import numpy as np
import threading
import queue

def startDataWorker(dataset,encoding,batch_size,block_size,chat):
    data_q = queue.Queue(maxsize=100)
    t = threading.Thread(target=dataWorker, args=(data_q, dataset, encoding, batch_size, block_size,chat), daemon=True)
    t.start()
    while (1):
        try:
            bx, by = data_q.get(timeout=30)
        except queue.Empty:
            print("queue empty ...")
            continue
        yield (bx,by)

def dataWorker(q, dataset, encoding, batch_size, block_size,chat):
    batch_x, batch_y = [], []
    while True:
        for text in dataset:
            tokens = None
            if(chat):
                txt = f"<|user|>{text['instruction']}"
                if(text["input"] != None):
                    txt += f"\n{text['input']}"
                txt = txt + f"<|end|>\n<|assistant|>{text['output']}<|end|>"
                tokens = [encoding.bos_token_id]+encoding.encode(txt)
            else:
                tokens = [encoding.bos_token_id]+encoding.encode(text["text"])
            for i in range(0, len(tokens)-block_size+1,block_size):
                x = tokens[i:i+block_size]
                y = tokens[i+1:i+block_size+1]

                if len(x) < block_size:
                    pad = len(x)-(block_size-1)
                    x = x + [encoding.eos_token_id] + [encoding.pad_token_id] * pad

                if len(y) < block_size:
                    pad = len(y)-(block_size-1)
                    y = y + [encoding.eos_token_id] + [encoding.pad_token_id] * pad

                batch_x.append(x)
                batch_y.append(y)

                if len(batch_x) == batch_size:
                    q.put((np.array(batch_x, dtype=np.int32),
                           np.array(batch_y, dtype=np.int32)))
                    batch_x, batch_y = [], []