import numpy as np

def initialize_tensor_ops(nn):
    tf = nn.tf
    from tensorflow.python.ops import array_ops, random_ops, math_ops, sparse_ops, gradients
    from tensorflow.python.framework import sparse_tensor

    def tf_get_value(tensor):
        return nn.tf_sess.run (tensor)
    nn.tf_get_value = tf_get_value


    def tf_batch_set_value(tuples):
        if len(tuples) != 0:
            with nn.tf.device('/CPU:0'):
                assign_ops = []
                feed_dict = {}

                for x, value in tuples:
                    if isinstance(value, nn.tf.Operation):
                        assign_ops.append(value)
                    else:
                        value = np.asarray(value, dtype=x.dtype.as_numpy_dtype)
                        assign_placeholder = nn.tf.placeholder( x.dtype.base_dtype, shape=[None]*value.ndim )
                        assign_op = nn.tf.assign (x, assign_placeholder )
                        assign_ops.append(assign_op)
                        feed_dict[assign_placeholder] = value

                nn.tf_sess.run(assign_ops, feed_dict=feed_dict)
    nn.tf_batch_set_value = tf_batch_set_value


    def tf_gradients ( loss, vars ):
        grads = gradients.gradients(loss, vars, colocate_gradients_with_ops=True )
        gv = [*zip(grads,vars)]
        for g,v in gv:
            if g is None:
                raise Exception("No gradient for variable {v.name}")
        return gv
    nn.tf_gradients = tf_gradients

    def tf_average_gv_list(grad_var_list, tf_device_string=None):
        if len(grad_var_list) == 1:
            return grad_var_list[0]

        e = tf.device(tf_device_string) if tf_device_string is not None else None
        if e is not None: e.__enter__()
        result = []
        for i, (gv) in enumerate(grad_var_list):
            for j,(g,v) in enumerate(gv):
                g = tf.expand_dims(g, 0)
                if i == 0:
                    result += [ [[g], v]  ]
                else:
                    result[j][0] += [g]

        for i,(gs,v) in enumerate(result):
            result[i] = ( tf.reduce_mean( tf.concat (gs, 0), 0 ), v )
        if e is not None: e.__exit__(None,None,None)
        return result
    nn.tf_average_gv_list = tf_average_gv_list

    def tf_average_tensor_list(tensors_list, tf_device_string=None):
        if len(tensors_list) == 1:
            return tensors_list[0]

        e = tf.device(tf_device_string) if tf_device_string is not None else None
        if e is not None: e.__enter__()
        result = tf.reduce_mean(tf.concat ([tf.expand_dims(t, 0) for t in tensors_list], 0), 0)
        if e is not None: e.__exit__(None,None,None)
        return result
    nn.tf_average_tensor_list = tf_average_tensor_list

    def tf_concat (tensors_list, axis):
        """
        Better version.
        """
        if len(tensors_list) == 1:
            return tensors_list[0]
        return tf.concat(tensors_list, axis)
    nn.tf_concat = tf_concat

    def tf_gelu(x):
        cdf = 0.5 * (1.0 + tf.nn.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
        return x * cdf
    nn.tf_gelu = tf_gelu

    def tf_upsample2d(x, size=2):
        if nn.data_format == "NCHW":
            b,c,h,w = x.shape.as_list()
            x = tf.reshape (x, (-1,c,h,1,w,1) )
            x = tf.tile(x, (1,1,1,size,1,size) )
            x = tf.reshape (x, (-1,c,h*size,w*size) )
            return x
        else:
            return tf.image.resize_nearest_neighbor(x, (x.shape[1]*size, x.shape[2]*size) )
    nn.tf_upsample2d = tf_upsample2d

    def tf_upsample2d_bilinear(x, size=2):
        return tf.image.resize_images(x, (x.shape[1]*size, x.shape[2]*size) )
    nn.tf_upsample2d_bilinear = tf_upsample2d_bilinear

    def tf_flatten(x):
        if nn.data_format == "NHWC":
            # match NCHW version in order to switch data_format without problems
            x = tf.transpose(x, (0,3,1,2) )
        return tf.reshape (x, (-1, np.prod(x.shape[1:])) )

    nn.tf_flatten = tf_flatten

    def tf_reshape_4D(x, w,h,c):
        if nn.data_format == "NHWC":
            # match NCHW version in order to switch data_format without problems
            x = tf.reshape (x, (-1,c,h,w))
            x = tf.transpose(x, (0,2,3,1) )
            return x
        else:
            return tf.reshape (x, (-1,c,h,w))
    nn.tf_reshape_4D = tf_reshape_4D

    def tf_random_binomial(shape, p=0.0, dtype=None, seed=None):
        if dtype is None:
            dtype=tf.float32

