#| default_exp learner

#|export
import math,torch,matplotlib.pyplot as plt
import fastcore.all as fc
from collections.abc import Mapping
from operator import attrgetter
from functools import partial
from copy import copy

from torch import optim
import torch.nn.functional as F

from miniai.conv import *

from fastprogress import progress_bar,master_bar

import matplotlib as mpl
import torchvision.transforms.functional as TF
from contextlib import contextmanager
from torch import nn,tensor
from datasets import load_dataset,load_dataset_builder
from miniai.datasets import *
from miniai.conv import *
import logging
from fastcore.test import test_close

torch.set_printoptions(precision=2, linewidth=140, sci_mode=False)
torch.manual_seed(1)
mpl.rcParams['image.cmap'] = 'gray'

logging.disable(logging.WARNING)

x,y = 'image','label'
name = "fashion_mnist"
dsd = load_dataset(name)

@inplace
def transformi(b): b[x] = [torch.flatten(TF.to_tensor(o)) for o in b[x]]

bs = 1024
tds = dsd.with_transform(transformi)

dls = DataLoaders.from_dd(tds, bs, num_workers=4)
dt = dls.train
xb,yb = next(iter(dt))
xb.shape,yb[:10]

class Learner:
    def __init__(self, model, dls, loss_func, lr, opt_func=optim.SGD): fc.store_attr()

    def one_batch(self):
        self.xb,self.yb = to_device(self.batch)
        self.preds = self.model(self.xb)
        self.loss = self.loss_func(self.preds, self.yb)
        if self.model.training:
            self.loss.backward()
            self.opt.step()
            self.opt.zero_grad()
        with torch.no_grad(): self.calc_stats()

    def calc_stats(self):
        acc = (self.preds.argmax(dim=1)==self.yb).float().sum()
        self.accs.append(acc)
        n = len(self.xb)
        self.losses.append(self.loss*n)
        self.ns.append(n)

    def one_epoch(self, train):
        self.model.training = train
        dl = self.dls.train if train else self.dls.valid
        for self.num,self.batch in enumerate(dl): self.one_batch()
        n = sum(self.ns)
        print(self.epoch, self.model.training, sum(self.losses).item()/n, sum(self.accs).item()/n)
    
    def fit(self, n_epochs):
        self.accs,self.losses,self.ns = [],[],[]
        self.model.to(def_device)
        self.opt = self.opt_func(self.model.parameters(), self.lr)
        self.n_epochs = n_epochs
        for self.epoch in range(n_epochs):
            self.one_epoch(True)
            with torch.no_grad(): self.one_epoch(False)

m,nh = 28*28,50
model = nn.Sequential(nn.Linear(m,nh), nn.ReLU(), nn.Linear(nh,10))

learn = Learner(model, dls, F.cross_entropy, lr=0.2)
learn.fit(1)

#|export
class CancelFitException(Exception): pass
class CancelBatchException(Exception): pass
class CancelEpochException(Exception): pass

#|export
class Callback(): order = 0

#|export
def run_cbs(cbs, method_nm, learn=None):
    for cb in sorted(cbs, key=attrgetter('order')):
        method = getattr(cb, method_nm, None)
        if method is not None: method(learn)

class CompletionCB(Callback):
    def before_fit(self, learn): self.count = 0
    def after_batch(self, learn): self.count += 1
    def after_fit(self, learn): print(f'Completed {self.count} batches')

cbs = [CompletionCB()]
run_cbs(cbs, 'before_fit')
run_cbs(cbs, 'after_batch')
run_cbs(cbs, 'after_fit')

class Learner():
    def __init__(self, model, dls, loss_func, lr, cbs, opt_func=optim.SGD): fc.store_attr()

    def one_batch(self):
        self.preds = self.model(self.batch[0])
        self.loss = self.loss_func(self.preds, self.batch[1])
        if self.model.training:
            self.loss.backward()
            self.opt.step()
            self.opt.zero_grad()

    def one_epoch(self, train):
        self.model.train(train)
        self.dl = self.dls.train if train else self.dls.valid
        try:
            self.callback('before_epoch')
            for self.iter,self.batch in enumerate(self.dl):
                try:
                    self.callback('before_batch')
                    self.one_batch()
                    self.callback('after_batch')
                except CancelBatchException: pass
            self.callback('after_epoch')
        except CancelEpochException: pass
    
    def fit(self, n_epochs):
        self.n_epochs = n_epochs
        self.epochs = range(n_epochs)
        self.opt = self.opt_func(self.model.parameters(), self.lr)
        try:
            self.callback('before_fit')
            for self.epoch in self.epochs:
                self.one_epoch(True)
                self.one_epoch(False)
            self.callback('after_fit')
        except CancelFitException: pass

    def callback(self, method_nm): run_cbs(self.cbs, method_nm, self)

m,nh = 28*28,50
def get_model(): return nn.Sequential(nn.Linear(m,nh), nn.ReLU(), nn.Linear(nh,10))

model = get_model()
learn = Learner(model, dls, F.cross_entropy, lr=0.2, cbs=[CompletionCB()])
learn.fit(1)

