# Copyright 2018 The Texar Authors. All Rights Reserved.
#
# 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.
"""
Various classifier classes.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# pylint: disable=not-context-manager, too-many-arguments, too-many-locals

import tensorflow as tf

from texar.tf.utils.exceptions import TexarError
from texar.tf.modules.classifiers.classifier_base import ClassifierBase
from texar.tf.modules.encoders.conv_encoders import Conv1DEncoder
from texar.tf.utils import utils
from texar.tf.hyperparams import HParams

__all__ = [
    "Conv1DClassifier"
]

class Conv1DClassifier(ClassifierBase):
    """Simple Conv-1D classifier.
    This is a combination of the
    :class:`~texar.tf.modules.Conv1DEncoder` with a classification layer.

    Args:
        hparams (dict, optional): Hyperparameters. Missing
            hyperparamerter will be set to default values. See
            :meth:`default_hparams` for the hyperparameter sturcture and
            default values.

    Example:

        .. code-block:: python

            clas = Conv1DClassifier(hparams={'num_classes': 10})

            inputs = tf.random_uniform([64, 20, 256])
            logits, pred = clas(inputs)
            # logits == Tensor of shape [64, 10]
            # pred   == Tensor of shape [64]

    .. document private functions
    .. automethod:: _build
    """

    def __init__(self, hparams=None):
        ClassifierBase.__init__(self, hparams)

        with tf.variable_scope(self.variable_scope):
            encoder_hparams = utils.dict_fetch(
                hparams, Conv1DEncoder.default_hparams())
            self._encoder = Conv1DEncoder(hparams=encoder_hparams)

            # Add an additional dense layer if needed
            self._num_classes = self._hparams.num_classes
            if self._num_classes > 0:
                if self._hparams.num_dense_layers <= 0:
                    self._encoder.append_layer({"type": "Flatten"})

                logit_kwargs = self._hparams.logit_layer_kwargs
                if logit_kwargs is None:
                    logit_kwargs = {}
                elif not isinstance(logit_kwargs, HParams):
                    raise ValueError(
                        "hparams['logit_layer_kwargs'] must be a dict.")
                else:
                    logit_kwargs = logit_kwargs.todict()
                logit_kwargs.update({"units": self._num_classes})
                if 'name' not in logit_kwargs:
                    logit_kwargs['name'] = "logit_layer"

                self._encoder.append_layer(
                    {"type": "Dense", "kwargs": logit_kwargs})

    @staticmethod
    def default_hparams():
        """Returns a dictionary of hyperparameters with default values.

        .. code-block:: python

            {
                # (1) Same hyperparameters as in Conv1DEncoder
                ...

                # (2) Additional hyperparameters
                "num_classes": 2,
                "logit_layer_kwargs": {
                    "use_bias": False
                },
                "name": "conv1d_classifier"
            }

        Here:

        1. Same hyperparameters as in :class:`~texar.tf.modules.Conv1DEncoder`.
        See the :meth:`~texar.tf.modules.Conv1DEncoder.default_hparams`.
        An instance of Conv1DEncoder is created for feature extraction.

        2. Additional hyperparameters:

            "num_classes": int
                Number of classes:

                - If **`> 0`**, an additional :tf_main:`Dense <layers/Dense>` \
                layer is appended to the encoder to compute the logits over \
                classes.
                - If **`<= 0`**, no dense layer is appended. The number of \
                classes is assumed to be the final dense layer size of the \
                encoder.

            "logit_layer_kwargs": dict
                Keyword arguments for the logit Dense layer constructor,
                except for argument "units" which is set to "num_classes".
                Ignored if no extra logit layer is appended.

            "name": str
                Name of the classifier.
        """
        hparams = Conv1DEncoder.default_hparams()
        hparams.update({
            "name": "conv1d_classifier",
            "num_classes": 2, #set to <=0 to avoid appending output layer
            "logit_layer_kwargs": {"use_bias": False}
        })
        return hparams

    def _build(self,    # pylint: disable=arguments-differ
               inputs,
               sequence_length=None,
               dtype=None,
               mode=None):
        """Feeds the inputs through the network and makes classification.

