# pylint: disable=too-many-instance-attributes, too-many-arguments, protected-access, too-many-branches
# pylint: disable=too-many-public-methods
"""A `Module` implement the `BaseModule` API by wrapping a `Symbol` and one or
more `Executor` for data parallelization.
"""

import logging
import warnings

from .. import context as ctx
from .. import ndarray as nd
from .. import optimizer as opt

from .executor_group import DataParallelExecutorGroup
from ..model import _create_kvstore, _initialize_kvstore, _update_params, _update_params_on_kvstore
from ..model import load_checkpoint
from ..initializer import Uniform, InitDesc

from .base_module import BaseModule, _check_input_names, _parse_data_desc


class Module(BaseModule):
    """Module is a basic module that wrap a `Symbol`. It is functionally the same
    as the `FeedForward` model, except under the module API.

    Parameters
    ----------
    symbol : Symbol
    data_names : list of str
        Defaults to `('data')` for a typical model used in image classification.
    label_names : list of str
        Defaults to `('softmax_label')` for a typical model used in image
        classification.
    logger : Logger
        Defaults to `logging`.
    context : Context or list of Context
        Defaults to ``mx.cpu()``.
    work_load_list : list of number
        Default ``None``, indicating uniform workload.
    fixed_param_names: list of str
        Default ``None``, indicating no network parameters are fixed.
    state_names : list of str
        states are similar to data and label, but not provided by data iterator.
        Instead they are initialized to 0 and can be set by set_states()
    """
    def __init__(self, symbol, data_names=('data',), label_names=('softmax_label',),
                 logger=logging, context=ctx.cpu(), work_load_list=None,
                 fixed_param_names=None, state_names=None):
        super(Module, self).__init__(logger=logger)

        if isinstance(context, ctx.Context):
            context = [context]
        self._context = context
        if work_load_list is None:
            work_load_list = [1] * len(self._context)
        assert len(work_load_list) == len(self._context)
        self._work_load_list = work_load_list

        self._symbol = symbol

        data_names = list(data_names) if data_names is not None else []
        label_names = list(label_names) if label_names is not None else []
        state_names = list(state_names) if state_names is not None else []
        fixed_param_names = list(fixed_param_names) if fixed_param_names is not None else []

        _check_input_names(symbol, data_names, "data", True)
        _check_input_names(symbol, label_names, "label", False)
        _check_input_names(symbol, state_names, "state", True)
        _check_input_names(symbol, fixed_param_names, "fixed_param", True)

        arg_names = symbol.list_arguments()
        input_names = data_names + label_names + state_names
        self._param_names = [x for x in arg_names if x not in input_names]
        self._fixed_param_names = fixed_param_names
        self._aux_names = symbol.list_auxiliary_states()
        self._data_names = data_names
        self._label_names = label_names
        self._state_names = state_names
        self._output_names = symbol.list_outputs()

        self._arg_params = None
        self._aux_params = None
        self._params_dirty = False

        self._optimizer = None
        self._kvstore = None
        self._update_on_kvstore = None
        self._updater = None
        self._preload_opt_states = None
        self._grad_req = None

        self._exec_group = None
        self._data_shapes = None
        self._label_shapes = None

    @staticmethod
    def load(prefix, epoch, load_optimizer_states=False, **kwargs):
        """Create a model from previously saved checkpoint.

        Parameters
        ----------
        prefix : str
            path prefix of saved model files. You should have
            "prefix-symbol.json", "prefix-xxxx.params", and
            optionally "prefix-xxxx.states", where xxxx is the
            epoch number.
        epoch : int
            epoch to load.
        load_optimizer_states : bool
            whether to load optimizer states. Checkpoint needs
            to have been made with save_optimizer_states=True.
        data_names : list of str
            Default is `('data')` for a typical model used in image classification.
        label_names : list of str
            Default is `('softmax_label')` for a typical model used in image
            classification.
        logger : Logger
            Default is `logging`.
        context : Context or list of Context
            Default is ``cpu()``.
        work_load_list : list of number
            Default ``None``, indicating uniform workload.
        fixed_param_names: list of str
            Default ``None``, indicating no network parameters are fixed.
        """
        sym, args, auxs = load_checkpoint(prefix, epoch)
        mod = Module(symbol=sym, **kwargs)
        mod._arg_params = args
        mod._aux_params = auxs
        mod.params_initialized = True
        if load_optimizer_states:
            mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
        return mod

    def save_checkpoint(self, prefix, epoch, save_optimizer_states=False):
        """Save current progress to checkpoint.
        Use mx.callback.module_checkpoint as epoch_end_callback to save during training.

