import tensorflow as tf
import numpy as np
import logging 

from ..core import Layer, Fake
from .ops import get_param_from_name, load_pkl_obj

def _fixed_padding(inputs, kernel_size, data_format):
  """Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or
      [batch, height_in, width_in, channels] depending on data_format.
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
                 Should be a positive integer.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    A tensor with the same format as the input with the data either intact
    (if kernel_size == 1) or padded (if kernel_size > 1).
  """
  pad_total = kernel_size - 1
  pad_beg = pad_total // 2
  pad_end = pad_total - pad_beg

  if data_format == 'channels_first':
    padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],
                                    [pad_beg, pad_end], [pad_beg, pad_end]])
  else:
    padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
                                    [pad_beg, pad_end], [0, 0]])
  return padded_inputs

class Conv2DFixedPadding(Layer):
    """ Definition for a convolution layer. 
        Strided 2-D convolution with explicit padding.

        Args:
        Args:
            name: name of the module.
            inputs: the input symbol.
            start: layer number for begining. 
            end: layer number for ending.
            weights: If need to initialize with some parameters, supply the numpy pickle path. 
                     We will read the exact same layer name.    
            data_format: typically 'channel_first'
            kernel_size: Size of each filter. Typically 3.
            filterS: Number of output channels required.
            strides: Stride of convolution.            
    """     
    def __init__ (self, name, inputs, filters, kernel_size, strides, data_format, start=None,
                  end=None, weights=None, weight_scope=None, fake=False):
        super(Conv2DFixedPadding, self).__init__(name = name, start=start, end=end)
        self.fake = fake
        if not self.fake:
            if weights is not None:
                params_name = weight_scope + '/' + str(name) + '/conv2d/'
                np_dict = load_pkl_obj(weights)
                kernel_np = np_dict[params_name+'kernel:0']
                in_shp = inputs.shape.as_list()[1]
                if not kernel_np.shape[2] == in_shp:
                    kernel_np = np.resize(kernel_np, (kernel_size, kernel_size, in_shp, filters))
                kernel_initializer = tf.constant_initializer(kernel_np)
            else:
                kernel_initializer = tf.variance_scaling_initializer()
            with tf.variable_scope(self._name):
                if strides > 1:
                    inputs = _fixed_padding(inputs, kernel_size, data_format)

                self.output=tf.layers.conv2d(inputs=inputs, 
                                            filters=filters, 
                                            kernel_size=kernel_size, 
                                            strides=strides,
                                            padding=('SAME' if strides == 1 else 'VALID'), 
                                            use_bias=False,
                                            kernel_initializer=kernel_initializer,
                                            data_format=data_format)
            self._tf_name = self.output.name.split('/')[0] + '/' + self.output.name.split('/')[1]
        else:
            assert isinstance(inputs, Fake)
            in_shp = inputs.shape
            param_shp = (kernel_size, kernel_size, in_shp[1], filters)
            self.param = Fake(param_shp)
            if kernel_size > 1:
                out_height = int(np.floor(((in_shp[2] - kernel_size  + 2)/strides )) + 1)
                out_width = int(np.floor(((in_shp[3] - kernel_size  + 2)/strides )) + 1)
            else:
                out_height = in_shp[2] / strides
                out_width = in_shp[3] / strides
            out_shp = (None, filters, out_height, out_width)
            self.output = Fake(out_shp)
            self._tf_name = 'fake'
        self.description.append('Conv')
        self.description.append(filters)
        self.description.append(kernel_size)
        self.description.append(strides)
        self.description.append(('SAME' if strides == 1 else 'VALID'))
        self.description.append(self.get_memory_footprint())

    def _get_params_real(self):
        """ Returns the kernel node """ 
        return {self.get_name(): get_param_from_name(self._tf_name + '/conv2d/kernel:0')}

    def _get_memory_footprint_real(self):
        """ Number of parameters in the layer """ 
        params = self.get_params()[self.get_name()]
        return int(np.prod(params.shape))

    def _get_params_fake(self):
        return self.param
    
    def _get_memory_footprint_fake(self):
        return int(np.prod(self.get_params().shape))
    
    def get_memory_footprint(self):
        if self.fake:
            return self._get_memory_footprint_fake()
        else:
            return self._get_memory_footprint_real()
    
    def get_params(self):
        if self.fake:
            return self._get_params_fake()
        else:
            return self._get_params_real()
import tensorflow as tf
import numpy as np
import logging 

from ..core import Layer, Fake
from .ops import get_param_from_name, load_pkl_obj

def _fixed_padding(inputs, kernel_size, data_format):
  """Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or
      [batch, height_in, width_in, channels] depending on data_format.
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
                 Should be a positive integer.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    A tensor with the same format as the input with the data either intact
    (if kernel_size == 1) or padded (if kernel_size > 1).
  """
  pad_total = kernel_size - 1
  pad_beg = pad_total // 2
  pad_end = pad_total - pad_beg

  if data_format == 'channels_first':
    padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],
                                    [pad_beg, pad_end], [pad_beg, pad_end]])
  else:
    padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
                                    [pad_beg, pad_end], [0, 0]])
  return padded_inputs

class Conv2DFixedPadding(Layer):
    """ Definition for a convolution layer. 
        Strided 2-D convolution with explicit padding.

