# pylint: disable=too-many-arguments, too-many-locals, too-many-public-methods, too-many-branches
"""`BaseModule` defines an API for modules."""

import logging
import time

from .. import metric
from .. import ndarray

from ..model import BatchEndParam
from ..initializer import Uniform

def _as_list(obj):
    """A utility function that treat the argument as a list.

    Parameters
    ----------
    obj : object

    Returns
    -------
    If `obj` is a list, return it. Otherwise, return `[obj]` as a single-element list.
    """
    if isinstance(obj, list):
        return obj
    else:
        return [obj]


class BaseModule(object):
    """The base class of a modules. A module represents a computation component. The design
    purpose of a module is that it abstract a computation "machine", that one can run forward,
    backward, update parameters, etc. We aim to make the APIs easy to use, especially in the
    case when we need to use imperative API to work with multiple modules (e.g. stochastic
    depth network).

    A module has several states:

    - Initial state. Memory is not allocated yet, not ready for computation yet.
    - Binded. Shapes for inputs, outputs, and parameters are all known, memory allocated,
      ready for computation.
    - Parameter initialized. For modules with parameters, doing computation before initializing
      the parameters might result in undefined outputs.
    - Optimizer installed. An optimizer can be installed to a module. After this, the parameters
      of the module can be updated according to the optimizer after gradients are computed
      (forward-backward).

    In order for a module to interactive with others, a module should be able to report the
    following information in its raw stage (before binded)

    - `data_names`: list of string indicating the names of required data.
    - `output_names`: list of string indicating the names of required outputs.

    And also the following richer information after binded:

    - state information
        - `binded`: `bool`, indicating whether the memory buffers needed for computation
          has been allocated.
        - `for_training`: whether the module is binded for training (if binded).
        - `params_initialized`: `bool`, indicating whether the parameters of this modules
          has been initialized.
        - `optimizer_initialized`: 'bool`, indicating whether an optimizer is defined
          and initialized.
        - `inputs_need_grad`: `bool`, indicating whether gradients with respect to the
          input data is needed. Might be useful when implementing composition of modules.

    - input/output information
        - `data_shapes`: a list of `(name, shape)`. In theory, since the memory is allocated,
          we could directly provide the data arrays. But in the case of data parallelization,
          the data arrays might not be of the same shape as viewed from the external world.
        - `label_shapes`: a list of `(name, shape)`. This might be `[]` if the module does
          not need labels (e.g. it does not contains a loss function at the top), or a module
          is not binded for training.
        - `output_shapes`: a list of `(name, shape)` for outputs of the module.

    - parameters (for modules with parameters)
        - `get_params()`: return a tuple `(arg_params, aux_params)`. Each of those
          is a dictionary of name to `NDArray` mapping. Those `NDArray` always lives on
          CPU. The actual parameters used for computing might live on other devices (GPUs),
          this function will retrieve (a copy of) the latest parameters. Therefore, modifying
        - `set_params(arg_params, aux_params)`: assign parameters to the devices
          doing the computation.
        - `init_params(...)`: a more flexible interface to assign or initialize the parameters.

    - setup
        - `bind()`: prepare environment for computation.
        - `init_optimizer()`: install optimizer for parameter updating.

    - computation
        - `forward(data_batch)`: forward operation.
        - `backward(out_grads=None)`: backward operation.
        - `update()`: update parameters according to installed optimizer.
        - `get_outputs()`: get outputs of the previous forward operation.
        - `get_input_grads()`: get the gradients with respect to the inputs computed
          in the previous backward operation.
        - `update_metric(metric, labels)`: update performance metric for the previous forward
           computed results.

    - other properties (mostly for backward compatability)
        - `symbol`: the underlying symbolic graph for this module (if any)
          This property is not necessarily constant. For example, for `BucketingModule`,
          this property is simply the *current* symbol being used. For other modules,
          this value might not be well defined.

    When those intermediate-level API are implemented properly, the following
    high-level API will be automatically available for a module:

    - `fit`: train the module parameters on a data set
    - `predict`: run prediction on a data set and collect outputs
    - `score`: run prediction on a data set and evaluate performance
    """
    def __init__(self, logger=logging):
        self.logger = logger
        self.binded = False
        self.for_training = False
        self.inputs_need_grad = False
        self.params_initialized = False
        self.optimizer_initialized = False
        self._symbol = None

    ################################################################################
    # High Level API
    ################################################################################
    def forward_backward(self, data_batch):
        """A convenient function that calls both `forward` and `backward`.
        """
        self.forward(data_batch, is_train=True)
        self.backward()

    def score(self, eval_data, eval_metric, num_batch=None, batch_end_callback=None,
              reset=True, epoch=0):
        """Run prediction on `eval_data` and evaluate the performance according to
        `eval_metric`.

