import os, subprocess, sys, zipfile, csv

os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1"
os.environ["HF_HUB_DISABLE_HF_TRANSFER"] = "1"
os.environ["HF_HUB_ENABLE_XET"] = "0"

os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
os.environ["NUMBA_CACHE_DIR"] = "/tmp/numba_cache"
os.environ["HF_HOME"] = "/tmp/hf_home"
os.environ["HF_HUB_CACHE"] = "/tmp/hf_cache"
os.environ["CHECKPOINTS_OUT_PATH"] = "/tmp/xtts_checkpoints"
os.environ["OUT_PATH"] = "/tmp/output_model"
os.makedirs("/tmp/numba_cache", exist_ok=True)
os.makedirs("/tmp/hf_cache", exist_ok=True)
os.makedirs("/tmp/hf_home", exist_ok=True)
os.makedirs("/tmp/xtts_checkpoints", exist_ok=True)
os.environ["NUMBA_DISABLE_JIT"] = "1"

from huggingface_hub import HfApi, HfFolder, upload_folder, snapshot_download

# 🔒 Eliminar hf_transfer si está presente
subprocess.run([sys.executable, "-m", "pip", "uninstall", "-y", "hf_transfer"])

# === Configuración ===
HF_MODEL_ID = "sob111/xtts-v2-finetuned"   # <--- cambia con tu repo en HF
HF_TOKEN = os.environ.get("HF_TOKEN")          # Debe estar definido en tu Space/entorno
DATASET_PATH = "/tmp/dataset"        # Ruta a tu dataset
VOXPOPULI_PATH = "/tmp/dataset/voxpopuli_es_500_vctk"        # Ruta a tu dataset
OUTPUT_PATH = "/tmp/output_model"
BASE_MODEL = "coqui/XTTS-v2"

os.makedirs("/tmp/xtts_cache", exist_ok=True)
os.chmod("/tmp/xtts_cache", 0o777)

os.makedirs("/tmp/xtts_model", exist_ok=True)
os.chmod("/tmp/xtts_model", 0o777)

os.makedirs("/tmp/xtts_model/.huggingface", exist_ok=True)
os.chmod("/tmp/xtts_model/.huggingface", 0o777)

os.makedirs(OUTPUT_PATH, exist_ok=True)
os.chmod(OUTPUT_PATH, 0o777)

# Solución matplotlib
os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
os.makedirs("/tmp/matplotlib", exist_ok=True)

# Continúa con tu lógica, usando las nuevas rutas de manera consistent

# 🔧 Forzar descarga sin symlinks ni hf_transfer
model_dir = snapshot_download(
    repo_id="coqui/XTTS-v2",
    local_dir="/tmp/xtts_model",   # descarga directa aquí
    cache_dir="/tmp/hf_cache",     # cache seguro en /tmp
    #local_dir_use_symlinks=False,  # 🔑 evita enlaces simbólicos
    resume_download=True,
    token=HF_TOKEN
)

print(f"✅ Modelo descargado en: {model_dir}")

CONFIG_PATH = "/tmp/xtts_model/config.json"
RESTORE_PATH = "/tmp/xtts_model/model.pth"

# === 1.B Extraer el dataset
def extract_zip(zip_file_path, destination_path):
    """
    Extracts the contents of a ZIP file to a specified directory.
    
    Args:
        zip_file_path (str): The full path to the ZIP file.
        destination_path (str): The directory where the contents will be extracted.
    """
    # Create the destination directory if it doesn't exist
    os.makedirs(destination_path, exist_ok=True)
    
    try:
        # Open the ZIP file in read mode
        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
            # Extract all the contents to the specified directory
            zip_ref.extractall(destination_path)
        print(f"✅ Extracted '{zip_file_path}' to '{destination_path}' successfully.")
    except zipfile.BadZipFile:
        print(f"❌ Error: The file '{zip_file_path}' is not a valid ZIP file.")
    except FileNotFoundError:
        print(f"❌ Error: The file '{zip_file_path}' was not found.")
    except Exception as e:
        print(f"❌ An unexpected error occurred: {e}")

# Example usage:
zip_file = "/home/user/app/voxpopuli_es_500_vctk.zip"

# To protect against security vulnerabilities, it is important to sanitize the destination path.
# This prevents an attacker from using a malicious ZIP file to write outside the destination folder.
safe_destination = os.path.abspath(DATASET_PATH)