        if seed is None:
            seed = np.random.randint(10e6)
        return array_ops.where(
            random_ops.random_uniform(shape, dtype=tf.float16, seed=seed) < p,
            array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
    nn.tf_random_binomial = tf_random_binomial

    def tf_gaussian_blur(input, radius=2.0):
        def gaussian(x, mu, sigma):
            return np.exp(-(float(x) - float(mu)) ** 2 / (2 * sigma ** 2))

        def make_kernel(sigma):
            kernel_size = max(3, int(2 * 2 * sigma + 1))
            mean = np.floor(0.5 * kernel_size)
            kernel_1d = np.array([gaussian(x, mean, sigma) for x in range(kernel_size)])
            np_kernel = np.outer(kernel_1d, kernel_1d).astype(np.float32)
            kernel = np_kernel / np.sum(np_kernel)
            return kernel, kernel_size

        gauss_kernel, kernel_size = make_kernel(radius)
        padding = kernel_size//2
        if padding != 0:
            if nn.data_format == "NHWC":
                padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
            else:
                padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
        else:
            padding = None
        gauss_kernel = gauss_kernel[:,:,None,None]

        outputs = []
        for i in range(input.shape[nn.conv2d_ch_axis]):
            x =      input[:,:,:,i:i+1] if nn.data_format == "NHWC" \
                else input[:,i:i+1,:,:]

            if padding is not None:
                x = tf.pad (x, padding)
            outputs += [ tf.nn.conv2d(x, tf.constant(gauss_kernel, dtype=input.dtype ), strides=[1,1,1,1], padding="VALID", data_format=nn.data_format) ]

        return tf.concat (outputs, axis=nn.conv2d_ch_axis)
    nn.tf_gaussian_blur = tf_gaussian_blur

    def tf_style_loss(target, style, gaussian_blur_radius=0.0, loss_weight=1.0, step_size=1):
        def sd(content, style, loss_weight):
            content_nc = content.shape[ nn.conv2d_ch_axis ]
            style_nc = style.shape[nn.conv2d_ch_axis]
            if content_nc != style_nc:
                raise Exception("style_loss() content_nc != style_nc")
            c_mean, c_var = tf.nn.moments(content, axes=nn.conv2d_spatial_axes, keep_dims=True)
            s_mean, s_var = tf.nn.moments(style, axes=nn.conv2d_spatial_axes, keep_dims=True)
            c_std, s_std = tf.sqrt(c_var + 1e-5), tf.sqrt(s_var + 1e-5)
            mean_loss = tf.reduce_sum(tf.square(c_mean-s_mean), axis=[1,2,3])
            std_loss  = tf.reduce_sum(tf.square(c_std-s_std), axis=[1,2,3])
            return (mean_loss + std_loss) * ( loss_weight / content_nc.value )

        if gaussian_blur_radius > 0.0:
            target = tf_gaussian_blur(target, gaussian_blur_radius)
            style = tf_gaussian_blur(style, gaussian_blur_radius)

        return sd( target, style, loss_weight=loss_weight )

    nn.tf_style_loss = tf_style_loss

    def tf_dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
        if img1.dtype != img2.dtype:
            raise ValueError("img1.dtype != img2.dtype")

        not_float32 = img1.dtype != tf.float32

        if not_float32:
            img_dtype = img1.dtype
            img1 = tf.cast(img1, tf.float32)
            img2 = tf.cast(img2, tf.float32)

        kernel = np.arange(0, filter_size, dtype=np.float32)
        kernel -= (filter_size - 1 ) / 2.0
        kernel = kernel**2
        kernel *= ( -0.5 / (filter_sigma**2) )
        kernel = np.reshape (kernel, (1,-1)) + np.reshape(kernel, (-1,1) )
        kernel = tf.constant ( np.reshape (kernel, (1,-1)), dtype=tf.float32 )
        kernel = tf.nn.softmax(kernel)
        kernel = tf.reshape (kernel, (filter_size, filter_size, 1, 1))
        kernel = tf.tile (kernel, (1,1, img1.shape[ nn.conv2d_ch_axis ] ,1))

        def reducer(x):
            return tf.nn.depthwise_conv2d(x, kernel, strides=[1,1,1,1], padding='VALID', data_format=nn.data_format)

        c1 = (k1 * max_val) ** 2
        c2 = (k2 * max_val) ** 2

        mean0 = reducer(img1)
        mean1 = reducer(img2)
        num0 = mean0 * mean1 * 2.0
        den0 = tf.square(mean0) + tf.square(mean1)
        luminance = (num0 + c1) / (den0 + c1)

        num1 = reducer(img1 * img2) * 2.0
        den1 = reducer(tf.square(img1) + tf.square(img2))
        c2 *= 1.0 #compensation factor
        cs = (num1 - num0 + c2) / (den1 - den0 + c2)

        ssim_val = tf.reduce_mean(luminance * cs, axis=nn.conv2d_spatial_axes )
        dssim = (1.0 - ssim_val ) / 2.0