        Parameters
        ----------
        eval_data : DataIter
        eval_metric : EvalMetric
        num_batch : int
            Number of batches to run. Default is `None`, indicating run until the `DataIter`
            finishes.
        batch_end_callback : function
            Could also be a list of functions.
        reset : bool
            Default `True`, indicating whether we should reset `eval_data` before starting
            evaluating.
        epoch : int
            Default 0. For compatibility, this will be passed to callbacks (if any). During
            training, this will correspond to the training epoch number.
        """
        assert self.binded and self.params_initialized

        if reset:
            eval_data.reset()

        if not isinstance(eval_metric, metric.EvalMetric):
            eval_metric = metric.create(eval_metric)

        eval_metric.reset()
        for nbatch, eval_batch in enumerate(eval_data):
            if num_batch is not None and nbatch == num_batch:
                break

            self.forward(eval_batch, is_train=False)
            self.update_metric(eval_metric, eval_batch.label)

            if batch_end_callback is not None:
                batch_end_params = BatchEndParam(epoch=epoch,
                                                 nbatch=nbatch,
                                                 eval_metric=eval_metric,
                                                 locals=locals())
                for callback in _as_list(batch_end_callback):
                    callback(batch_end_params)
        return eval_metric.get_name_value()

    def iter_predict(self, eval_data, num_batch=None, reset=True):
        """Iterate over predictions.

            for pred, i_batch, batch in module.iter_predict(eval_data):
                # pred is a list of outputs from the module
                # i_batch is a integer
                # batch is the data batch from the data iterator

        Parameters
        ----------
        eval_data : DataIter
        num_batch : int
            Default is `None`, indicating running all the batches in the data iterator.
        reset : bool
            Default is `True`, indicating whether we should reset the data iter before start
            doing prediction.
        """
        assert self.binded and self.params_initialized

        if reset:
            eval_data.reset()

        for nbatch, eval_batch in enumerate(eval_data):
            if num_batch is not None and nbatch == num_batch:
                break
            self.forward(eval_batch, is_train=False)
            pad = eval_batch.pad
            outputs = [out[0:out.shape[0]-pad] for out in self.get_outputs()]

            yield (outputs, nbatch, eval_batch)

    def predict(self, eval_data, num_batch=None, merge_batches=True, reset=True,
                always_output_list=False):
        """Run prediction and collect the outputs.

        Parameters
        ----------
        eval_data : DataIter
        num_batch : int
            Default is `None`, indicating running all the batches in the data iterator.
        merge_batches : bool
            Default is `True`, see the doc for return values.
        reset : bool
            Default is `True`, indicating whether we should reset the data iter before start
            doing prediction.
        always_output_list : bool
            Default is `False`, see the doc for return values.

        Returns
        -------
        When `merge_batches` is `True` (by default), the return value will be a list
        `[out1, out2, out3]`.  Where each element is concatenation of the outputs for
        all the mini-batches. If further that `always_output_list` is `False` (by default),
        then in the case of a single output, `out1` is returned instead of `[out1]`.

        When `merge_batches` is `False`, the return value will be a nested list like
        `[[out1_batch1, out2_batch1], [out1_batch2], ...]`. This mode is useful because
        in some cases (e.g. bucketing), the module does not necessarily produce the same
        number of outputs.

