def test_get_all_elections_xlsx(self):
        response = self.client.get(reverse('results-export'))

        self.assertEqual(response.status_code, 200)

        self.assertEqual(
            response['Content-Disposition'],
            'attachment; filename="Election Results.xlsx"'
        )

        wb = openpyxl.load_workbook(io.BytesIO(response.content))

        self.assertEqual(len(wb.worksheets), 2)

        # Check first worksheet.
        ws = wb.worksheets[0]

        self.assertEqual(wb.sheetnames[0], 'Election 0')

        row_count = ws.max_row
        col_count = ws.max_column
        self.assertEqual(row_count, 25)
        self.assertEqual(col_count, 5)

        self.assertEqual(str(ws.cell(1, 1).value), 'Election 0 Results')

        self.assertEqual(str(ws.cell(2, 1).value), 'Candidates')

        cellContents = [
            'Position 0',
            'Party 0',
            '0, 0',
            'Party 1',
            '3, 3',
            'Party 2',
            'None',
            'Position 1',
            'Party 0',
            '1, 1',
            'Party 1',
            '4, 4',
            'Party 2',
            'None',
            'Position 2',
            'Party 0',
            '2, 2',
            'Party 1',
            '5, 5',
            'Party 2',
            'None'
        ]
        for cellIndex, content in enumerate(cellContents, 5):
            self.assertEqual(str(ws.cell(cellIndex, 1).value), content) 

        self.assertEqual(str(ws.cell(2, 2).value), 'Number of Votes')

        self.assertEqual(str(ws.cell(3, 2).value), '0')

        self.assertEqual(str(ws.cell(4, 2).value), '0') # Section

        self.assertEqual(str(ws.cell(7, 2).value), '1')
        self.assertEqual(str(ws.cell(9, 2).value), '0')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 2).value), '2')
        self.assertEqual(str(ws.cell(16, 2).value), '0')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 2).value), '0')
        self.assertEqual(str(ws.cell(23, 2).value), '0')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(4, 3).value), '1') # Section

        self.assertEqual(str(ws.cell(7, 3).value), '0')
        self.assertEqual(str(ws.cell(9, 3).value), '1')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 3).value), '0')
        self.assertEqual(str(ws.cell(16, 3).value), '0')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 3).value), '1')
        self.assertEqual(str(ws.cell(23, 3).value), '0')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(3, 4).value), '1')

        self.assertEqual(str(ws.cell(4, 4).value), '2') # Section

        self.assertEqual(str(ws.cell(7, 4).value), '0')
        self.assertEqual(str(ws.cell(9, 4).value), '0')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 4).value), '0')
        self.assertEqual(str(ws.cell(16, 4).value), '1')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 4).value), '0')
        self.assertEqual(str(ws.cell(23, 4).value), '1')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(3, 5).value), 'Total Votes')
        self.assertEqual(str(ws.cell(7, 5).value), '1')
        self.assertEqual(str(ws.cell(9, 5).value), '1')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 5).value), '2')
        self.assertEqual(str(ws.cell(16, 5).value), '1')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 5).value), '1')
        self.assertEqual(str(ws.cell(23, 5).value), '1')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        # Check second worksheet.
        ws = wb.worksheets[1]

        self.assertEqual(wb.sheetnames[1], 'Election 1')

        row_count = ws.max_row
        col_count = ws.max_column
        self.assertEqual(row_count, 25)
        self.assertEqual(col_count, 5)

        self.assertEqual(str(ws.cell(1, 1).value), 'Election 1 Results')

        self.assertEqual(str(ws.cell(2, 1).value), 'Candidates')

        self.assertEqual(str(ws.cell(2, 1).value), 'Candidates')

        cellContents = [
            'Position 3',
            'Party 3',
            '6, 6',
            'Party 4',
            '9, 9',
            'Party 5',
            'None',
            'Position 4',
            'Party 3',
            '7, 7',
            'Party 4',
            '10, 10',
            'Party 5',
            'None',
            'Position 5',
            'Party 3',
            '8, 8',
            'Party 4',
            '11, 11',
            'Party 5',
            'None'
        ]
        for cellIndex, content in enumerate(cellContents, 5):
            self.assertEqual(str(ws.cell(cellIndex, 1).value), content) 

        self.assertEqual(str(ws.cell(2, 2).value), 'Number of Votes')

        self.assertEqual(str(ws.cell(3, 2).value), '2')

        self.assertEqual(str(ws.cell(4, 2).value), '3') # Section

        self.assertEqual(str(ws.cell(7, 2).value), '1')
        self.assertEqual(str(ws.cell(9, 2).value), '0')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 2).value), '1')
        self.assertEqual(str(ws.cell(16, 2).value), '0')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 2).value), '0')
        self.assertEqual(str(ws.cell(23, 2).value), '0')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(4, 3).value), '4') # Section

