#!/usr/bin/env python
# -*- encoding: utf-8 -*-

import torch
from .base_grad_scaler import BaseGradScaler

__all__ = ['DynamicGradScaler']


class DynamicGradScaler(BaseGradScaler):

    def __init__(self,
                 initial_scale: int = 2**16,
                 growth_factor: int = 2,
                 backoff_factor: float = 0.5,
                 growth_interval: int = 1000,
                 min_scale: int = None,
                 max_scale: int = None,
                 hysteresis: int = None,
                 verbose: bool = False):
        super().__init__(initial_scale, verbose)
        self._min_scale = min_scale
        self._max_scale = max_scale
        self._growth_factor = growth_factor
        self._backoff_factor = backoff_factor
        self._growth_interval = growth_interval
        self._growth_step = 0
        self._hysteresis = hysteresis
        self._hysteresis_step = 0
        self._sanity_checks()

    def _sanity_checks(self) -> None:
        if self._min_scale:
            assert self._min_scale > 0, 'The minimum gradient scale cannot be zero or negative'
        if self._max_scale:
            assert self._min_scale > 0, 'The maximum gradient scale cannot be zero or negative'
        assert self._growth_factor > 1, 'The growth factor cannot be equal or smaller than 1'
        assert self._backoff_factor < 1 and self._backoff_factor > 0, 'The backoff factor must be between 0 and 1'
        assert self._hysteresis >= 0, 'The hysteresis cannot be negative'

    def update(self, overflow: bool) -> None:
        if overflow:
            self._hysteresis_step += 1
            self._growth_step = 0

            if self._hysteresis_step >= self._hysteresis:
                self._backoff_scale()
                self.log(f"Overflow occurs, the loss scale is adjusted to {self.scale.item()}", ranks=[0])
        else:
            self._growth_step += 1
            if self._growth_step == self._growth_interval:
                self._growth_step = 0
                self._hysteresis_step = 0
                self._grow_scale()
                self.log(
                    f"No overflow for consecutive {self._growth_interval} steps, "
                    f"the loss scale is adjusted to {self.scale.item()}",
                    ranks=[0])

    def _backoff_scale(self) -> None:
        self._scale = self._scale * self._backoff_factor
        if self._min_scale:
            self._scale = torch.max(self._scale, self._min_scale)

    def _grow_scale(self) -> None:
        self._scale = self._scale * self._growth_factor
        if self._max_scale:
            self._scale = torch.min(self._scale, self._max_scale)