        The objects in the results are `NDArray`s. If you need to work with numpy array,
        just call `.asnumpy()` on each of the `NDArray`.
        """
        assert self.binded and self.params_initialized

        if reset:
            eval_data.reset()

        output_list = []

        for nbatch, eval_batch in enumerate(eval_data):
            if num_batch is not None and nbatch == num_batch:
                break
            self.forward(eval_batch, is_train=False)
            pad = eval_batch.pad
            outputs = [out[0:out.shape[0]-pad].copy() for out in self.get_outputs()]

            output_list.append(outputs)

        if len(output_list) == 0:
            return output_list

        if merge_batches:
            num_outputs = len(output_list[0])
            for out in output_list:
                assert len(out) == num_outputs, \
                       'Cannot merge batches, as num of outputs is not the same ' + \
                       'in mini-batches. Maybe bucketing is used?'
            output_list2 = [ndarray.concatenate([out[i] for out in output_list])
                            for i in range(num_outputs)]

            if num_outputs == 1 and not always_output_list:
                return output_list2[0]
            return output_list2

        return output_list

    def fit(self, train_data, eval_data=None, eval_metric='acc',
            epoch_end_callback=None, batch_end_callback=None, kvstore='local',
            optimizer='sgd', optimizer_params=(('learning_rate', 0.01),),
            eval_batch_end_callback=None, initializer=Uniform(0.01),
            arg_params=None, aux_params=None, allow_missing=False,
            force_rebind=False, force_init=False, begin_epoch=0, num_epoch=None,
            validation_metric=None, monitor=None):
        """Train the module parameters.

        Parameters
        ----------
        train_data : DataIter
        eval_data : DataIter
            If not `None`, will be used as validation set and evaluate the performance
            after each epoch.
        eval_metric : str or EvalMetric
            Default `'acc'`. The performance measure used to display during training.
        epoch_end_callback : function or list of function
            Each callback will be called with the current `epoch`, `symbol`, `arg_params`
            and `aux_params`.
        batch_end_callback : function or list of function
            Each callback will be called with a `BatchEndParam`.
        kvstore : str or KVStore
            Default `'local'`.
        optimizer : str or Optimizer
            Default `'sgd'`
        optimizer_params : dict
            Default `(('learning_rate', 0.01),)`. The parameters for the optimizer constructor.
            The default value is not a `dict`, just to avoid pylint warning on dangerous
            default values.
        eval_batch_end_callback : function or list of function
        initializer : Initializer
            Will be called to initialize the module parameters if not already initialized.
        arg_params : dict
            Default `None`, if not `None`, should be existing parameters from a trained
            model or loaded from a checkpoint (previously saved model). In this case,
            the value here will be used to initialize the module parameters, unless they
            are already initialized by the user via a call to `init_params` or `fit`.
            `arg_params` has higher priority to `initializer`.
        aux_params : dict
            Default `None`. Similar to `arg_params`, except for auxiliary states.
        allow_missing : bool
            Default `False`. Indicate whether we allow missing parameters when `arg_params`
            and `aux_params` are not `None`. If this is `True`, then the missing parameters
            will be initialized via the `initializer`.
        force_rebind : bool
            Default `False`. Whether to force rebinding the executors if already binded.
        force_init : bool
            Default `False`. Indicate whether we should force initialization even if the
            parameters are already initialized.
        begin_epoch : int
            Default `0`. Indicate the starting epoch. Usually, if we are resuming from a
            checkpoint saved at a previous training phase at epoch N, then we should specify
            this value as N+1.
        num_epoch : int
            Number of epochs to run training.
        """
        assert num_epoch is not None, 'please specify number of epochs'

        self.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label,
                  for_training=True, force_rebind=force_rebind)
        if monitor is not None:
            self.install_monitor(monitor)
        self.init_params(initializer=initializer, arg_params=arg_params, aux_params=aux_params,
                         allow_missing=allow_missing, force_init=force_init)
        self.init_optimizer(kvstore=kvstore, optimizer=optimizer,
                            optimizer_params=optimizer_params)

        if validation_metric is None:
            validation_metric = eval_metric
        if not isinstance(eval_metric, metric.EvalMetric):
            eval_metric = metric.create(eval_metric)

        ################################################################################
        # training loop
        ################################################################################
        for epoch in range(begin_epoch, num_epoch):
            tic = time.time()
            eval_metric.reset()
            for nbatch, data_batch in enumerate(train_data):
                if monitor is not None:
                    monitor.tic()
                self.forward_backward(data_batch)
                self.update()
                self.update_metric(eval_metric, data_batch.label)

                if monitor is not None:
                    monitor.toc_print()

                if batch_end_callback is not None:
                    batch_end_params = BatchEndParam(epoch=epoch, nbatch=nbatch,
                                                     eval_metric=eval_metric,
                                                     locals=locals())
                    for callback in _as_list(batch_end_callback):
                        callback(batch_end_params)

