import numpy as np

from deeplearning.layers import *
from deeplearning.layer_utils import *

from hw1.deeplearning import layer_utils
from hw1.deeplearning import layers


class TwoLayerNet(object):
    """
    A two-layer fully-connected neural network with ReLU nonlinearity and
    softmax loss that uses a modular layer design. We assume an input dimension
    of D, a hidden dimension of H, and perform classification over C classes.

    The architecure should be affine - relu - affine - softmax.

    Note that this class does not implement gradient descent; instead, it
    will interact with a separate Solver object that is responsible for running
    optimization.

    The learnable parameters of the model are stored in the dictionary
    self.params that maps parameter names to numpy arrays.
    """

    def __init__(self, input_dim=3 * 32 * 32, hidden_dim=100, num_classes=10,
                 weight_scale=1e-3, reg=0.0):
        """
        Initialize a new network.

        Inputs:
        - input_dim: An integer giving the size of the input
        - hidden_dim: An integer giving the size of the hidden layer
        - num_classes: An integer giving the number of classes to classify
        - dropout: Scalar between 0 and 1 giving dropout strength.
        - weight_scale: Scalar giving the standard deviation for random
          initialization of the weights.
        - reg: Scalar giving L2 regularization strength.
        """
        self.params = {}
        self.reg = reg

        ############################################################################
        # TODO: Initialize the weights and biases of the two-layer net. Weights    #
        # should be initialized from a Gaussian with standard deviation equal to   #
        # weight_scale, and biases should be initialized to zero. All weights and  #
        # biases should be stored in the dictionary self.params, with first layer  #
        # weights and biases using the keys 'W1' and 'b1' and second layer weights #
        # and biases using the keys 'W2' and 'b2'.                                 #
        ############################################################################
        self.params['W1'] = np.random.normal(scale=weight_scale, size=(input_dim, hidden_dim))
        self.params['W2'] = np.random.normal(scale=weight_scale, size=(hidden_dim, num_classes))
        self.params['b1'] = np.zeros(shape=(hidden_dim,))
        self.params['b2'] = np.zeros(shape=(num_classes,))
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

    def loss(self, X, y=None):
        """
        Compute loss and gradient for a minibatch of data.

        Inputs:
        - X: Array of input data of shape (N, d_1, ..., d_k)
        - y: Array of labels, of shape (N,). y[i] gives the label for X[i].

        Returns:
        If y is None, then run a test-time forward pass of the model and return:
        - scores: Array of shape (N, C) giving classification scores, where
          scores[i, c] is the classification score for X[i] and class c.

        If y is not None, then run a training-time forward and backward pass and
        return a tuple of:
        - loss: Scalar value giving the loss
        - grads: Dictionary with the same keys as self.params, mapping parameter
          names to gradients of the loss with respect to those parameters.
        """
        scores = None
        ############################################################################
        # TODO: Implement the forward pass for the two-layer net, computing the    #
        # class scores for X and storing them in the scores variable.              #
        ############################################################################
        scores, fc1_relu_cache = layer_utils.affine_relu_forward(x=X, w=self.params['W1'], b=self.params['b1'])
        scores, fc2_cache = layers.affine_forward(x=scores, w=self.params['W2'], b=self.params['b2'])
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        # If y is None then we are in test mode so just return scores
        if y is None:
            return scores

        loss, grads = 0, {}
        ############################################################################
        # TODO: Implement the backward pass for the two-layer net. Store the loss  #
        # in the loss variable and gradients in the grads dictionary. Compute data #
        # loss using softmax, and make sure that grads[k] holds the gradients for  #
        # self.params[k]. Don't forget to add L2 regularization on the weights,    #
        # but not the biases.                                                      #
        #                                                                          #
        # NOTE: To ensure that your implementation matches ours and you pass the   #
        # automated tests, make sure that your L2 regularization includes a factor #
        # of 0.5 to simplify the expression for the gradient.                      #
        ############################################################################
        loss, delta = layers.softmax_loss(x=scores, y=y)

        delta, grads['W2'], grads['b2']  = layers.affine_backward(dout=delta, cache=fc2_cache)
        delta, grads['W1'], grads['b1'] = layer_utils.affine_relu_backward(dout=delta, cache=fc1_relu_cache)