        Parameters
        ----------
        prefix : str
            The file prefix to checkpoint to
        epoch : int
            The current epoch number
        save_optimizer_states : bool
            Whether to save optimizer states for continue training
        """
        self._symbol.save('%s-symbol.json'%prefix)
        param_name = '%s-%04d.params' % (prefix, epoch)
        self.save_params(param_name)
        logging.info('Saved checkpoint to \"%s\"', param_name)
        if save_optimizer_states:
            state_name = '%s-%04d.states' % (prefix, epoch)
            self.save_optimizer_states(state_name)
            logging.info('Saved optimizer state to \"%s\"', state_name)

    def _reset_bind(self):
        """Internal function to reset binded state."""
        self.binded = False
        self._exec_group = None
        self._data_shapes = None
        self._label_shapes = None

    @property
    def data_names(self):
        """A list of names for data required by this module."""
        return self._data_names

    @property
    def label_names(self):
        """A list of names for labels required by this module."""
        return self._label_names

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

    @property
    def data_shapes(self):
        """Get data shapes.

        Returns
        -------
        A list of `(name, shape)` pairs.
        """
        assert self.binded
        return self._data_shapes

    @property
    def label_shapes(self):
        """Get label shapes.

        Returns
        -------
            A list of `(name, shape)` pairs. The return value could be ``None`` if
            the module does not need labels, or if the module is not bound for
            training (in this case, label information is not available).
        """
        assert self.binded
        return self._label_shapes

    @property
    def output_shapes(self):
        """Get output shapes.

        Returns
        -------
        A list of `(name, shape)` pairs.
        """
        assert self.binded
        return self._exec_group.get_output_shapes()

    def get_params(self):
        """Get current parameters.
        Returns
        -------
        `(arg_params, aux_params)`, each a dictionary of name to parameters (in
        `NDArray`) mapping.
        """
        assert self.binded and self.params_initialized

        if self._params_dirty:
            self._sync_params_from_devices()
        return (self._arg_params, self._aux_params)

    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.
        """
        if self.params_initialized and not force_init:
            warnings.warn("Parameters already initialized and force_init=False. "
                          "init_params call ignored.", stacklevel=2)
            return
        assert self.binded, 'call bind before initializing the parameters'

        def _impl(name, arr, cache):
            """Internal helper for parameter initialization"""
            if cache is not None:
                if name in cache:
                    cache_arr = cache[name]

                    # just in case the cached array is just the target itself
                    if cache_arr is not arr:
                        cache_arr.copyto(arr)
                else:
                    if not allow_missing:
                        raise RuntimeError("%s is not presented" % name)
                    if initializer is not None:
                        initializer(name, arr)
            else:
                initializer(name, arr)

        attrs = self._symbol.attr_dict()
        for name, arr in self._arg_params.items():
            desc = InitDesc(name, attrs.get(name, None))
            _impl(desc, arr, arg_params)

        for name, arr in self._aux_params.items():
            desc = InitDesc(name, attrs.get(name, None))
            _impl(desc, arr, aux_params)

        self.params_initialized = True
        self._params_dirty = False

        # copy the initialized parameters to devices
        self._exec_group.set_params(self._arg_params, self._aux_params)

    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 `NDArray`.
        aux_params : dict
            Dictionary of name to `NDArray`.
        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.

        Examples
        --------
        An example of setting module parameters::
            >>> sym, arg_params, aux_params = \
            mx.model.load_checkpoint(model_prefix, n_epoch_load)
            >>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
        """
        if not allow_missing:
            self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
                             allow_missing=allow_missing, force_init=force_init)
            return

        if self.params_initialized and not force_init:
            warnings.warn("Parameters already initialized and force_init=False. "
                          "set_params call ignored.", stacklevel=2)
            return

        self._exec_group.set_params(arg_params, aux_params)

