import gc
import traceback
from queue import Queue
from threading import Thread

import torch
import transformers
from transformers import is_torch_xpu_available

import modules.shared as shared


class _StopEverythingStoppingCriteria(transformers.StoppingCriteria):
    def __init__(self):
        transformers.StoppingCriteria.__init__(self)

    def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool:
        return shared.stop_everything


class Stream(transformers.StoppingCriteria):
    def __init__(self, callback_func=None):
        self.callback_func = callback_func

    def __call__(self, input_ids, scores) -> bool:
        if self.callback_func is not None:
            self.callback_func(input_ids[0])

        return False


class Iteratorize:

    """
    Transforms a function that takes a callback
    into a lazy iterator (generator).

    Adapted from: https://stackoverflow.com/a/9969000
    """

    def __init__(self, func, args=None, kwargs=None, callback=None):
        self.mfunc = func
        self.c_callback = callback
        self.q = Queue()
        self.sentinel = object()
        self.args = args or []
        self.kwargs = kwargs or {}
        self.stop_now = False

        def _callback(val):
            if self.stop_now or shared.stop_everything:
                raise ValueError
            self.q.put(val)

        def gentask():
            try:
                ret = self.mfunc(callback=_callback, *args, **self.kwargs)
            except ValueError:
                pass
            except:
                traceback.print_exc()
                pass

            clear_torch_cache()
            self.q.put(self.sentinel)
            if self.c_callback:
                self.c_callback(ret)

        self.thread = Thread(target=gentask)
        self.thread.start()

    def __iter__(self):
        return self

    def __next__(self):
        obj = self.q.get(True, None)
        if obj is self.sentinel:
            raise StopIteration
        else:
            return obj

    def __del__(self):
        clear_torch_cache()

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.stop_now = True
        clear_torch_cache()


def clear_torch_cache():
    gc.collect()
    if not shared.args.cpu:
        if is_torch_xpu_available():
            torch.xpu.empty_cache()
        else:
            torch.cuda.empty_cache()