        # regularization
        W1, W2 = self.params['W1'], self.params['W2']
        penalty = (np.sum(np.square(W1)) + np.sum(np.square(W2))) * 0.5
        loss = loss + self.reg * penalty
        grads['W1'] += self.reg * W1
        grads['W2'] += self.reg * W2

        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        return loss, grads


class FullyConnectedNet(object):
    """
    A fully-connected neural network with an arbitrary number of hidden layers,
    ReLU nonlinearities, and a softmax loss function. This will also implement
    dropout and batch normalization as options. For a network with L layers,
    the architecture will be

    {affine - [batch norm] - relu - [dropout]} x (L - 1) - affine - softmax

    where batch normalization and dropout are optional, and the {...} block is
    repeated L - 1 times.

    Similar to the TwoLayerNet above, learnable parameters are stored in the
    self.params dictionary and will be learned using the Solver class.
    """

    def __init__(self, hidden_dims, input_dim=3 * 32 * 32, num_classes=10,
                 dropout=0, use_batchnorm=False, reg=0.0,
                 weight_scale=1e-2, dtype=np.float32, seed=None):
        """
        Initialize a new FullyConnectedNet.

        Inputs:
        - hidden_dims: A list of integers giving the size of each hidden layer.
        - input_dim: An integer giving the size of the input.
        - num_classes: An integer giving the number of classes to classify.
        - dropout: Scalar between 0 and 1 giving dropout strength. If dropout=0 then
          the network should not use dropout at all.
        - use_batchnorm: Whether or not the network should use batch normalization.
        - reg: Scalar giving L2 regularization strength.
        - weight_scale: Scalar giving the standard deviation for random
          initialization of the weights.
        - dtype: A numpy datatype object; all computations will be performed using
          this datatype. float32 is faster but less accurate, so you should use
          float64 for numeric gradient checking.
        - seed: If not None, then pass this random seed to the dropout layers. This
          will make the dropout layers deteriminstic so we can gradient check the
          model.
        """
        self.use_batchnorm = use_batchnorm
        self.use_dropout = dropout > 0
        self.reg = reg
        self.num_layers = 1 + len(hidden_dims)
        self.dtype = dtype
        self.params = {}

        ############################################################################
        # TODO: Initialize the parameters of the network, storing all values in    #
        # the self.params dictionary. Store weights and biases for the first layer #
        # in W1 and b1; for the second layer use W2 and b2, etc. Weights should be #
        # initialized from a normal distribution with standard deviation equal to  #
        # weight_scale and biases should be initialized to zero.                   #
        #                                                                          #
        # When using batch normalization, store scale and shift parameters for the #
        # first layer in gamma1 and beta1; for the second layer use gamma2 and     #
        # beta2, etc. Scale parameters should be initialized to one and shift      #
        # parameters should be initialized to zero.                                #
        ############################################################################

        dimensions = zip([input_dim, *hidden_dims], [*hidden_dims, num_classes])
        for i, (in_dim, out_dim) in enumerate(dimensions):
            self.params[f'W{i+1}'] = np.random.normal(loc=0.0, scale=weight_scale, size=(in_dim, out_dim))
            self.params[f'b{i+1}'] = np.zeros(shape=(out_dim,))
            if use_batchnorm and i < self.num_layers - 1:
                self.params[f'gamma{i + 1}'] = np.ones(shape=(out_dim,))
                self.params[f'beta{i + 1}'] = np.zeros(shape=(out_dim,))
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        # When using dropout we need to pass a dropout_param dictionary to each
        # dropout layer so that the layer knows the dropout probability and the mode
        # (train / test). You can pass the same dropout_param to each dropout layer.
        self.dropout_param = {}
        if self.use_dropout:
            self.dropout_param = {'mode': 'train', 'p': dropout}
            if seed is not None:
                self.dropout_param['seed'] = seed

        # With batch normalization we need to keep track of running means and
        # variances, so we need to pass a special bn_param object to each batch
        # normalization layer. You should pass self.bn_params[0] to the forward pass
        # of the first batch normalization layer, self.bn_params[1] to the forward
        # pass of the second batch normalization layer, etc.
        self.bn_params = []
        if self.use_batchnorm:
            self.bn_params = [{'mode': 'train'} for i in range(self.num_layers - 1)]

        # Cast all parameters to the correct datatype
        for k, v in self.params.items():
            self.params[k] = v.astype(dtype)

    def loss(self, X, y=None):
        """
        Compute loss and gradient for the fully-connected net.