#| export
class SingleBatchCB(Callback):
    order = 1
    def after_batch(self, learn): raise CancelFitException()

learn = Learner(get_model(), dls, F.cross_entropy, lr=0.2, cbs=[SingleBatchCB(), CompletionCB()])
learn.fit(1)

class Metric:
    def __init__(self): self.reset()
    def reset(self): self.vals,self.ns = [],[]
    def add(self, inp, targ=None, n=1):
        self.last = self.calc(inp, targ)
        self.vals.append(self.last)
        self.ns.append(n)
    @property
    def value(self):
        ns = tensor(self.ns)
        return (tensor(self.vals)*ns).sum()/ns.sum()
    def calc(self, inps, targs): return inps

class Accuracy(Metric):
    def calc(self, inps, targs): return (inps==targs).float().mean()

acc = Accuracy()
acc.add(tensor([0, 1, 2, 0, 1, 2]), tensor([0, 1, 1, 2, 1, 0]))
acc.add(tensor([1, 1, 2, 0, 1]), tensor([0, 1, 1, 2, 1]))
acc.value

loss = Metric()
loss.add(0.6, n=32)
loss.add(0.9, n=2)
loss.value, round((0.6*32+0.9*2)/(32+2), 2)

#|export
from torcheval.metrics import MulticlassAccuracy,Mean

metric = MulticlassAccuracy()
metric.update(tensor([0, 2, 1, 3]), tensor([0, 1, 2, 3]))
metric.compute()

metric.reset()
metric.compute()

#|export
def to_cpu(x):
    if isinstance(x, Mapping): return {k:to_cpu(v) for k,v in x.items()}
    if isinstance(x, list): return [to_cpu(o) for o in x]
    if isinstance(x, tuple): return tuple(to_cpu(list(x)))
    res = x.detach().cpu()
    return res.float() if res.dtype==torch.float16 else res

#|export
class MetricsCB(Callback):
    def __init__(self, *ms, **metrics):
        for o in ms: metrics[type(o).__name__] = o
        self.metrics = metrics
        self.all_metrics = copy(metrics)
        self.all_metrics['loss'] = self.loss = Mean()

    def _log(self, d): print(d)
    def before_fit(self, learn): learn.metrics = self
    def before_epoch(self, learn): [o.reset() for o in self.all_metrics.values()]

    def after_epoch(self, learn):
        log = {k:f'{v.compute():.3f}' for k,v in self.all_metrics.items()}
        log['epoch'] = learn.epoch
        log['train'] = 'train' if learn.model.training else 'eval'
        self._log(log)

    def after_batch(self, learn):
        x,y,*_ = to_cpu(learn.batch)
        for m in self.metrics.values(): m.update(to_cpu(learn.preds), y)
        self.loss.update(to_cpu(learn.loss), weight=len(x))

#|export
class DeviceCB(Callback):
    def __init__(self, device=def_device): fc.store_attr()
    def before_fit(self, learn):
        if hasattr(learn.model, 'to'): learn.model.to(self.device)
    def before_batch(self, learn): learn.batch = to_device(learn.batch, device=self.device)

model = get_model()
metrics = MetricsCB(accuracy=MulticlassAccuracy())
learn = Learner(model, dls, F.cross_entropy, lr=0.2, cbs=[DeviceCB(), metrics])
learn.fit(1)

class Learner():
    def __init__(self, model, dls=(0,), loss_func=F.mse_loss, lr=0.1, cbs=None, opt_func=optim.SGD):
        cbs = fc.L(cbs)
        fc.store_attr()