# Call the function
extract_zip(zip_file, DATASET_PATH)
print(f"safe destination {safe_destination}")

# === 2. Editar configuración para tu dataset VoxPopuli ===

import json

# === Convertir metadata.json → metadata.csv ===
#json_path = os.path.join(VOXPOPULI_PATH, "metadata.json")
#print(f"ruta de json {json_path}")
#csv_path = os.path.join(VOXPOPULI_PATH, "metadata.csv")

#if os.path.exists(json_path):
#    print("🔄 Convirtiendo metadata.json → metadata.csv...")
#    with open(json_path, "r", encoding="utf-8") as f:
#        data = json.load(f)

#    with open(csv_path, "w", encoding="utf-8", newline="") as f:
#        writer = csv.writer(f, delimiter=",", quoting=csv.QUOTE_MINIMAL)
        
#        for entry in data:
#            path = entry["audio_filepath"]
            # Quitar prefijo "voxpopuli_es_500/" si existe
#            if path.startswith("voxpopuli_es_500/"):
#                path = path.replace("voxpopuli_es_500/", "", 1)
#            text = entry["text"].replace("\n", " ").strip()
#            speaker = entry.get("speaker", "spk1")
#            writer.writerow([path, text, speaker])
#    print(f"✅ metadata.csv generado en {csv_path}")
#else:
#    raise FileNotFoundError(f"❌ No se encontró {json_path}. Verifica el zip.")

from TTS.tts.datasets import load_tts_samples
from TTS.config.shared_configs import BaseDatasetConfig

# Configuración idéntica a tu dataset
config_dataset = BaseDatasetConfig(
    formatter="vctk",
    dataset_name="voxpopuli",
    path="/tmp/dataset",
    language="es",
)
if os.path.exists("/tmp/dataset"):
    print("/tmp/dataset encontrado")
if os.path.exists("/tmp/dataset/wav48"):
    print("/tmp/dataset/wav48 encontrado")
if os.path.exists("/tmp/dataset/voxpopuli_es_500_vctk"):
    print("/tmp/dataset/voxpopuli_es_500_vctk encontrado")
if os.path.exists("/tmp/dataset/voxpopuli_es_500_vctk/wav48"):
    print("/tmp/dataset/voxpopuli_es_500_vctk/wav48 encontrado")

#train_samples, eval_samples = load_tts_samples(
#    "/tmp/dataset","vctk"
#)
# Construimos rutas completas
#root_path = config_dataset.path
#meta_file_train = config_dataset.meta_file_train

#meta_path = os.path.join(root_path, meta_file_train)
#print(f"Verificando archivo CSV: {meta_path}")
#print(f"Existe?: {os.path.exists(meta_path)}")

# Intentamos cargar los samples
#try:
#    train_samples, eval_samples = load_tts_samples(config_dataset)
    
#    print(f"Samples detectados: {len(train_samples)} training, {len(eval_samples)} eval")
#    print("Primeros 3 samples:")
#    for s in train_samples[:3]:
#        print(s)
#except AssertionError as e:
#    print("❌ Error cargando samples:", e)

print("=== Editando configuración para fine-tuning con VoxPopuli ===")

with open(CONFIG_PATH, "r") as f:
    config = json.load(f)

config["output_path"] = OUTPUT_PATH
config["datasets"] = [
    {
        "formatter": "ljspeech",
        "path": VOXPOPULI_PATH,
        "meta_file_train": "metadata.csv"
    }
]
config["run_name"] = "xtts-finetune-voxpopuli"
config["lr"] = 1e-5  # más bajo para fine-tuning

with open(CONFIG_PATH, "w") as f:
    json.dump(config, f, indent=2)

# === 3. Lanzar entrenamiento ===
print("=== Iniciando fine-tuning de XTTS-v2 ===")

import librosa
from librosa.core import spectrum

subprocess.run([
   "python", "/home/user/app/train_gpt_xtts.py",
#   "--config_path", CONFIG_PATH,
#   "--restore_path", RESTORE_PATH
], check=True)

# subprocess.run([
#    "python", "-m", "TTS.bin.train",
#    "--config_path", CONFIG_PATH,
#    "--restore_path", RESTORE_PATH
# ], check=True)

# === 4. Subir modelo resultante a HF ===
print("=== Subiendo modelo fine-tuneado a Hugging Face Hub ===")
api = HfApi()
HfFolder.save_token(HF_TOKEN)

api.create_repo(repo_id="sob111/xtts-v2-finetuned", repo_type="model", private=False)  
upload_folder(
    repo_id=HF_MODEL_ID,
    repo_type="model",
    folder_path=OUTPUT_PATH,
    token=HF_TOKEN
)

print("✅ Fine-tuning completado y modelo subido a Hugging Face.")