import os
import json
import torch
import numpy as np

from tqdm.auto import tqdm
from pathlib import Path

from vall_e.config import cfg

# things that could be args
cfg.sample_rate = 24_000
cfg.inference.audio_backend = "encodec"
"""
cfg.inference.weight_dtype = "bfloat16"
cfg.inference.dtype = torch.bfloat16
cfg.inference.amp = True
"""

from vall_e.emb.g2p import encode as valle_phonemize
from vall_e.emb.qnt import encode_from_file as valle_quantize, _replace_file_extension

audio_extension = ".dac" if cfg.inference.audio_backend == "dac" else ".enc"

input_dataset = "LibriTTS_R"
output_dataset = f"LibriTTS-Train-{'2' if cfg.sample_rate == 24_000 else '4'}4KHz"
device = "cuda"

txts = []
wavs = []

for dataset_name in os.listdir(f'./{input_dataset}/'):
	if not os.path.isdir(f'./{input_dataset}/{dataset_name}/'):
		continue

	for speaker_id in tqdm(os.listdir(f'./{input_dataset}/{dataset_name}/'), desc="Processing speaker"):
		if not os.path.isdir(f'./{input_dataset}/{dataset_name}/{speaker_id}'):
			continue
		
		os.makedirs(f'./{output_dataset}/{speaker_id}/', exist_ok=True)
		for book_id in os.listdir(f'./{input_dataset}/{dataset_name}/{speaker_id}'):
			if not os.path.isdir(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}'):
				continue
			for filename in os.listdir(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}'):
				# os.rename(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}/{filename}', f'./{output_dataset}/{speaker_id}/{filename}')

				inpath = Path(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}/{filename}')
				outpath = Path(f'./{output_dataset}/{speaker_id}/{filename}')
				
				if ".wav" in filename: # and not _replace_file_extension(outpath, ".dac").exists():
					txts.append((
						inpath,
						outpath
					))

for paths in tqdm(txts, desc="Processing..."):
	inpath, outpath = paths
	try:
		if _replace_file_extension(outpath, ".dac").exists() and _replace_file_extension(outpath, ".json").exists():
			data = json.loads(open(_replace_file_extension(outpath, ".json"), 'r', encoding='utf-8').read())
			qnt = np.load(_replace_file_extension(outpath, audio_extension), allow_pickle=True)
			
			if not isinstance(data["phonemes"], str):
				data["phonemes"] = "".join(data["phonemes"])

			for k, v in data.items():
				qnt[()]['metadata'][k] = v

			np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), qnt)
		else:
			text = open(_replace_file_extension(inpath, ".original.txt"), "r", encoding="utf-8").read()
			
			phones = valle_phonemize(text)
			qnt = valle_quantize(_replace_file_extension(inpath, ".wav"), device=device)

			if cfg.inference.audio_backend == "dac":
				np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
					"codes": qnt.codes.cpu().numpy().astype(np.uint16),
					"metadata": {
						"original_length": qnt.original_length,
						"sample_rate": qnt.sample_rate,
						
						"input_db": qnt.input_db.cpu().numpy().astype(np.float32),
						"chunk_length": qnt.chunk_length,
						"channels": qnt.channels,
						"padding": qnt.padding,
						"dac_version": "1.0.0",

						"text": text.strip(),
						"phonemes": "".join(phones),
						"language": "en",
					},
				})
			else:
				np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
					"codes": qnt.cpu().numpy().astype(np.uint16),
					"metadata": {
						"original_length": qnt.shape[0] / 75.0,
						"sample_rate": cfg.sample_rate,

						"text": text.strip(),
						"phonemes": "".join(phones),
						"language": "en",
					},
				})
	except Exception as e:
		tqdm.write(f"Failed to process: {paths}: {e}")