        # because we didn't update self._arg_params, they are dirty now.
        self._params_dirty = True
        self.params_initialized = True

    def bind(self, data_shapes, label_shapes=None, for_training=True,
             inputs_need_grad=False, force_rebind=False, shared_module=None,
             grad_req='write'):
        """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 bound 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
            bound. 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).
        """
        # force rebinding is typically used when one want to switch from
        # training to prediction phase.
        if force_rebind:
            self._reset_bind()

        if self.binded:
            self.logger.warning('Already bound, ignoring bind()')
            return

        self.for_training = for_training
        self.inputs_need_grad = inputs_need_grad
        self.binded = True
        self._grad_req = grad_req

        if not for_training:
            assert not inputs_need_grad
        else:
            pass
            # this is not True, as some module might not contains a loss function
            # that consumes the labels
            # assert label_shapes is not None

        self._data_shapes, self._label_shapes = _parse_data_desc(
            self.data_names, self.label_names, data_shapes, label_shapes)

        if shared_module is not None:
            assert isinstance(shared_module, Module) and \
                    shared_module.binded and shared_module.params_initialized
            shared_group = shared_module._exec_group
        else:
            shared_group = None

        self._exec_group = DataParallelExecutorGroup(self._symbol, self._context,
                                                     self._work_load_list, self._data_shapes,
                                                     self._label_shapes, self._param_names,
                                                     for_training, inputs_need_grad,
                                                     shared_group, logger=self.logger,
                                                     fixed_param_names=self._fixed_param_names,
                                                     grad_req=grad_req,
                                                     state_names=self._state_names)
        self._total_exec_bytes = self._exec_group._total_exec_bytes
        if shared_module is not None:
            self.params_initialized = True
            self._arg_params = shared_module._arg_params
            self._aux_params = shared_module._aux_params
        elif self.params_initialized:
            # if the parameters are already initialized, we are re-binding
            # so automatically copy the already initialized params
            self._exec_group.set_params(self._arg_params, self._aux_params)
        else:
            assert self._arg_params is None and self._aux_params is None
            param_arrays = [
                nd.zeros(x[0].shape, dtype=x[0].dtype)
                for x in self._exec_group.param_arrays
            ]
            self._arg_params = {name:arr for name, arr in zip(self._param_names, param_arrays)}

            aux_arrays = [
                nd.zeros(x[0].shape, dtype=x[0].dtype)
                for x in self._exec_group.aux_arrays
            ]
            self._aux_params = {name:arr for name, arr in zip(self._aux_names, aux_arrays)}

        if shared_module is not None and shared_module.optimizer_initialized:
            self.borrow_optimizer(shared_module)


    def reshape(self, data_shapes, label_shapes=None):
        """Reshape the module for new input shapes.

        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``.
        """
        assert self.binded
        self._data_shapes, self._label_shapes = _parse_data_desc(
            self.data_names, self.label_names, data_shapes, label_shapes)

        self._exec_group.reshape(self._data_shapes, self._label_shapes)

    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.
        """
        assert self.binded and self.params_initialized

        if self.optimizer_initialized and not force_init:
            self.logger.warning('optimizer already initialized, ignoring...')
            return

        if self._params_dirty:
            self._sync_params_from_devices()
        (kvstore, update_on_kvstore) = \
                _create_kvstore(kvstore, len(self._context), self._arg_params)

        batch_size = self._exec_group.batch_size
        if kvstore and 'dist' in kvstore.type and '_sync' in kvstore.type:
            batch_size *= kvstore.num_workers
        rescale_grad = 1.0/batch_size

        if isinstance(optimizer, str):
            idx2name = {}
            if update_on_kvstore:
                idx2name.update(enumerate(self._exec_group.param_names))
            else:
                for k in range(len(self._context)):
                    idx2name.update({i*len(self._context)+k: n
                                     for i, n in enumerate(self._exec_group.param_names)})
            optimizer_params = dict(optimizer_params)
            if 'rescale_grad' not in optimizer_params:
                optimizer_params['rescale_grad'] = rescale_grad
            optimizer = opt.create(optimizer,
                                   sym=self.symbol, param_idx2name=idx2name,
                                   **optimizer_params)
        else:
            assert isinstance(optimizer, opt.Optimizer)
            if optimizer.rescale_grad != rescale_grad:
                #pylint: disable=no-member
                warnings.warn(
                    "Optimizer created manually outside Module but rescale_grad " +
                    "is not normalized to 1.0/batch_size/num_workers (%s vs. %s). "%(
                        optimizer.rescale_grad, rescale_grad) +
                    "Is this intended?", stacklevel=2)

        self._optimizer = optimizer
        self._kvstore = kvstore
        self._update_on_kvstore = update_on_kvstore
        self._updater = None

