import glob
import json
import os
import shutil

from trainer import get_last_checkpoint

from tests import get_device_id, get_tests_output_path, run_cli
from TTS.config.shared_configs import BaseAudioConfig
from TTS.tts.configs.fastspeech2_config import Fastspeech2Config

config_path = os.path.join(get_tests_output_path(), "fast_pitch_speaker_emb_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")

audio_config = BaseAudioConfig(
    sample_rate=22050,
    do_trim_silence=True,
    trim_db=60.0,
    signal_norm=False,
    mel_fmin=0.0,
    mel_fmax=8000,
    spec_gain=1.0,
    log_func="np.log",
    ref_level_db=20,
    preemphasis=0.0,
)

config = Fastspeech2Config(
    audio=audio_config,
    batch_size=8,
    eval_batch_size=8,
    num_loader_workers=0,
    num_eval_loader_workers=0,
    text_cleaner="english_cleaners",
    use_phonemes=True,
    phoneme_language="en-us",
    phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
    f0_cache_path="tests/data/ljspeech/f0_cache/",
    compute_f0=True,
    compute_energy=True,
    energy_cache_path="tests/data/ljspeech/energy_cache/",
    run_eval=True,
    test_delay_epochs=-1,
    epochs=1,
    print_step=1,
    print_eval=True,
    use_speaker_embedding=True,
    test_sentences=[
        "Be a voice, not an echo.",
    ],
)
config.audio.do_trim_silence = True
config.use_speaker_embedding = True
config.model_args.use_speaker_embedding = True
config.audio.trim_db = 60
config.save_json(config_path)

# train the model for one epoch
command_train = (
    f"CUDA_VISIBLE_DEVICES='{get_device_id()}'  python TTS/bin/train_tts.py --config_path {config_path}  "
    f"--coqpit.output_path {output_path} "
    "--coqpit.datasets.0.formatter ljspeech_test "
    "--coqpit.datasets.0.meta_file_train metadata.csv "
    "--coqpit.datasets.0.meta_file_val metadata.csv "
    "--coqpit.datasets.0.path tests/data/ljspeech "
    "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
    "--coqpit.test_delay_epochs 0"
)
run_cli(command_train)

# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)

# Inference using TTS API
continue_config_path = os.path.join(continue_path, "config.json")
continue_restore_path, _ = get_last_checkpoint(continue_path)
out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
speaker_id = "ljspeech-1"
continue_speakers_path = os.path.join(continue_path, "speakers.json")

# Check integrity of the config
with open(continue_config_path, "r", encoding="utf-8") as f:
    config_loaded = json.load(f)
assert config_loaded["characters"] is not None
assert config_loaded["output_path"] in continue_path
assert config_loaded["test_delay_epochs"] == 0

# Load the model and run inference
inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
run_cli(inference_command)

# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)