        if not_float32:
            dssim = tf.cast(dssim, img_dtype)
        return dssim

    nn.tf_dssim = tf_dssim

    def tf_space_to_depth(x, size):
        if nn.data_format == "NHWC":
            # match NCHW version in order to switch data_format without problems
            b,h,w,c = x.shape.as_list()
            oh, ow = h // size, w // size
            x = tf.reshape(x, (-1, size, oh, size, ow, c))
            x = tf.transpose(x, (0, 2, 4, 1, 3, 5))
            x = tf.reshape(x, (-1, oh, ow, size* size* c ))
            return x
        else:
            return tf.space_to_depth(x, size, data_format=nn.data_format)
    nn.tf_space_to_depth = tf_space_to_depth

    def tf_depth_to_space(x, size):
        if nn.data_format == "NHWC":
            # match NCHW version in order to switch data_format without problems

            b,h,w,c = x.shape.as_list()
            oh, ow = h * size, w * size
            oc = c // (size * size)

            x = tf.reshape(x, (-1, h, w, size, size, oc, ) )
            x = tf.transpose(x, (0, 1, 3, 2, 4, 5))
            x = tf.reshape(x, (-1, oh, ow, oc, ))
            return x
        else:
            return tf.depth_to_space(x, size, data_format=nn.data_format)
    nn.tf_depth_to_space = tf_depth_to_space

    def tf_rgb_to_lab(srgb):
        srgb_pixels = tf.reshape(srgb, [-1, 3])
        linear_mask = tf.cast(srgb_pixels <= 0.04045, dtype=tf.float32)
        exponential_mask = tf.cast(srgb_pixels > 0.04045, dtype=tf.float32)
        rgb_pixels = (srgb_pixels / 12.92 * linear_mask) + (((srgb_pixels + 0.055) / 1.055) ** 2.4) * exponential_mask
        rgb_to_xyz = tf.constant([
            #    X        Y          Z
            [0.412453, 0.212671, 0.019334], # R
            [0.357580, 0.715160, 0.119193], # G
            [0.180423, 0.072169, 0.950227], # B
        ])
        xyz_pixels = tf.matmul(rgb_pixels, rgb_to_xyz)

        xyz_normalized_pixels = tf.multiply(xyz_pixels, [1/0.950456, 1.0, 1/1.088754])

        epsilon = 6/29
        linear_mask = tf.cast(xyz_normalized_pixels <= (epsilon**3), dtype=tf.float32)
        exponential_mask = tf.cast(xyz_normalized_pixels > (epsilon**3), dtype=tf.float32)
        fxfyfz_pixels = (xyz_normalized_pixels / (3 * epsilon**2) + 4/29) * linear_mask + (xyz_normalized_pixels ** (1/3)) * exponential_mask

        fxfyfz_to_lab = tf.constant([
            #  l       a       b
            [  0.0,  500.0,    0.0], # fx
            [116.0, -500.0,  200.0], # fy
            [  0.0,    0.0, -200.0], # fz
        ])
        lab_pixels = tf.matmul(fxfyfz_pixels, fxfyfz_to_lab) + tf.constant([-16.0, 0.0, 0.0])
        return tf.reshape(lab_pixels, tf.shape(srgb))
    nn.tf_rgb_to_lab = tf_rgb_to_lab

    def tf_suppress_lower_mean(t, eps=0.00001):
        if t.shape.ndims != 1:
            raise ValueError("tf_suppress_lower_mean: t rank must be 1")
        t_mean_eps = tf.reduce_mean(t) - eps
        q = tf.clip_by_value(t, t_mean_eps, tf.reduce_max(t) )
        q = tf.clip_by_value(q-t_mean_eps, 0, eps)
        q = q * (t/eps)
        return q
"""
class GeLU(KL.Layer):
            Gaussian Error Linear Unit.
            A smoother version of ReLU generally used
            in the BERT or BERT architecture based models.
            Original paper: https://arxiv.org/abs/1606.08415
            Input shape:
                Arbitrary. Use the keyword argument `input_shape`
                (tuple of integers, does not include the samples axis)
                when using this layer as the first layer in a model.
            Output shape:
                Same shape as the input.

            def __init__(self, approximate=True, **kwargs):
                super(GeLU, self).__init__(**kwargs)
                self.approximate = approximate
                self.supports_masking = True

            def call(self, inputs):
                cdf = 0.5 * (1.0 + K.tanh((np.sqrt(2 / np.pi) * (inputs + 0.044715 * K.pow(inputs, 3)))))
                return inputs * cdf

            def get_config(self):
                config = {'approximate': self.approximate}
                base_config = super(GeLU, self).get_config()
                return dict(list(base_config.items()) + list(config.items()))

            def compute_output_shape(self, input_shape):
                return input_shape
        nn.GeLU = GeLU
"""