import ipywidgets as widgets

def f(o): print('hi')

"""
Explanation: Jump_to opening comments and overview of lesson 10
Callbacks
Callbacks as GUI events
Jump_to lesson 10 video
End of explanation
"""


w = widgets.Button(description='Click me')

w

w.on_click(f)

"""
Explanation: From the ipywidget docs:

the button widget is used to handle mouse clicks. The on_click method of the Button can be used to register function to be called when the button is clicked
End of explanation
"""


from time import sleep

def slow_calculation():
    res = 0
    for i in range(5):
        res += i*i
        sleep(1)
    return res

slow_calculation()

def slow_calculation(cb=None):
    res = 0
    for i in range(5):
        res += i*i
        sleep(1)
        if cb: cb(i)
    return res

def show_progress(epoch):
    print(f"Awesome! We've finished epoch {epoch}!")

slow_calculation(show_progress)

"""
Explanation: NB: When callbacks are used in this way they are often called "events".
Did you know what you can create interactive apps in Jupyter with these widgets? Here's an example from plotly:

Creating your own callback
Jump_to lesson 10 video
End of explanation
"""


slow_calculation(lambda o: print(f"Awesome! We've finished epoch {o}!"))

def show_progress(exclamation, epoch):
    print(f"{exclamation}! We've finished epoch {epoch}!")

slow_calculation(lambda o: show_progress("OK I guess", o))

def make_show_progress(exclamation):
    _inner = lambda epoch: print(f"{exclamation}! We've finished epoch {epoch}!")
    return _inner

slow_calculation(make_show_progress("Nice!"))

def make_show_progress(exclamation):
    # Leading "_" is generally understood to be "private"
    def _inner(epoch): print(f"{exclamation}! We've finished epoch {epoch}!")
    return _inner

slow_calculation(make_show_progress("Nice!"))

f2 = make_show_progress("Terrific")

slow_calculation(f2)

slow_calculation(make_show_progress("Amazing"))

from functools import partial

slow_calculation(partial(show_progress, "OK I guess"))

f2 = partial(show_progress, "OK I guess")

"""
Explanation: Lambdas and partials
Jump_to lesson 10 video
End of explanation
"""


class ProgressShowingCallback():
    def __init__(self, exclamation="Awesome"): self.exclamation = exclamation
    def __call__(self, epoch): print(f"{self.exclamation}! We've finished epoch {epoch}!")

cb = ProgressShowingCallback("Just super")

slow_calculation(cb)

"""
Explanation: Callbacks as callable classes
Jump_to lesson 10 video
End of explanation
"""


def f(*args, **kwargs): print(f"args: {args}; kwargs: {kwargs}")

f(3, 'a', thing1="hello")

"""
Explanation: Multiple callback funcs; *args and **kwargs
Jump_to lesson 10 video
End of explanation
"""


def slow_calculation(cb=None):
    res = 0
    for i in range(5):
        if cb: cb.before_calc(i)
        res += i*i
        sleep(1)
        if cb: cb.after_calc(i, val=res)
    return res

class PrintStepCallback():
    def __init__(self): pass
    def before_calc(self, *args, **kwargs): print(f"About to start")
    def after_calc (self, *args, **kwargs): print(f"Done step")

slow_calculation(PrintStepCallback())

class PrintStatusCallback():
    def __init__(self): pass
    def before_calc(self, epoch, **kwargs): print(f"About to start: {epoch}")
    def after_calc (self, epoch, val, **kwargs): print(f"After {epoch}: {val}")

slow_calculation(PrintStatusCallback())

"""
Explanation: NB: We've been guilty of over-using kwargs in fastai - it's very convenient for the developer, but is annoying for the end-user unless care is taken to ensure docs show all kwargs too. kwargs can also hide bugs (because it might not tell you about a typo in a param name). In R there's a very similar issue (R uses ... for the same thing), and matplotlib uses kwargs a lot too.
End of explanation
"""


def slow_calculation(cb=None):
    res = 0
    for i in range(5):
        if cb and hasattr(cb,'before_calc'): cb.before_calc(i)
        res += i*i
        sleep(1)
        if cb and hasattr(cb,'after_calc'):
            if cb.after_calc(i, res):
                print("stopping early")
                break
    return res

class PrintAfterCallback():
    def after_calc (self, epoch, val):
        print(f"After {epoch}: {val}")
        if val>10: return True

slow_calculation(PrintAfterCallback())

class SlowCalculator():
    def __init__(self, cb=None): self.cb,self.res = cb,0
    
    def callback(self, cb_name, *args):
        if not self.cb: return
        cb = getattr(self.cb,cb_name, None)
        if cb: return cb(self, *args)

    def calc(self):
        for i in range(5):
            self.callback('before_calc', i)
            self.res += i*i
            sleep(1)
            if self.callback('after_calc', i):
                print("stopping early")
                break

