import datetime
import os
import re

import torch

from TTS.speaker_encoder.model import SpeakerEncoder
from TTS.utils.generic_utils import check_argument


def to_camel(text):
    text = text.capitalize()
    return re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text)


def setup_model(c):
    model = SpeakerEncoder(c.model["input_dim"], c.model["proj_dim"], c.model["lstm_dim"], c.model["num_lstm_layers"])
    return model


def save_checkpoint(model, optimizer, model_loss, out_path, current_step, epoch):
    checkpoint_path = "checkpoint_{}.pth.tar".format(current_step)
    checkpoint_path = os.path.join(out_path, checkpoint_path)
    print(" | | > Checkpoint saving : {}".format(checkpoint_path))

    new_state_dict = model.state_dict()
    state = {
        "model": new_state_dict,
        "optimizer": optimizer.state_dict() if optimizer is not None else None,
        "step": current_step,
        "epoch": epoch,
        "loss": model_loss,
        "date": datetime.date.today().strftime("%B %d, %Y"),
    }
    torch.save(state, checkpoint_path)


def save_best_model(model, optimizer, model_loss, best_loss, out_path, current_step):
    if model_loss < best_loss:
        new_state_dict = model.state_dict()
        state = {
            "model": new_state_dict,
            "optimizer": optimizer.state_dict(),
            "step": current_step,
            "loss": model_loss,
            "date": datetime.date.today().strftime("%B %d, %Y"),
        }
        best_loss = model_loss
        bestmodel_path = "best_model.pth.tar"
        bestmodel_path = os.path.join(out_path, bestmodel_path)
        print("\n > BEST MODEL ({0:.5f}) : {1:}".format(model_loss, bestmodel_path))
        torch.save(state, bestmodel_path)
    return best_loss


def check_config_speaker_encoder(c):
    """Check the config.json file of the speaker encoder"""
    check_argument("run_name", c, restricted=True, val_type=str)
    check_argument("run_description", c, val_type=str)

    # audio processing parameters
    check_argument("audio", c, restricted=True, val_type=dict)
    check_argument("num_mels", c["audio"], restricted=True, val_type=int, min_val=10, max_val=2056)
    check_argument("fft_size", c["audio"], restricted=True, val_type=int, min_val=128, max_val=4058)
    check_argument("sample_rate", c["audio"], restricted=True, val_type=int, min_val=512, max_val=100000)
    check_argument(
        "frame_length_ms",
        c["audio"],
        restricted=True,
        val_type=float,
        min_val=10,
        max_val=1000,
        alternative="win_length",
    )
    check_argument(
        "frame_shift_ms", c["audio"], restricted=True, val_type=float, min_val=1, max_val=1000, alternative="hop_length"
    )
    check_argument("preemphasis", c["audio"], restricted=True, val_type=float, min_val=0, max_val=1)
    check_argument("min_level_db", c["audio"], restricted=True, val_type=int, min_val=-1000, max_val=10)
    check_argument("ref_level_db", c["audio"], restricted=True, val_type=int, min_val=0, max_val=1000)
    check_argument("power", c["audio"], restricted=True, val_type=float, min_val=1, max_val=5)
    check_argument("griffin_lim_iters", c["audio"], restricted=True, val_type=int, min_val=10, max_val=1000)

    # training parameters
    check_argument("loss", c, enum_list=["ge2e", "angleproto"], restricted=True, val_type=str)
    check_argument("grad_clip", c, restricted=True, val_type=float)
    check_argument("epochs", c, restricted=True, val_type=int, min_val=1)
    check_argument("lr", c, restricted=True, val_type=float, min_val=0)
    check_argument("lr_decay", c, restricted=True, val_type=bool)
    check_argument("warmup_steps", c, restricted=True, val_type=int, min_val=0)
    check_argument("tb_model_param_stats", c, restricted=True, val_type=bool)
    check_argument("num_speakers_in_batch", c, restricted=True, val_type=int)
    check_argument("num_loader_workers", c, restricted=True, val_type=int)
    check_argument("wd", c, restricted=True, val_type=float, min_val=0.0, max_val=1.0)

    # checkpoint and output parameters
    check_argument("steps_plot_stats", c, restricted=True, val_type=int)
    check_argument("checkpoint", c, restricted=True, val_type=bool)
    check_argument("save_step", c, restricted=True, val_type=int)
    check_argument("print_step", c, restricted=True, val_type=int)
    check_argument("output_path", c, restricted=True, val_type=str)

    # model parameters
    check_argument("model", c, restricted=True, val_type=dict)
    check_argument("input_dim", c["model"], restricted=True, val_type=int)
    check_argument("proj_dim", c["model"], restricted=True, val_type=int)
    check_argument("lstm_dim", c["model"], restricted=True, val_type=int)
    check_argument("num_lstm_layers", c["model"], restricted=True, val_type=int)
    check_argument("use_lstm_with_projection", c["model"], restricted=True, val_type=bool)

    # in-memory storage parameters
    check_argument("storage", c, restricted=True, val_type=dict)
    check_argument("sample_from_storage_p", c["storage"], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
    check_argument("storage_size", c["storage"], restricted=True, val_type=int, min_val=1, max_val=100)
    check_argument("additive_noise", c["storage"], restricted=True, val_type=float, min_val=0.0, max_val=1.0)

    # datasets - checking only the first entry
    check_argument("datasets", c, restricted=True, val_type=list)
    for dataset_entry in c["datasets"]:
        check_argument("name", dataset_entry, restricted=True, val_type=str)
        check_argument("path", dataset_entry, restricted=True, val_type=str)
        check_argument("meta_file_train", dataset_entry, restricted=True, val_type=[str, list])
        check_argument("meta_file_val", dataset_entry, restricted=True, val_type=str)