import sys
import argparse
import codecs
import re
from pathlib import Path

import numpy as np
import soundfile as sf
# import tomli
from cached_path import cached_path

from f5_tts.model import DiT, UNetT
from f5_tts.model.utils_infer import (
	load_vocoder,
	load_model,
	preprocess_ref_audio_text,
	infer_process,
	remove_silence_for_generated_wav,
)

import os
import json
from time import time
from appPublic.dictObject import DictObject
from appPublic.folderUtils import temp_file
from appPublic.jsonConfig import getConfig
from appPublic.worker import awaitify
from ahserver.webapp import webapp
from ahserver.serverEnv import ServerEnv

n_mel_channels = 100
hop_length = 256
target_rms = 0.1
nfe_step = 32  # 16, 32
cfg_strength = 2.0
ode_method = "euler"
sway_sampling_coef = -1.0
speed = 1.0

class F5TTS:
	def __init__(self):
		self.config = getConfig()
		# self.vocos = load_vocoder(is_local=True, local_path="../checkpoints/charactr/vocos-mel-24khz")
		self.load_model()
		self.setup_voice()

	def load_model(self):
		# load models
		ckpt_file = ''
		if self.config.modelname == "F5-TTS":
			model_cls = DiT
			model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
			if ckpt_file == "":
				repo_name = "F5-TTS"
				exp_name = "F5TTS_Base"
				ckpt_step = 1200000
				ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))

		elif self.config.modelname == "E2-TTS":
			model_cls = UNetT
			model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
			if ckpt_file == "":
				repo_name = "E2-TTS"
				exp_name = "E2TTS_Base"
				ckpt_step = 1200000
				ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))

		self.model = load_model(model_cls, model_cfg, ckpt_file, 
						self.config.vocab_file)
		self.model = self.model.to(self.config.device)

	def setup_voice(self):
		main_voice = {"ref_audio": self.config.ref_audio_fn, 
					"ref_text": self.config.ref_text}
		if "voices" not in self.config:
			voices = {"main": main_voice}
		else:
			voices = self.config["voices"]
			voices["main"] = main_voice
		for voice in voices:
			voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
				voices[voice]["ref_audio"], voices[voice]["ref_text"]
			)
			print("Voice:", voice)
			print("Ref_audio:", voices[voice]["ref_audio"])
			print("Ref_text:", voices[voice]["ref_text"])
		self.voices = voices

	def _inference_stream(self, prompt):
		text_gen = prompt
		remove_silence = False
		generated_audio_segments = []
		reg1 = r"(?=\[\w+\])"
		chunks = re.split(reg1, text_gen)
		reg2 = r"\[(\w+)\]"
		for text in chunks:
			match = re.match(reg2, text)
			if match:
				voice = match[1]
			else:
				print("No voice tag found, using main.")
				voice = "main"
			if voice not in self.voices:
				print(f"Voice {voice} not found, using main.")
				voice = "main"
			text = re.sub(reg2, "", text)
			gen_text = text.strip()
			ref_audio = self.voices[voice]["ref_audio"]
			ref_text = self.voices[voice]["ref_text"]
			print(f"Voice: {voice}, {self.model}")
			audio, final_sample_rate, spectragram = \
					infer_process(ref_audio, ref_text, gen_text, self.model)
			yield {
				'audio':audio,
				'sample_rate':final_sample_rate,
				'spectragram':spectragram,
				'finish':False
			}
		yield {
			'finish':True
		}

	def _inference(self, prompt):
		generated_audio_segments = []
		remove_silence = self.config.remove_silence or False
		final_sample_rate = 24000
		for d in self._inference_stream(prompt):
			if not d['finish']:
				audio = d['audio'] 
				final_sample_rate = d['sample_rate']
				generated_audio_segments.append(audio)

		if generated_audio_segments:
			final_wave = np.concatenate(generated_audio_segments)
			fn = temp_file(suffix='.wav')
			with open(fn, "wb") as f:
				sf.write(f.name, final_wave, final_sample_rate)
				# Remove silence
				if remove_silence:
					remove_silence_for_generated_wav(f.name)
			return fn

def init():
	g = ServerEnv()
	f5 = F5TTS()
	g.infer_stream = awaitify(f5._inference_stream)
	g.infer = awaitify(f5._inference)

if __name__ == '__main__':
	webapp(init)