        Args:
        Args:
            name: name of the module.
            inputs: the input symbol.
            start: layer number for begining. 
            end: layer number for ending.
            weights: If need to initialize with some parameters, supply the numpy pickle path. 
                     We will read the exact same layer name.    
            data_format: typically 'channel_first'
            kernel_size: Size of each filter. Typically 3.
            filterS: Number of output channels required.
            strides: Stride of convolution.            
    """     
    def __init__ (self, name, inputs, filters, kernel_size, strides, data_format, start=None,
                  end=None, weights=None, weight_scope=None, fake=False):
        super(Conv2DFixedPadding, self).__init__(name = name, start=start, end=end)
        self.fake = fake
        if not self.fake:
            if weights is not None:
                params_name = weight_scope + '/' + str(name) + '/conv2d/'
                np_dict = load_pkl_obj(weights)
                kernel_np = np_dict[params_name+'kernel:0']
                in_shp = inputs.shape.as_list()[1]
                if not kernel_np.shape[2] == in_shp:
                    kernel_np = np.resize(kernel_np, (kernel_size, kernel_size, in_shp, filters))
                kernel_initializer = tf.constant_initializer(kernel_np)
            else:
                kernel_initializer = tf.variance_scaling_initializer()
            with tf.variable_scope(self._name):
                if strides > 1:
                    inputs = _fixed_padding(inputs, kernel_size, data_format)

                self.output=tf.layers.conv2d(inputs=inputs, 
                                            filters=filters, 
                                            kernel_size=kernel_size, 
                                            strides=strides,
                                            padding=('SAME' if strides == 1 else 'VALID'), 
                                            use_bias=False,
                                            kernel_initializer=kernel_initializer,
                                            data_format=data_format)
            self._tf_name = self.output.name.split('/')[0] + '/' + self.output.name.split('/')[1]
        else:
            assert isinstance(inputs, Fake)
            in_shp = inputs.shape
            param_shp = (kernel_size, kernel_size, in_shp[1], filters)
            self.param = Fake(param_shp)
            if kernel_size > 1:
                out_height = int(np.floor(((in_shp[2] - kernel_size  + 2)/strides )) + 1)
                out_width = int(np.floor(((in_shp[3] - kernel_size  + 2)/strides )) + 1)
            else:
                out_height = in_shp[2] / strides
                out_width = in_shp[3] / strides
            out_shp = (None, filters, out_height, out_width)
            self.output = Fake(out_shp)
            self._tf_name = 'fake'
        self.description.append('Conv')
        self.description.append(filters)
        self.description.append(kernel_size)
        self.description.append(strides)
        self.description.append(('SAME' if strides == 1 else 'VALID'))
        self.description.append(self.get_memory_footprint())

    def _get_params_real(self):
        """ Returns the kernel node """ 
        return {self.get_name(): get_param_from_name(self._tf_name + '/conv2d/kernel:0')}

    def _get_memory_footprint_real(self):
        """ Number of parameters in the layer """ 
        params = self.get_params()[self.get_name()]
        return int(np.prod(params.shape))

    def _get_params_fake(self):
        return self.param
    
    def _get_memory_footprint_fake(self):
        return int(np.prod(self.get_params().shape))
    
    def get_memory_footprint(self):
        if self.fake:
            return self._get_memory_footprint_fake()
        else:
            return self._get_memory_footprint_real()
    
    def get_params(self):
        if self.fake:
            return self._get_params_fake()
        else:
            return self._get_params_real()
Pads the input along the spatial dimensions independently of input size.    Args:     inputs: A tensor of size [batch, channels, height_in, width_in] or       [batch, height_in, width_in, channels] depending on data_format.     kernel_size: The kernel to be used in the conv2d or max_pool2d operation.                  Should be a positive integer.     data_format: The input format ('channels_last' or 'channels_first').    Returns:     A tensor with the same format as the input with the data either intact     (if kernel_size == 1) or padded (if kernel_size > 1). Definition for a convolution layer.          Strided 2-D convolution with explicit padding.          Args:         Args:             name: name of the module.             inputs: the input symbol.             start: layer number for begining.              end: layer number for ending.             weights: If need to initialize with some parameters, supply the numpy pickle path.                       We will read the exact same layer name.                 data_format: typically 'channel_first'             kernel_size: Size of each filter. Typically 3.             filterS: Number of output channels required.             strides: Stride of convolution. Returns the kernel node Number of parameters in the layer