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


class Base(object):
    def __init__(self, model):
        super(Base, self).__init__()
        self.device = next(model.parameters()).device
        self.model = model
        self.handlers = []

    def _encode_one_hot(self, idx):
        one_hot = torch.zeros_like(self.logits).to(self.device)
        one_hot.scatter_(1, idx, 1.0)
        return one_hot

    def forward(self, image):
        self.image_shape = image.shape[2:]
        self.logits = self.model(image)
        self.probs = F.softmax(self.logits, dim=1)
        return self.probs.sort(dim=1, descending=True)

    def backward(self, ids):
        one_hot = self._encode_one_hot(ids)
        self.model.zero_grad()
        self.logits.backward(gradient=one_hot, retain_graph=True)

    def generate(self):
        raise NotImplementedError

    def remove_hook(self):
        for handle in self.handlers:
            handle.remove()

class BackPropagation(Base):
    def forward(self, image):
        self.image = image.requires_grad_()
        return super(BackPropagation, self).forward(self.image)

    def generate(self):
        gradient = self.image.grad.clone()
        self.image.grad.zero_()
        return gradient

class GuidedBackProp(BackPropagation):
    def __init__(self, model):
        super(GuidedBackProp, self).__init__(model)

        def backward_hook(module, grad_in, grad_out):
            if isinstance(module, nn.ReLU):
                return (F.relu(grad_in[0]),)

        for module in self.model.named_modules():
            self.handlers.append(module[1].register_backward_hook(backward_hook))


class Deconvnet(BackPropagation):
    def __init__(self, model):
        super(Deconvnet, self).__init__(model)

        def backward_hook(module, grad_in, grad_out):
            if isinstance(module, nn.ReLU):
                return (F.relu(grad_out[0]),)

        for module in self.model.named_modules():
            self.handlers.append(module[1].register_backward_hook(backward_hook))


class GradCAM(Base):
    def __init__(self, model, candidate_layers=None):
        super(GradCAM, self).__init__(model)
        self.fmap_pool = {}
        self.grad_pool = {}
        self.candidate_layers = candidate_layers

        def save_fmaps(key):
            def forward_hook(module, input, output):
                self.fmap_pool[key] = output.detach()

            return forward_hook

        def save_grads(key):
            def backward_hook(module, grad_in, grad_out):
                self.grad_pool[key] = grad_out[0].detach()

            return backward_hook

        for name, module in self.model.named_modules():
            if self.candidate_layers is None or name in self.candidate_layers:
                self.handlers.append(module.register_forward_hook(save_fmaps(name)))
                self.handlers.append(module.register_backward_hook(save_grads(name)))

    def _find(self, pool, target_layer):
        if target_layer in pool.keys():
            return pool[target_layer]
        else:
            raise ValueError("Layer no válida: {}".format(target_layer))

    def generate(self, target_layer):
        fmaps = self._find(self.fmap_pool, target_layer)
        grads = self._find(self.grad_pool, target_layer)
        weights = F.adaptive_avg_pool2d(grads, 1)

        gcam = torch.mul(fmaps, weights).sum(dim=1, keepdim=True)
        gcam = F.relu(gcam)
        gcam = F.interpolate(
            gcam, self.image_shape, mode="bilinear", align_corners=False
        )

        B, C, H, W = gcam.shape
        gcam = gcam.view(B, -1)
        gcam -= gcam.min(dim=1, keepdim=True)[0]
        gcam /= gcam.max(dim=1, keepdim=True)[0]
        gcam = gcam.view(B, C, H, W)

        return gcam