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# coding: utf-8
"""Autograd for NDArray."""
from __future__ import absolute_import
from __future__ import division

from array import array
from threading import Lock
import traceback
import ctypes
from ctypes import c_int, c_void_p, CFUNCTYPE, POINTER, cast
from .base import _LIB, check_call, string_types, mx_uint
from .base import NDArrayHandle, c_array, c_handle_array, c_array_buf, MXCallbackList, SymbolHandle
from .ndarray import NDArray, _ndarray_cls
from .ndarray import _GRAD_REQ_MAP
from .symbol import Symbol


def set_recording(is_recording): #pylint: disable=redefined-outer-name
    """Set status to recording/not recording. When recording, graph will be constructed
    for gradient computation.

    Parameters
    ----------
    is_recording: bool

    Returns
    -------
    previous state before this set.
    """
    prev = ctypes.c_int()
    check_call(_LIB.MXAutogradSetIsRecording(
        ctypes.c_int(is_recording), ctypes.byref(prev)))
    return bool(prev.value)

def set_training(train_mode): #pylint: disable=redefined-outer-name
    """Set status to training/predicting. This affects ctx.is_train in operator
    running context. For example, Dropout will drop inputs randomly when
    train_mode=True while simply passing through if train_mode=False.

    Parameters
    ----------
    train_mode: bool

    Returns
    -------
    previous state before this set.
    """
    prev = ctypes.c_int()
    check_call(_LIB.MXAutogradSetIsTraining(
        ctypes.c_int(train_mode), ctypes.byref(prev)))
    return bool(prev.value)

def is_recording():
    """Get status on recording/not recording.

    Returns
    -------
    Current state of recording.
    """
    curr = ctypes.c_bool()
    check_call(_LIB.MXAutogradIsRecording(ctypes.byref(curr)))
    return curr.value

def is_training():
    """Get status on training/predicting.

    Returns
    -------
    Current state of training/predicting.
    """
    curr = ctypes.c_bool()
    check_call(_LIB.MXAutogradIsTraining(ctypes.byref(curr)))
    return curr.value


class _RecordingStateScope(object):
    """Scope for managing training state.

    Example::

        with _RecordingStateScope(True, True):
            y = model(x)
            backward([y])

    """
    def __init__(self, is_record, train_mode): #pylint: disable=redefined-outer-name
        self._enter_is_record = is_record
        self._enter_train_mode = train_mode
        self._prev_is_record = None
        self._prev_train_mode = None

    def __enter__(self):
        if self._enter_is_record is not None:
            self._prev_is_record = set_recording(self._enter_is_record)
        if self._enter_train_mode is not None:
            self._prev_train_mode = set_training(self._enter_train_mode)

    def __exit__(self, ptype, value, trace):
        if self._enter_is_record is not None and self._prev_is_record != self._enter_is_record:
            set_recording(self._prev_is_record)
        if self._enter_train_mode is not None and self._prev_train_mode != self._enter_train_mode:
            set_training(self._prev_train_mode)


def record(train_mode=True): #pylint: disable=redefined-outer-name
    """Returns an autograd recording scope context to be used in 'with' statement
    and captures code that needs gradients to be calculated.

    .. note:: When forwarding with train_mode=False, the corresponding backward
              should also use train_mode=False, otherwise gradient is undefined.

    Example::

        with autograd.record():
            y = model(x)
            backward([y])
        metric.update(...)
        optim.step(...)

    Parameters
    ----------
    train_mode: bool, default True
        Whether the forward pass is in training or predicting mode. This controls the behavior
        of some layers such as Dropout, BatchNorm.
    """
    return _RecordingStateScope(True, train_mode)


def pause(train_mode=False): #pylint: disable=redefined-outer-name
    """Returns a scope context to be used in 'with' statement for codes that do not need
    gradients to be calculated.

    Example::

        with autograd.record():
            y = model(x)
            backward([y])
            with autograd.pause():
                # testing, IO, gradient updates...

    Parameters
    ----------
    train_mode: bool, default False
        Whether to do forward for training or predicting.
    """
    return _RecordingStateScope(False, train_mode)


def train_mode():
    """Returns a scope context to be used in 'with' statement
    in which forward pass behavior is set to training mode,
    without changing the recording states.

    Example::

        y = model(x)
        with autograd.train_mode():
            y = dropout(y)

    """
    return _RecordingStateScope(None, True)


def predict_mode():
    """Returns a scope context to be used in 'with' statement
    in which forward pass behavior is set to inference mode,
    without changing the recording states.

    Example::

        with autograd.record():
            y = model(x)
            with autograd.predict_mode():
                y = sampling(y)
            backward([y])
    """
    return _RecordingStateScope(None, False)


def mark_variables(variables, gradients, grad_reqs='write'):
    """Mark NDArrays as variables to compute gradient for autograd.

