulation_steps=2,
        learning_rate=best['learning_rate'],
        warmup_steps=best['warmup_steps'],
        max_steps=FINAL_STEPS,
        gradient_checkpointing=True,
        fp16=True,
        eval_strategy='no',
        save_strategy='steps',
        save_steps=500,
        logging_steps=50,
        push_to_hub=False,
        remove_unused_columns=False,
        label_names=['labels'],
        report_to='none',
    ),
    model=best_model,
    train_dataset=full_processed,
    data_collator=data_collator,
    callbacks=[StepProgressCallback(), HubCheckpointCallback()],
).train(resume_from_checkpoint=resume_path)

best_model.base_model.model.config.use_cache = True
accuracy, wer, _ = score_on_my_recordings(best_model.cuda(), verbose=True)
print(f'\nFinal model → accuracy={accuracy:.3f}  WER={wer:.3f}')

# ── Merge LoRA ────────────────────────────────────────────────────────────────
print('Merging LoRA weights...')
merged_path  = f'{WORK_DIR}/merged'
final_merged = best_model.merge_and_unload()
final_merged.save_pretrained(merged_path)
processor.save_pretrained(merged_path)
print(f'Saved merged model to {merged_path}')

del best_model, final_merged
gc.collect()
torch.cuda.empty_cache()

# ── Convert to CTranslate2 (faster-whisper) ───────────────────────────────────
ct2_path = f'{WORK_DIR}/ct2'
print('Converting to CTranslate2 float16 (faster-whisper compatible)...')
subprocess.run([
    'ct2-transformers-converter',
    '--model', merged_path,
    '--output_dir', ct2_path,
    '--quantization', 'float16',
    '--force',
], check=True)

for fname in os.listdir(merged_path):
    if fname.endswith('.json') and fname != 'config.json':
        src = os.path.join(merged_path, fname)
        dst = os.path.join(ct2_path, fname)
        if not os.path.exists(dst):
            shutil.copy2(src, dst)
print('CTranslate2 conversion complete')

# ── Push CT2 model to Hub ─────────────────────────────────────────────────────
print(f'Pushing faster-whisper model to {HF_REPO_ID}...')
try:
    upload_folder(
        folder_path=ct2_path,
        repo_id=HF_REPO_ID,
        repo_type='model',
        token=HF_TOKEN,
        ignore_patterns=['checkpoint-*/**'],
    )
    print(f'Model live at https://huggingface.co/{HF_REPO_ID}')
except Exception as e:
    _fail(f'push_to_hub failed: {e}')

# ── Mark model ready in Supabase ──────────────────────────────────────────────
sb.table('model_versions').update({
    'status':                 'ready',
    'avg_accuracy':           round(accuracy * 100, 2),
    'training_samples_count': len(all_recordings),
    'model_metadata': {
        'hf_repo_id':         HF_REPO_ID,
        'base_model':         MODEL_NAME,
        'search_best_params': best,
        'search_best_accuracy': round(study.best_value, 3),
        'final_wer':          round(wer, 4),
        'n_trials':           15,
        'trial_steps':        TRIAL_STEPS,
        'final_steps':        FINAL_STEPS,
    },
}).eq('id', _mv_id).execute()

print(f'model_versions v{_mv_version} → ready  (accuracy={accuracy:.3f}  repo={HF_REPO_ID})')