        self.assertEqual(str(ws.cell(7, 3).value), '0')
        self.assertEqual(str(ws.cell(9, 3).value), '1')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 3).value), '0')
        self.assertEqual(str(ws.cell(16, 3).value), '0')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 3).value), '1')
        self.assertEqual(str(ws.cell(23, 3).value), '0')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(3, 4).value), '3')

        self.assertEqual(str(ws.cell(4, 4).value), '5') # Section

        self.assertEqual(str(ws.cell(7, 4).value), '0')
        self.assertEqual(str(ws.cell(9, 4).value), '0')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 4).value), '0')
        self.assertEqual(str(ws.cell(16, 4).value), '1')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 4).value), '0')
        self.assertEqual(str(ws.cell(23, 4).value), '1')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(3, 5).value), 'Total Votes')
        self.assertEqual(str(ws.cell(7, 5).value), '1')
        self.assertEqual(str(ws.cell(9, 5).value), '1')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 5).value), '1')
        self.assertEqual(str(ws.cell(16, 5).value), '1')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 5).value), '1')
        self.assertEqual(str(ws.cell(23, 5).value), '1')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')
def __call__(self, inputs, training):
    """Add operations to classify a batch of input images.

    Args:
      inputs: A Tensor representing a batch of input images.
      training: A boolean. Set to True to add operations required only when
        training the classifier.

    Returns:
      A logits Tensor with shape [<batch_size>, self.num_classes].
    """

    with self._model_variable_scope():
      if self.data_format == 'channels_first':
        # Convert the inputs from channels_last (NHWC) to channels_first (NCHW).
        # This provides a large performance boost on GPU. See
        # https://www.tensorflow.org/performance/performance_guide#data_formats
        inputs = tf.transpose(a=inputs, perm=[0, 3, 1, 2])

      inputs = conv2d_fixed_padding(
          inputs=inputs,
          filters=self.num_filters,
          kernel_size=self.kernel_size,
          strides=self.conv_stride,
          data_format=self.data_format,
          name='initial_input')
      inputs = tf.identity(inputs, 'initial_conv')

      # We do not include batch normalization or activation functions in V2
      # for the initial conv1 because the first ResNet unit will perform these
      # for both the shortcut and non-shortcut paths as part of the first
      # block's projection. Cf. Appendix of [2].
      if self.resnet_version == 1:
        inputs = batch_norm(inputs, training, self.data_format)
        inputs = tf.nn.relu(inputs)

      if self.first_pool_size:
        inputs = tf.compat.v1.layers.max_pooling2d(
            inputs=inputs,
            pool_size=self.first_pool_size,
            strides=self.first_pool_stride,
            padding='SAME',
            data_format=self.data_format)
        inputs = tf.identity(inputs, 'initial_max_pool')

      for i, num_blocks in enumerate(self.block_sizes):
        # We now have 4 block layers, but the last does not
        # double the number of filters.
        # We also skip the projection shortcut in the first block layer.
        num_filters = self.num_filters * min((2**i), 4)
        shortcut = i != 0
        inputs = block_layer(
            inputs=inputs,
            filters=num_filters,
            bottleneck=self.bottleneck,
            block_fn=self.block_fn,
            blocks=num_blocks,
            strides=self.block_strides[i],
            training=training,
            name='block_layer{}'.format(i + 1),
            data_format=self.data_format,
            shortcut=shortcut)

      # Skip the last BN+relu.
      # Only apply the BN and ReLU for model that does pre_activation in each
      # building/bottleneck block, eg resnet V2.
      # if self.pre_activation:
      #   inputs = batch_norm(inputs, training, self.data_format,
      #     name='pre_act'+'batch_norm')
      #   inputs = tf.nn.relu(inputs,name='pre_act'+'relu')

      # The current top layer has shape
      # `batch_size x pool_size x pool_size x final_size`.
      # ResNet does an Average Pooling layer over pool_size,
      # but that is the same as doing a reduce_mean. We do a reduce_mean
      # here because it performs better than AveragePooling2D.
      # Also perform max-pooling, and concat results.
      axes = [2, 3] if self.data_format == 'channels_first' else [1, 2]
      avg_pooled = tf.reduce_mean(input_tensor=inputs, axis=axes, keepdims=True)
      avg_pooled = tf.squeeze(avg_pooled, axes)
      max_pooled = tf.reduce_max(input_tensor=inputs, axis=axes, keepdims=True)
      max_pooled = tf.squeeze(max_pooled, axes)
      inputs = tf.concat([avg_pooled, max_pooled], axis=1)
      inputs = tf.identity(inputs, 'final_pooling')

      inputs = tf.compat.v1.layers.dense(
          inputs=inputs, units=self.num_classes, reuse=tf.AUTO_REUSE)
      inputs = tf.identity(inputs, 'final_dense')
      return inputs