    @contextmanager
    def cb_ctx(self, nm):
        try:
            self.callback(f'before_{nm}')
            yield
            self.callback(f'after_{nm}')
        except globals()[f'Cancel{nm.title()}Exception']: pass
        finally: self.callback(f'cleanup_{nm}')
                
    def one_epoch(self, train):
        self.model.train(train)
        self.dl = self.dls.train if train else self.dls.valid
        with self.cb_ctx('epoch'):
            for self.iter,self.batch in enumerate(self.dl):
                with self.cb_ctx('batch'):
                    self.predict()
                    self.get_loss()
                    if self.training:
                        self.backward()
                        self.step()
                        self.zero_grad()
    
    def fit(self, n_epochs=1, train=True, valid=True, cbs=None, lr=None):
        cbs = fc.L(cbs)
        # `add_cb` and `rm_cb` were added in lesson 18
        for cb in cbs: self.cbs.append(cb)
        try:
            self.n_epochs = n_epochs
            self.epochs = range(n_epochs)
            self.opt = self.opt_func(self.model.parameters(), self.lr if lr is None else lr)
            with self.cb_ctx('fit'):
                for self.epoch in self.epochs:
                    if train: self.one_epoch(True)
                    if valid: torch.no_grad()(self.one_epoch)(False)
        finally:
            for cb in cbs: self.cbs.remove(cb)

    def __getattr__(self, name):
        if name in ('predict','get_loss','backward','step','zero_grad'): return partial(self.callback, name)
        raise AttributeError(name)

    def callback(self, method_nm): run_cbs(self.cbs, method_nm, self)
    
    @property
    def training(self): return self.model.training

#|export
class TrainCB(Callback):
    def __init__(self, n_inp=1): self.n_inp = n_inp
    def predict(self, learn): learn.preds = learn.model(*learn.batch[:self.n_inp])
    def get_loss(self, learn): learn.loss = learn.loss_func(learn.preds, *learn.batch[self.n_inp:])
    def backward(self, learn): learn.loss.backward()
    def step(self, learn): learn.opt.step()
    def zero_grad(self, learn): learn.opt.zero_grad()

#|export
class ProgressCB(Callback):
    order = MetricsCB.order+1
    def __init__(self, plot=False): self.plot = plot
    def before_fit(self, learn):
        learn.epochs = self.mbar = master_bar(learn.epochs)
        self.first = True
        if hasattr(learn, 'metrics'): learn.metrics._log = self._log
        self.losses = []
        self.val_losses = []

    def _log(self, d):
        if self.first:
            self.mbar.write(list(d), table=True)
            self.first = False
        self.mbar.write(list(d.values()), table=True)

    def before_epoch(self, learn): learn.dl = progress_bar(learn.dl, leave=False, parent=self.mbar)
    def after_batch(self, learn):
        learn.dl.comment = f'{learn.loss:.3f}'
        if self.plot and hasattr(learn, 'metrics') and learn.training:
            self.losses.append(learn.loss.item())
            if self.val_losses: self.mbar.update_graph([[fc.L.range(self.losses), self.losses],[fc.L.range(learn.epoch).map(lambda x: (x+1)*len(learn.dls.train)), self.val_losses]])
    
    def after_epoch(self, learn): 
        if not learn.training:
            if self.plot and hasattr(learn, 'metrics'): 
                self.val_losses.append(learn.metrics.all_metrics['loss'].compute())
                self.mbar.update_graph([[fc.L.range(self.losses), self.losses],[fc.L.range(learn.epoch+1).map(lambda x: (x+1)*len(learn.dls.train)), self.val_losses]])

model = get_model()

metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [TrainCB(), DeviceCB(), metrics, ProgressCB(plot=True)]
learn = Learner(model, dls, F.cross_entropy, lr=0.2, cbs=cbs)
learn.fit(2)

#|export
class with_cbs:
    def __init__(self, nm): self.nm = nm
    def __call__(self, f):
        def _f(o, *args, **kwargs):
            try:
                o.callback(f'before_{self.nm}')
                f(o, *args, **kwargs)
                o.callback(f'after_{self.nm}')
            except globals()[f'Cancel{self.nm.title()}Exception']: pass
            finally: o.callback(f'cleanup_{self.nm}')
        return _f

#|export
class Learner():
    def __init__(self, model, dls=(0,), loss_func=F.mse_loss, lr=0.1, cbs=None, opt_func=optim.SGD):
        cbs = fc.L(cbs)
        fc.store_attr()

    @with_cbs('batch')
    def _one_batch(self):
        self.predict()
        self.callback('after_predict')
        self.get_loss()
        self.callback('after_loss')
        if self.training:
            self.backward()
            self.callback('after_backward')
            self.step()
            self.callback('after_step')
            self.zero_grad()

    @with_cbs('epoch')
    def _one_epoch(self):
        for self.iter,self.batch in enumerate(self.dl): self._one_batch()

    def one_epoch(self, training):
        self.model.train(training)
        self.dl = self.dls.train if training else self.dls.valid
        self._one_epoch()