            # one epoch of training is finished
            for name, val in eval_metric.get_name_value():
                self.logger.info('Epoch[%d] Train-%s=%f', epoch, name, val)
            toc = time.time()
            self.logger.info('Epoch[%d] Time cost=%.3f', epoch, (toc-tic))

            if epoch_end_callback is not None:
                arg_params, aux_params = self.get_params()
                for callback in _as_list(epoch_end_callback):
                    callback(epoch, self.symbol, arg_params, aux_params)

            #----------------------------------------
            # evaluation on validation set
            if eval_data:
                res = self.score(eval_data, validation_metric,
                                 batch_end_callback=eval_batch_end_callback, epoch=epoch)
                for name, val in res:
                    self.logger.info('Epoch[%d] Validation-%s=%f', epoch, name, val)

            # end of 1 epoch, reset the data-iter for another epoch
            train_data.reset()

    ################################################################################
    # Symbol information
    ################################################################################
    @property
    def data_names(self):
        """A list of names for data required by this module."""
        raise NotImplementedError()

    @property
    def output_names(self):
        """A list of names for the outputs of this module."""
        raise NotImplementedError()

    ################################################################################
    # Input/Output information
    ################################################################################
    @property
    def data_shapes(self):
        """A list of (name, shape) pairs specifying the data inputs to this module."""
        raise NotImplementedError()

    @property
    def label_shapes(self):
        """A list of (name, shape) pairs specifying the label inputs to this module.
        If this module does not accept labels -- either it is a module without loss
        function, or it is not binded for training, then this should return an empty
        list `[]`.
        """
        raise NotImplementedError()

    @property
    def output_shapes(self):
        """A list of (name, shape) pairs specifying the outputs of this module."""
        raise NotImplementedError()

    ################################################################################
    # Parameters of a module
    ################################################################################
    def get_params(self):
        """Get parameters, those are potentially copies of the the actual parameters used
        to do computation on the device.

        Returns
        -------
        `(arg_params, aux_params)`, a pair of dictionary of name to value mapping.
        """
        raise NotImplementedError()

    def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None,
                    allow_missing=False, force_init=False):
        """Initialize the parameters and auxiliary states.

        Parameters
        ----------
        initializer : Initializer
            Called to initialize parameters if needed.
        arg_params : dict
            If not None, should be a dictionary of existing arg_params. Initialization
            will be copied from that.
        aux_params : dict
            If not None, should be a dictionary of existing aux_params. Initialization
            will be copied from that.
        allow_missing : bool
            If true, params could contain missing values, and the initializer will be
            called to fill those missing params.
        force_init : bool
            If true, will force re-initialize even if already initialized.
        """
        raise NotImplementedError()

    def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
        """Assign parameter and aux state values.

        Parameters
        ----------
        arg_params : dict
            Dictionary of name to value (`NDArray`) mapping.
        aux_params : dict
            Dictionary of name to value (`NDArray`) mapping.
        allow_missing : bool
            If true, params could contain missing values, and the initializer will be
            called to fill those missing params.
        force_init : bool
            If true, will force re-initialize even if already initialized.

        """
        self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
                         allow_missing=allow_missing, force_init=force_init)

    def save_params(self, fname):
        """Save model parameters to file.

        Parameters
        ----------
        fname : str
            Path to output param file.
        """
        arg_params, aux_params = self.get_params()
        save_dict = {('arg:%s' % k) : v for k, v in arg_params.items()}
        save_dict.update({('aux:%s' % k) : v for k, v in aux_params.items()})
        ndarray.save(fname, save_dict)

    def load_params(self, fname):
        """Load model parameters from file.

        Parameters
        ----------
        fname : str
            Path to input param file.
        """
        save_dict = ndarray.load(fname)
        arg_params = {}
        aux_params = {}
        for k, value in save_dict.items():
            arg_type, name = k.split(':', 1)
            if arg_type == 'arg':
                arg_params[name] = value
            elif arg_type == 'aux':
                aux_params[name] = value
            else:
                raise ValueError("Invalid param file " + fname)
        self.set_params(arg_params, aux_params)

    def install_monitor(self, mon):
        """Install monitor on all executors"""
        raise NotImplementedError()

    ################################################################################
    # Computations
    ################################################################################
    def forward(self, data_batch, is_train=None):
        """Forward computation.

