# Copyright (c) 2016-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################

## @package fc
# Module caffe2.python.layers.fc
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin
import math
import numpy as np


class FC(SamplingTrainableMixin, ModelLayer):

    def __init__(self, model, input_record, output_dims, weight_init=None,
                 bias_init=None, weight_optim=None, bias_optim=None, name='fc',
                 **kwargs):
        super(FC, self).__init__(model, name, input_record, **kwargs)
        assert isinstance(input_record, schema.Scalar), "Incorrect input type"
        assert len(input_record.field_types()[0].shape) > 0, (
            "FC expects limited dimensions of the input tensor")

        input_dims = input_record.field_types()[0].shape[0]
        assert input_dims > 0, (
            "FC expects input dimensions > 0, got {}".format(input_dims))

        scale = math.sqrt(1.0 / input_dims)
        weight_init = weight_init if weight_init else (
            'UniformFill', {'min': -scale, 'max': scale})
        bias_init = bias_init if bias_init else (
            'UniformFill', {'min': -scale, 'max': scale})

        self.w = self.create_param(param_name='w',
                                   shape=[output_dims, input_dims],
                                   initializer=weight_init,
                                   optimizer=weight_optim)

        self.b = self.create_param(param_name='b',
                                   shape=[output_dims, ],
                                   initializer=bias_init,
                                   optimizer=bias_optim)

        self.output_schema = schema.Scalar(
            (np.float32, (output_dims, )),
            self.get_next_blob_reference('output')
        )

    def _add_ops(self, net, params):
        net.FC(self.input_record.field_blobs() + params,
               self.output_schema.field_blobs(), **self.kwargs)

    @property
    def param_blobs(self):
        return [self.w, self.b]