    @with_cbs('fit')
    def _fit(self, train, valid):
        for self.epoch in self.epochs:
            if train: self.one_epoch(True)
            if valid: torch.no_grad()(self.one_epoch)(False)

    def fit(self, n_epochs=1, train=True, valid=True, cbs=None, lr=None):
        cbs = fc.L(cbs)
        # `add_cb` and `rm_cb` were added in lesson 18
        for cb in cbs: self.cbs.append(cb)
        try:
            self.n_epochs = n_epochs
            self.epochs = range(n_epochs)
            if lr is None: lr = self.lr
            if self.opt_func: self.opt = self.opt_func(self.model.parameters(), lr)
            self._fit(train, valid)
        finally:
            for cb in cbs: self.cbs.remove(cb)

    def __getattr__(self, name):
        if name in ('predict','get_loss','backward','step','zero_grad'): return partial(self.callback, name)
        raise AttributeError(name)

    def callback(self, method_nm): run_cbs(self.cbs, method_nm, self)
    
    @property
    def training(self): return self.model.training

model = get_model()

metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [TrainCB(), DeviceCB(), metrics, ProgressCB(plot=True)]
learn = Learner(model, dls, F.cross_entropy, lr=0.2, cbs=cbs)
learn.fit(1)

#|export
class TrainLearner(Learner):
    def predict(self): self.preds = self.model(self.batch[0])
    def get_loss(self): self.loss = self.loss_func(self.preds, self.batch[1])
    def backward(self): self.loss.backward()
    def step(self): self.opt.step()
    def zero_grad(self): self.opt.zero_grad()

#|export
class MomentumLearner(TrainLearner):
    def __init__(self, model, dls, loss_func, lr=None, cbs=None, opt_func=optim.SGD, mom=0.85):
        self.mom = mom
        super().__init__(model, dls, loss_func, lr, cbs, opt_func)

    def zero_grad(self):
        with torch.no_grad():
            for p in self.model.parameters(): p.grad *= self.mom

# NB: No TrainCB
metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [DeviceCB(), metrics, ProgressCB(plot=True)]
learn = MomentumLearner(get_model(), dls, F.cross_entropy, lr=0.1, cbs=cbs)
learn.fit(1)

class LRFinderCB(Callback):
    def __init__(self, lr_mult=1.3): fc.store_attr()
    
    def before_fit(self, learn):
        self.lrs,self.losses = [],[]
        self.min = math.inf

    def after_batch(self, learn):
        if not learn.training: raise CancelEpochException()
        self.lrs.append(learn.opt.param_groups[0]['lr'])
        loss = to_cpu(learn.loss)
        self.losses.append(loss)
        if loss < self.min: self.min = loss
        if loss > self.min*3: raise CancelFitException()
        for g in learn.opt.param_groups: g['lr'] *= self.lr_mult

lrfind = LRFinderCB()
cbs = [DeviceCB(), lrfind]
learn = MomentumLearner(get_model(), dls, F.cross_entropy, lr=1e-4, cbs=cbs)
learn.fit(1)
plt.plot(lrfind.lrs, lrfind.losses)
plt.xscale('log')

#|export
from torch.optim.lr_scheduler import ExponentialLR

#|export
class LRFinderCB(Callback):
    def __init__(self, gamma=1.3, max_mult=3): fc.store_attr()
    
    def before_fit(self, learn):
        self.sched = ExponentialLR(learn.opt, self.gamma)
        self.lrs,self.losses = [],[]
        self.min = math.inf

    def after_batch(self, learn):
        if not learn.training: raise CancelEpochException()
        self.lrs.append(learn.opt.param_groups[0]['lr'])
        loss = to_cpu(learn.loss)
        self.losses.append(loss)
        if loss < self.min: self.min = loss
        if math.isnan(loss) or (loss > self.min*self.max_mult):
            raise CancelFitException()
        self.sched.step()

    def cleanup_fit(self, learn):
        plt.plot(self.lrs, self.losses)
        plt.xscale('log')

cbs = [DeviceCB()]
learn = MomentumLearner(get_model(), dls, F.cross_entropy, lr=1e-5, cbs=cbs)
learn.fit(3, cbs=LRFinderCB())

#|export
@fc.patch
def lr_find(self:Learner, gamma=1.3, max_mult=3, start_lr=1e-5, max_epochs=10):
    self.fit(max_epochs, lr=start_lr, cbs=LRFinderCB(gamma=gamma, max_mult=max_mult))

MomentumLearner(get_model(), dls, F.cross_entropy, cbs=cbs).lr_find()

import nbdev; nbdev.nbdev_export()