        Parameters
        ----------
        data_batch : DataBatch
            Could be anything with similar API implemented.
        is_train : bool
            Default is `None`, which means `is_train` takes the value of `self.for_training`.
        """
        raise NotImplementedError()

    def backward(self, out_grads=None):
        """Backward computation.

        Parameters
        ----------
        out_grads : NDArray or list of NDArray, optional
            Gradient on the outputs to be propagated back.
            This parameter is only needed when bind is called
            on outputs that are not a loss function.
        """
        raise NotImplementedError()

    def get_outputs(self, merge_multi_context=True):
        """Get outputs of the previous forward computation.

        Parameters
        ----------
        merge_multi_context : bool
            Default is `True`. In the case when data-parallelism is used, the outputs
            will be collected from multiple devices. A `True` value indicate that we
            should merge the collected results so that they look like from a single
            executor.

        Returns
        -------
        If `merge_multi_context` is `True`, it is like `[out1, out2]`. Otherwise, it
        is like `[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]`. All the output
        elements are `NDArray`. When `merge_multi_context` is `False`, those `NDArray`
        might live on different devices.
        """
        raise NotImplementedError()

    def get_input_grads(self, merge_multi_context=True):
        """Get the gradients to the inputs, computed in the previous backward computation.

        Parameters
        ----------
        merge_multi_context : bool
            Default is `True`. In the case when data-parallelism is used, the gradients
            will be collected from multiple devices. A `True` value indicate that we
            should merge the collected results so that they look like from a single
            executor.

        Returns
        -------
        If `merge_multi_context` is `True`, it is like `[grad1, grad2]`. Otherwise, it
        is like `[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]`. All the output
        elements are `NDArray`. When `merge_multi_context` is `False`, those `NDArray`
        might live on different devices.
        """
        raise NotImplementedError()

    def update(self):
        """Update parameters according to the installed optimizer and the gradients computed
        in the previous forward-backward batch.
        """
        raise NotImplementedError()

    def update_metric(self, eval_metric, labels):
        """Evaluate and accumulate evaluation metric on outputs of the last forward computation.

        Parameters
        ----------
        eval_metric : EvalMetric
        labels : list of NDArray
            Typically `data_batch.label`.
        """
        raise NotImplementedError()

    ################################################################################
    # module setup
    ################################################################################
    def bind(self, data_shapes, label_shapes=None, for_training=True,
             inputs_need_grad=False, force_rebind=False, shared_module=None):
        """Bind the symbols to construct executors. This is necessary before one
        can perform computation with the module.

        Parameters
        ----------
        data_shapes : list of (str, tuple)
            Typically is `data_iter.provide_data`.
        label_shapes : list of (str, tuple)
            Typically is `data_iter.provide_label`.
        for_training : bool
            Default is `True`. Whether the executors should be bind for training.
        inputs_need_grad : bool
            Default is `False`. Whether the gradients to the input data need to be computed.
            Typically this is not needed. But this might be needed when implementing composition
            of modules.
        force_rebind : bool
            Default is `False`. This function does nothing if the executors are already
            binded. But with this `True`, the executors will be forced to rebind.
        shared_module : Module
            Default is `None`. This is used in bucketing. When not `None`, the shared module
            essentially corresponds to a different bucket -- a module with different symbol
            but with the same sets of parameters (e.g. unrolled RNNs with different lengths).
        """
        raise NotImplementedError()

    def init_optimizer(self, kvstore='local', optimizer='sgd',
                       optimizer_params=(('learning_rate', 0.01),), force_init=False):
        """Install and initialize optimizers.

        Parameters
        ----------
        kvstore : str or KVStore
            Default `'local'`.
        optimizer : str or Optimizer
            Default `'sgd'`
        optimizer_params : dict
            Default `(('learning_rate', 0.01),)`. The default value is not a dictionary,
            just to avoid pylint warning of dangerous default values.
        force_init : bool
            Default `False`, indicating whether we should force re-initializing the
            optimizer in the case an optimizer is already installed.
        """
        raise NotImplementedError()

    ################################################################################
    # misc
    ################################################################################
    @property
    def symbol(self):
        """Get the symbol associated with this module.

        Except for `Module`, for other types of modules (e.g. `BucketingModule`), this
        property might not be a constant throughout its life time. Some modules might
        not even be associated with any symbols.
        """
        return self._symbol