        if kvstore:
            # copy initialized local parameters to kvstore
            _initialize_kvstore(kvstore=kvstore,
                                param_arrays=self._exec_group.param_arrays,
                                arg_params=self._arg_params,
                                param_names=self._param_names,
                                update_on_kvstore=update_on_kvstore)
        if update_on_kvstore:
            kvstore.set_optimizer(self._optimizer)
        else:
            self._updater = opt.get_updater(optimizer)

        self.optimizer_initialized = True

        if self._preload_opt_states is not None:
            self.load_optimizer_states(self._preload_opt_states)
            self._preload_opt_states = None

    def borrow_optimizer(self, shared_module):
        """Borrow optimizer from a shared module. Used in bucketing, where exactly the same
        optimizer (esp. kvstore) is used.

        Parameters
        ----------
        shared_module : Module
        """
        assert shared_module.optimizer_initialized
        self._optimizer = shared_module._optimizer
        self._kvstore = shared_module._kvstore
        self._update_on_kvstore = shared_module._update_on_kvstore
        self._updater = shared_module._updater
        self.optimizer_initialized = True

    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``.
        """
        assert self.binded and self.params_initialized
        self._exec_group.forward(data_batch, is_train)

    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.
        """
        assert self.binded and self.params_initialized
        self._exec_group.backward(out_grads=out_grads)

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

        self._params_dirty = True
        if self._update_on_kvstore:
            _update_params_on_kvstore(self._exec_group.param_arrays,
                                      self._exec_group.grad_arrays,
                                      self._kvstore)
        else:
            _update_params(self._exec_group.param_arrays,
                           self._exec_group.grad_arrays,
                           updater=self._updater,
                           num_device=len(self._context),
                           kvstore=self._kvstore)

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

        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.

        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
        -------
        list of NDArray or list of list of NDArray
            Output.
        """
        assert self.binded and self.params_initialized
        return self._exec_group.get_outputs(merge_multi_context=merge_multi_context)

    def get_input_grads(self, merge_multi_context=True):
        """Get the gradients with respect to the inputs of the module.

        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`.

        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
        -------
        list of NDArray or list of list of NDArray
              Input gradients
        """
        assert self.binded and self.params_initialized and self.inputs_need_grad
        return self._exec_group.get_input_grads(merge_multi_context=merge_multi_context)

    def get_states(self, merge_multi_context=True):
        """Get states from all devices

        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`.

        Parameters
        ----------
        merge_multi_context : bool
            Default is ``True``. In the case when data-parallelism is used, the states
            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
        -------
        list of NDArray or list of list of NDArray
            States
        """
        assert self.binded and self.params_initialized
        return self._exec_group.get_states(merge_multi_context=merge_multi_context)

    def set_states(self, states=None, value=None):
        """Set value for states. Only one of states & value can be specified.

        Parameters
        ----------
        states : list of list of NDArrays
            source states arrays formatted like ``[[state1_dev1, state1_dev2],
            [state2_dev1, state2_dev2]]``.
        value : number
            a single scalar value for all state arrays.
        """
        assert self.binded and self.params_initialized
        self._exec_group.set_states(states, value)

    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``.
        """
        self._exec_group.update_metric(eval_metric, labels)

    def _sync_params_from_devices(self):
        """Synchronize parameters from devices to CPU. This function should be called after
        calling `update` that updates the parameters on the devices, before one can read the
        latest parameters from ``self._arg_params`` and ``self._aux_params``.
        """
        self._exec_group.get_params(self._arg_params, self._aux_params)
        self._params_dirty = False

    def save_optimizer_states(self, fname):
        """Save optimizer (updater) state to file

        Parameters
        ----------
        fname : str
            Path to output states file.
        """
        assert self.optimizer_initialized

        if self._update_on_kvstore:
            self._kvstore.save_optimizer_states(fname)
        else:
            with open(fname, 'wb') as fout:
                fout.write(self._updater.get_states())

    def load_optimizer_states(self, fname):
        """Load optimizer (updater) state from file

        Parameters
        ----------
        fname : str
            Path to input states file.
        """
        assert self.optimizer_initialized

        if self._update_on_kvstore:
            self._kvstore.load_optimizer_states(fname)
        else:
            self._updater.set_states(open(fname, 'rb').read())

    def install_monitor(self, mon):
        """ Install monitor on all executors """
        assert self.binded
        self._exec_group.install_monitor(mon)