        Input / output: Same as TwoLayerNet above.
        """
        X = X.astype(self.dtype)
        mode = 'test' if y is None else 'train'

        # Set train/test mode for batchnorm params and dropout param since they
        # behave differently during training and testing.
        if self.dropout_param is not None:
            self.dropout_param['mode'] = mode
        if self.use_batchnorm:
            for bn_param in self.bn_params:
                bn_param['mode'] = mode

        scores = None
        ############################################################################
        # TODO: Implement the forward pass for the fully-connected net, computing  #
        # the class scores for X and storing them in the scores variable.          #
        #                                                                          #
        # When using dropout, you'll need to pass self.dropout_param to each       #
        # dropout forward pass.                                                    #
        #                                                                          #
        # When using batch normalization, you'll need to pass self.bn_params[0] to #
        # the forward pass for the first batch normalization layer, pass           #
        # self.bn_params[1] to the forward pass for the second batch normalization #
        # layer, etc.                                                              #
        ############################################################################
        z = X
        caches = []
        for i in range(1, self.num_layers+1):
            W, b = self.params[f'W{i}'], self.params[f'b{i}']
            if i < self.num_layers:
                z, cache = layer_utils.affine_relu_forward(x=z, w=W, b=b)
                if self.use_dropout:
                    z, do_cache = layers.dropout_forward(z, dropout_param=self.dropout_param)
                    cache = (cache, do_cache)
                if self.use_batchnorm:
                    gamma, beta = self.params[f'gamma{i}'], self.params[f'beta{i}']
                    z, bn_cache = layers.batchnorm_forward(x=z, gamma=gamma, beta=beta, bn_param=self.bn_params[i - 1])
                    cache = (cache, bn_cache)
            else:
                z, cache = layers.affine_forward(x=z, w=W, b=b)
            caches.append(cache)
        scores = z
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        # If test mode return early
        if mode == 'test':
            return scores

        loss, grads = 0.0, {}
        ############################################################################
        # TODO: Implement the backward pass for the fully-connected net. Store the #
        # loss in the loss variable and gradients in the grads dictionary. Compute #
        # data loss using softmax, and make sure that grads[k] holds the gradients #
        # for self.params[k]. Don't forget to add L2 regularization on the         #
        # weights, but not the biases.                                             #
        #                                                                          #
        # When using batch normalization, you don't need to regularize the scale   #
        # and shift parameters.                                                    #
        #                                                                          #
        # NOTE: To ensure that your implementation matches ours and you pass the   #
        # automated tests, make sure that your L2 regularization includes a factor #
        # of 0.5 to simplify the expression for the gradient.                      #
        ############################################################################
        loss, delta = layers.softmax_loss(x=scores, y=y)
        penalty = 0.0
        for i, cache in reversed(list(enumerate(caches, start=1))):
            if i == self.num_layers:
                delta, grads[f'W{i}'], grads[f'b{i}'] = layers.affine_backward(dout=delta, cache=cache)
            else:
                if self.use_batchnorm:
                    cache, bn_cache = cache
                    delta, grads[f'gamma{i}'], grads[f'beta{i}'] = layers.batchnorm_backward(dout=delta, cache=bn_cache)

                if self.use_dropout:
                    cache, do_cache = cache
                    delta = layers.dropout_backward(dout=delta, cache=do_cache)

                delta, grads[f'W{i}'], grads[f'b{i}'] = layer_utils.affine_relu_backward(dout=delta, cache=cache)


            # regularization
            W = self.params[f'W{i}']
            penalty += np.sum(np.square(W))
            grads[f'W{i}'] += self.reg * W

        loss = loss + self.reg * 0.5 * penalty
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        return loss, grads


if __name__ == '__main__':
    pass