        The arguments are the same as in :class:`~texar.tf.modules.Conv1DEncoder`.

        The predictions of binary classification ("num_classes"=1) and
        multi-way classification ("num_classes">1) are different, as explained
        below.

        Args:
            inputs: The inputs to the network, which is a 3D tensor. See
                :class:`~texar.tf.modules.Conv1DEncoder` for more details.
            sequence_length (optional): An int tensor of shape `[batch_size]`
                containing the length of each element in :attr:`inputs`.
                If given, time steps beyond the length will first be masked out
                before feeding to the layers.
            dtype (optional): Type of the inputs. If not provided, infers
                from inputs automatically.
            mode (optional): A tensor taking value in
                :tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`, including
                `TRAIN`, `EVAL`, and `PREDICT`. If `None`,
                :func:`texar.tf.global_mode` is used.

        Returns:
            A tuple `(logits, pred)`, where

            - **`logits`** is a Tensor of shape `[batch_size, num_classes]`\
            for `num_classes` >1, and `[batch_size]` for `num_classes` =1 \
            (i.e., binary classification).
            - **`pred`** is the prediction, a Tensor of shape `[batch_size]` \
            and type `tf.int64`. For binary classification, the standard \
            sigmoid function is used for prediction, and the class labels are \
            `{0, 1}`.
        """
        logits = self._encoder(inputs, sequence_length, dtype, mode)

        num_classes = self._hparams.num_classes
        is_binary = num_classes == 1
        is_binary = is_binary or (num_classes <= 0 and logits.shape[1] == 1)

        if is_binary:
            pred = tf.greater(logits, 0)
            logits = tf.reshape(logits, [-1])
        else:
            pred = tf.argmax(logits, 1)
        pred = tf.cast(tf.reshape(pred, [-1]), tf.int64)

        self._built = True

        return logits, pred

    @property
    def trainable_variables(self):
        """The list of trainable variables of the module.
        """
        if not self._built:
            raise TexarError(
                "Attempting to access trainable_variables before module %s "
                "was fully built. The module is built once it is called, "
                "e.g., with `%s(...)`" % (self.name, self.name))
        return self._encoder.trainable_variables

    @property
    def num_classes(self):
        """The number of classes.
        """
        return self._num_classes

    @property
    def nn(self): # pylint: disable=invalid-name
        """The classifier neural network.
        """
        return self._encoder

    def has_layer(self, layer_name):
        """Returns `True` if the network with the name exists. Returns `False`
        otherwise.

        Args:
            layer_name (str): Name of the layer.
        """
        return self._encoder.has_layer(layer_name)

    def layer_by_name(self, layer_name):
        """Returns the layer with the name. Returns 'None' if the layer name
        does not exist.

        Args:
            layer_name (str): Name of the layer.
        """
        return self._encoder.layer_by_name(layer_name)

    @property
    def layers_by_name(self):
        """A dictionary mapping layer names to the layers.
        """
        return self._encoder.layers_by_name

    @property
    def layers(self):
        """A list of the layers.
        """
        return self._encoder.layers

    @property
    def layer_names(self):
        """A list of uniquified layer names.
        """
        return self._encoder.layer_names

    def layer_outputs_by_name(self, layer_name):
        """Returns the output tensors of the layer with the specified name.
        Returns `None` if the layer name does not exist.

        Args:
            layer_name (str): Name of the layer.
        """
        return self._encoder.layer_outputs_by_name(layer_name)

    @property
    def layer_outputs(self):
        """A list containing output tensors of each layer.
        """
        return self._encoder.layer_outputs

tensorflow.argmax tensorflow.variable_scope tensorflow.greater tensorflow.reshape