import os.path

import torch
import numpy as np
import hydra
import json
from omegaconf import DictConfig, OmegaConf
import logging
from funasr.download.download_from_hub import download_model
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_bytes
from funasr.train_utils.device_funcs import to_device
from tqdm import tqdm
from funasr.train_utils.load_pretrained_model import load_pretrained_model
import time
import random
import string
from funasr.utils.register import registry_tables


def build_iter_for_infer(data_in, input_len=None, data_type="sound"):
	"""
	
	:param input:
	:param input_len:
	:param data_type:
	:param frontend:
	:return:
	"""
	data_list = []
	key_list = []
	filelist = [".scp", ".txt", ".json", ".jsonl"]
	
	chars = string.ascii_letters + string.digits
	
	if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
		_, file_extension = os.path.splitext(data_in)
		file_extension = file_extension.lower()
		if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
			with open(data_in, encoding='utf-8') as fin:
				for line in fin:
					key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
					if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
						lines = json.loads(line.strip())
						data = lines["source"]
						key = data["key"] if "key" in data else key
					else: # filelist, wav.scp, text.txt: id \t data or data
						lines = line.strip().split()
						data = lines[1] if len(lines)>1 else lines[0]
						key = lines[0] if len(lines)>1 else key
					
					data_list.append(data)
					key_list.append(key)
		else:
			key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
			data_list = [data_in]
			key_list = [key]
	elif isinstance(data_in, (list, tuple)): # [audio sample point, fbank]
		data_list = data_in
		key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
	else: # raw text; audio sample point, fbank; bytes
		if isinstance(data_in, bytes): # audio bytes
			data_in = load_bytes(data_in)
		key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
		data_list = [data_in]
		key_list = [key]
	
	return key_list, data_list

@hydra.main(config_name=None, version_base=None)
def main_hydra(kwargs: DictConfig):
	log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())

	logging.basicConfig(level=log_level)

	import pdb;
	pdb.set_trace()
	model = AutoModel(**kwargs)
	res = model.generate(input=kwargs["input"])
	print(res)

class AutoModel:
	def __init__(self, **kwargs):
		registry_tables.print()
		assert "model" in kwargs
		if "model_conf" not in kwargs:
			logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
			kwargs = download_model(**kwargs)
		
		set_all_random_seed(kwargs.get("seed", 0))
		
		device = kwargs.get("device", "cuda")
		if not torch.cuda.is_available() or kwargs.get("ngpu", 1):
			device = "cpu"
			kwargs["batch_size"] = 1
		kwargs["device"] = device

		# build tokenizer
		tokenizer = kwargs.get("tokenizer", None)
		if tokenizer is not None:
			tokenizer_class = registry_tables.tokenizer_classes.get(tokenizer.lower())
			tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
			kwargs["tokenizer"] = tokenizer
		
		# build frontend
		frontend = kwargs.get("frontend", None)
		if frontend is not None:
			frontend_class = registry_tables.frontend_classes.get(frontend.lower())
			frontend = frontend_class(**kwargs["frontend_conf"])
			kwargs["frontend"] = frontend
			kwargs["input_size"] = frontend.output_size()
		
		# build model
		model_class = registry_tables.model_classes.get(kwargs["model"].lower())
		model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list))
		model.eval()
		model.to(device)
		
		kwargs["token_list"] = tokenizer.token_list
		
		# init_param
		init_param = kwargs.get("init_param", None)
		if init_param is not None:
			logging.info(f"Loading pretrained params from {init_param}")
			load_pretrained_model(
				model=model,
				init_param=init_param,
				ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
				oss_bucket=kwargs.get("oss_bucket", None),
			)
		self.kwargs = kwargs
		self.model = model
		self.tokenizer = tokenizer
	
	def generate(self, input, input_len=None, **cfg):
		self.kwargs.update(cfg)
		data_type = self.kwargs.get("data_type", "sound")
		batch_size = self.kwargs.get("batch_size", 1)
		if self.kwargs.get("device", "cpu") == "cpu":
			batch_size = 1
		
		key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
		
		speed_stats = {}
		asr_result_list = []
		num_samples = len(data_list)
		pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True)
		for beg_idx in range(0, num_samples, batch_size):
			end_idx = min(num_samples, beg_idx + batch_size)
			data_batch = data_list[beg_idx:end_idx]
			key_batch = key_list[beg_idx:end_idx]
			batch = {"data_in": data_batch, "key": key_batch}
			if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
				batch["data_batch"] = data_batch[0]
				batch["data_lengths"] = input_len
		
			time1 = time.perf_counter()
			results, meta_data = self.model.generate(**batch, **self.kwargs)
			time2 = time.perf_counter()
			
			asr_result_list.append(results)
			pbar.update(1)
			
			# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
			batch_data_time = meta_data.get("batch_data_time", -1)
			speed_stats["load_data"] = meta_data.get("load_data", 0.0)
			speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
			speed_stats["forward"] = f"{time2 - time1:0.3f}"
			speed_stats["rtf"] = f"{(time2 - time1) / batch_data_time:0.3f}"
			description = (
				f"{speed_stats}, "
			)
			pbar.set_description(description)
		
		torch.cuda.empty_cache()
		return asr_result_list

if __name__ == '__main__':
	main_hydra()