# -*- encoding: utf-8 -*-

import os.path
from pathlib import Path
from typing import List, Union, Tuple

import copy
import librosa
import numpy as np

from .utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError,
                          OrtInferSession, TokenIDConverter, get_logger,
                          read_yaml)
from .utils.postprocess_utils import sentence_postprocess
from .utils.frontend import WavFrontend
from .utils.timestamp_utils import time_stamp_lfr6_onnx
from .utils.e2e_vad import E2EVadModel

logging = get_logger()


class Fsmn_vad():
	def __init__(self, model_dir: Union[str, Path] = None,
	             batch_size: int = 1,
	             device_id: Union[str, int] = "-1",
	             quantize: bool = False,
	             intra_op_num_threads: int = 4,
	             max_end_sil: int = 800,
	             ):
		
		if not Path(model_dir).exists():
			raise FileNotFoundError(f'{model_dir} does not exist.')
		
		model_file = os.path.join(model_dir, 'model.onnx')
		if quantize:
			model_file = os.path.join(model_dir, 'model_quant.onnx')
		config_file = os.path.join(model_dir, 'vad.yaml')
		cmvn_file = os.path.join(model_dir, 'vad.mvn')
		config = read_yaml(config_file)
		
		self.frontend = WavFrontend(
			cmvn_file=cmvn_file,
			**config['frontend_conf']
		)
		self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
		self.batch_size = batch_size
		self.vad_scorer = E2EVadModel(**config)
		self.max_end_sil = max_end_sil
	
	def prepare_cache(self, in_cache: list = []):
		if len(in_cache) > 0:
			return in_cache
		
		for i in range(4):
			cache = np.random.rand(1, 128, 19, 1).astype(np.float32)
			in_cache.append(cache)
		return in_cache
		
	
	def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
		waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
		waveform_nums = len(waveform_list)
		is_final = kwargs.get('kwargs', False)

		asr_res = []
		for beg_idx in range(0, waveform_nums, self.batch_size):
			
			end_idx = min(waveform_nums, beg_idx + self.batch_size)
			waveform = waveform_list[beg_idx:end_idx]
			feats, feats_len = self.extract_feat(waveform)
			param_dict = kwargs.get('param_dict', dict())
			in_cache = param_dict.get('cache', list())
			in_cache = self.prepare_cache(in_cache)
			try:
				
				scores, out_caches = self.infer(feats, *in_cache)
				param_dict['cache'] = out_caches
				segments = self.vad_scorer(scores, waveform, is_final=is_final, max_end_sil=self.max_end_sil)
				
			except ONNXRuntimeError:
				# logging.warning(traceback.format_exc())
				logging.warning("input wav is silence or noise")
				segments = ''
			asr_res.append(segments)
			# else:
			# 	preds = self.decode(am_scores, valid_token_lens)
			#
			# 	asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens})
				
		return asr_res

	def load_data(self,
	              wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
		def load_wav(path: str) -> np.ndarray:
			waveform, _ = librosa.load(path, sr=fs)
			return waveform
		
		if isinstance(wav_content, np.ndarray):
			return [wav_content]
		
		if isinstance(wav_content, str):
			return [load_wav(wav_content)]
		
		if isinstance(wav_content, list):
			return [load_wav(path) for path in wav_content]
		
		raise TypeError(
			f'The type of {wav_content} is not in [str, np.ndarray, list]')
	
	def extract_feat(self,
	                 waveform_list: List[np.ndarray]
	                 ) -> Tuple[np.ndarray, np.ndarray]:
		feats, feats_len = [], []
		for waveform in waveform_list:
			speech, _ = self.frontend.fbank(waveform)
			feat, feat_len = self.frontend.lfr_cmvn(speech)
			feats.append(feat)
			feats_len.append(feat_len)
		
		feats = self.pad_feats(feats, np.max(feats_len))
		feats_len = np.array(feats_len).astype(np.int32)
		return feats, feats_len
	
	@staticmethod
	def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
		def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
			pad_width = ((0, max_feat_len - cur_len), (0, 0))
			return np.pad(feat, pad_width, 'constant', constant_values=0)
		
		feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
		feats = np.array(feat_res).astype(np.float32)
		return feats
	
	def infer(self, feats: np.ndarray,
	          feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
		outputs = self.ort_infer([feats, feats_len])
		return outputs
	
	def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:
		return [self.decode_one(am_score, token_num)
		        for am_score, token_num in zip(am_scores, token_nums)]
	
	def decode_one(self,
	               am_score: np.ndarray,
	               valid_token_num: int) -> List[str]:
		yseq = am_score.argmax(axis=-1)
		score = am_score.max(axis=-1)
		score = np.sum(score, axis=-1)
		
		# pad with mask tokens to ensure compatibility with sos/eos tokens
		# asr_model.sos:1  asr_model.eos:2
		yseq = np.array([1] + yseq.tolist() + [2])
		hyp = Hypothesis(yseq=yseq, score=score)
		
		# remove sos/eos and get results
		last_pos = -1
		token_int = hyp.yseq[1:last_pos].tolist()
		
		# remove blank symbol id, which is assumed to be 0
		token_int = list(filter(lambda x: x not in (0, 2), token_int))
		
		# Change integer-ids to tokens
		token = self.converter.ids2tokens(token_int)
		token = token[:valid_token_num - self.pred_bias]
		# texts = sentence_postprocess(token)
		return token