class ModifyingCallback():
    def after_calc (self, calc, epoch):
        print(f"After {epoch}: {calc.res}")
        if calc.res>10: return True
        if calc.res<3: calc.res = calc.res*2

calculator = SlowCalculator(ModifyingCallback())

calculator.calc()
calculator.res

"""
Explanation: Modifying behavior
Jump_to lesson 10 video
End of explanation
"""


class SloppyAdder():
    def __init__(self,o): self.o=o
    def __add__(self,b): return SloppyAdder(self.o + b.o + 0.01)
    def __repr__(self): return str(self.o)

a = SloppyAdder(1)
b = SloppyAdder(2)
a+b

"""
Explanation: __dunder__ thingies
Anything that looks like __this__ is, in some way, special. Python, or some library, can define some functions that they will call at certain documented times. For instance, when your class is setting up a new object, python will call __init__. These are defined as part of the python data model.
For instance, if python sees +, then it will call the special method __add__. If you try to display an object in Jupyter (or lots of other places in Python) it will call __repr__.
Jump_to lesson 10 video
End of explanation
"""


t = torch.tensor([1.,2.,4.,18])

m = t.mean(); m

(t-m).mean()

"""
Explanation: Special methods you should probably know about (see data model link above) are:

__getitem__
__getattr__
__setattr__
__del__
__init__
__new__
__enter__
__exit__
__len__
__repr__
__str__

Variance and stuff
Variance
Variance is the average of how far away each data point is from the mean. E.g.:
Jump_to lesson 10 video
End of explanation
"""


(t-m).pow(2).mean()

(t-m).abs().mean()

"""
Explanation: Oops. We can't do that. Because by definition the positives and negatives cancel out. So we can fix that in one of (at least) two ways:
End of explanation
"""


(t-m).pow(2).mean().sqrt()

"""
Explanation: But the first of these is now a totally different scale, since we squared. So let's undo that at the end.
End of explanation
"""


(t-m).pow(2).mean(), (t*t).mean() - (m*m)

"""
Explanation: They're still different. Why?
Note that we have one outlier (18). In the version where we square everything, it makes that much bigger than everything else.
(t-m).pow(2).mean() is refered to as variance. It's a measure of how spread out the data is, and is particularly sensitive to outliers.
When we take the sqrt of the variance, we get the standard deviation. Since it's on the same kind of scale as the original data, it's generally more interpretable. However, since sqrt(1)==1, it doesn't much matter which we use when talking about unit variance for initializing neural nets.
(t-m).abs().mean() is referred to as the mean absolute deviation. It isn't used nearly as much as it deserves to be, because mathematicians don't like how awkward it is to work with. But that shouldn't stop us, because we have computers and stuff.
Here's a useful thing to note about variance:
End of explanation
"""


t

"""
Explanation: You can see why these are equal if you want to work thru the algebra. Or not.
But, what's important here is that the latter is generally much easier to work with. In particular, you only have to track two things: the sum of the data, and the sum of squares of the data. Whereas in the first form you actually have to go thru all the data twice (once to calculate the mean, once to calculate the differences).
Let's go steal the LaTeX from Wikipedia:
$$\operatorname{E}\left[X^2 \right] - \operatorname{E}[X]^2$$
Covariance and correlation
Here's how Wikipedia defines covariance:
$$\operatorname{cov}(X,Y) = \operatorname{E}{\big[(X - \operatorname{E}[X])(Y - \operatorname{E}[Y])\big]}$$
Jump_to lesson 10 video
End of explanation
"""


# `u` is twice `t`, plus a bit of randomness
u = t*2
u *= torch.randn_like(t)/10+0.95

plt.scatter(t, u);

prod = (t-t.mean())*(u-u.mean()); prod

prod.mean()

v = torch.randn_like(t)
plt.scatter(t, v);

((t-t.mean())*(v-v.mean())).mean()

"""
Explanation: Let's see that in code. So now we need two vectors.
End of explanation
"""


cov = (t*v).mean() - t.mean()*v.mean(); cov

"""
Explanation: It's generally more conveniently defined like so:
$$\operatorname{E}\left[X Y\right] - \operatorname{E}\left[X\right] \operatorname{E}\left[Y\right]$$
End of explanation
"""


cov / (t.std() * v.std())

"""
Explanation: From now on, you're not allowed to look at an equation (or especially type it in LaTeX) without also typing it in Python and actually calculating some values. Ideally, you should also plot some values.
Finally, here is the Pearson correlation coefficient:
$$\rho_{X,Y}= \frac{\operatorname{cov}(X,Y)}{\sigma_X \sigma_Y}$$
End of explanation
"""


def log_softmax(x): return x - x.exp().sum(-1,keepdim=True).log()

"""
Explanation: It's just a scaled version of the same thing. Question: Why is it scaled by standard deviation, and not by variance or mean or something else?
Softmax
Here's our final logsoftmax definition:
Jump_to lesson 10 video
End of explanation
"""


