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class CallbackHandler():
    def __init__(self,cbs=None):
        self.cbs = cbs if cbs else []

    def begin_fit(self, learn):
        self.learn,self.in_train = learn,True
        learn.stop = False
        res = True
        for cb in self.cbs: res = res and cb.begin_fit(learn)
        return res

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class Callback():
    def begin_fit(self, learn):
        self.learn = learn
        return True
    def after_fit(self): return True
    def begin_epoch(self, epoch):
        self.epoch=epoch
        return True
    def begin_validate(self): return True
    def after_epoch(self): return True

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def one_batch(xb, yb, cb):
    if not cb.begin_batch(xb,yb): return
    loss = cb.learn.loss_func(cb.learn.model(xb), yb)
    if not cb.after_loss(loss): return
    loss.backward()
    if cb.after_backward(): cb.learn.opt.step()
    if cb.after_step(): cb.learn.opt.zero_grad()

def all_batches(dl, cb):
    for xb,yb in dl:

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class CallbackHandler():
    def __init__(self,cbs=None):
        self.cbs = cbs if cbs else []

    def begin_fit(self, learn):
        self.learn,self.in_train = learn,True
        learn.stop = False
        res = True
        for cb in self.cbs: res = res and cb.begin_fit(learn)
        return res

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class BatchCounter(Callback): 
    def begin_epoch(self, epoch):
        self.epoch=epoch
        self.batch_counter = 1
        return True
        
    def after_step(self):
        self.batch_counter += 1
        if self.batch_counter % 200 == 0: print(f'Batch {self.batch_counter} completed')
        return True

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def one_batch(xb,yb):
    pred = model(xb)
    loss = loss_func(pred, yb)
    loss.backward()
    opt.step()
    opt.zero_grad()
    
def fit():
    for epoch in range(epochs):
        for b in train_dl: one_batch(*b)

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