    Parameters
    ----------
    variables: NDArray or list of NDArray
    gradients: NDArray or list of NDArray
    grad_reqs: str or list of str
    """
    if isinstance(variables, NDArray):
        assert isinstance(gradients, NDArray)
        variables = [variables]
        gradients = [gradients]

    if isinstance(grad_reqs, string_types):
        grad_reqs = [_GRAD_REQ_MAP[grad_reqs]]*len(variables)
    else:
        grad_reqs = [_GRAD_REQ_MAP[i] for i in grad_reqs]

    check_call(_LIB.MXAutogradMarkVariables(
        len(variables),
        c_handle_array(variables),
        c_array_buf(mx_uint, array('I', grad_reqs)),
        c_handle_array(gradients)))


def _parse_head(heads, head_grads):
    """parse head gradient for backward and grad."""
    if isinstance(heads, NDArray):
        heads = [heads]
    if isinstance(head_grads, NDArray):
        head_grads = [head_grads]

    head_handles = c_handle_array(heads)

    if head_grads is None:
        hgrad_handles = ctypes.c_void_p(0)
    else:
        assert len(heads) == len(head_grads), \
            "heads and head_grads must be lists of the same length"
        hgrad_handles = c_array(NDArrayHandle,
                                [i.handle if i is not None else NDArrayHandle(0)
                                 for i in head_grads])
    return head_handles, hgrad_handles


def backward(heads, head_grads=None, retain_graph=False, train_mode=True): #pylint: disable=redefined-outer-name
    """Compute the gradients of heads w.r.t previously marked variables.

    Parameters
    ----------
    heads: NDArray or list of NDArray
        Output NDArray(s)
    head_grads: NDArray or list of NDArray or None
        Gradients with respect to heads.
    train_mode: bool, optional
        Whether to do backward for training or predicting.
    """
    head_handles, hgrad_handles = _parse_head(heads, head_grads)

    check_call(_LIB.MXAutogradBackwardEx(
        len(head_handles),
        head_handles,
        hgrad_handles,
        0,
        ctypes.c_void_p(0),
        ctypes.c_int(retain_graph),
        ctypes.c_int(0),
        ctypes.c_int(train_mode),
        ctypes.c_void_p(0),
        ctypes.c_void_p(0)))


def grad(heads, variables, head_grads=None, retain_graph=None, create_graph=False,
         train_mode=True):  #pylint: disable=redefined-outer-name
    """Compute the gradients of heads w.r.t variables. Gradients will be
    returned as new NDArrays instead of stored into `variable.grad`.
    Supports recording gradient graph for computing higher order gradients.

    .. Note: Currently only a very limited set of operators support higher order
    gradients.

    Parameters
    ----------
    heads: NDArray or list of NDArray
        Output NDArray(s)
    variables: NDArray or list of NDArray
        Input variables to compute gradients for.
    head_grads: NDArray or list of NDArray or None
        Gradients with respect to heads.
    retain_graph: bool
        Whether to keep computation graph to differentiate again, instead
        of clearing history and release memory. Defaults to the same value
        as create_graph.
    create_graph: bool
        Whether to record gradient graph for computing higher order
    train_mode: bool, optional
        Whether to do backward for training or prediction.

    Returns
    -------
    NDArray or list of NDArray:
        Gradients with respect to variables.

    Examples
    --------
    >>> x = mx.nd.ones((1,))
    >>> x.attach_grad()
    >>> with mx.autograd.record():
    ...     z = mx.nd.elemwise_add(mx.nd.exp(x), x)
    >>> dx = mx.autograd.grad(z, [x], create_graph=True)
    >>> dx.backward()
    >>> print(dx.grad)
    [
    [ 3.71828175]
    <NDArray 1 @cpu(0)>]
    """
    head_handles, hgrad_handles = _parse_head(heads, head_grads)

    if isinstance(variables, NDArray):
        variables = [variables]
    else:
        assert len(variables), "variables cannot be an empty list."
    var_handles = c_handle_array(variables)

    retain_graph = retain_graph if retain_graph is not None else create_graph
    grad_vars = ctypes.POINTER(NDArrayHandle)()
    grad_stypes = ctypes.POINTER(ctypes.c_int)()

    check_call(_LIB.MXAutogradBackwardEx(
        len(head_handles),
        head_handles,
        hgrad_handles,
        len(var_handles),
        var_handles,
        ctypes.c_int(retain_graph),
        ctypes.c_int(create_graph),
        ctypes.c_int(train_mode),
        ctypes.byref(grad_vars),
        ctypes.byref(grad_stypes)))

    ret = [_ndarray_cls(ctypes.cast(grad_vars[i], NDArrayHandle),
                        stype=grad_stypes[i])
           for i in range(len(var_handles))]
    if isinstance(variables, NDArray):
        return ret[0]
    return ret


def get_symbol(x):
    """Retrieve recorded computation history as `Symbol`.

    Parameters
    ----------
    x : NDArray
        Array representing the head of computation graph.

    Returns
    -------
    Symbol
        The retrieved Symbol.
    """
    hdl = SymbolHandle()
    check_call(_LIB.MXAutogradGetSymbol(x.handle, ctypes.byref(hdl)))
    return Symbol(hdl)


class Function(object):
    """User-defined differentiable function.

    Function allows defining both forward and backward computation for
    custom operators. During gradient computation, the used-defined
    backward function will be used instead of the default chain-rule.
    You can also cast to numpy array and back for some operations in
    forward and backward.

    For example, a stable sigmoid function can be defined as::

        class sigmoid(mx.autograd.Function):
            def forward(self, x):
                y = 1 / (1 + mx.nd.exp(-x))
                self.save_for_backward(y)
                return y

            def backward(self, dy):
                # backward takes as many inputs as forward's return value,
                # and returns as many NDArrays as forward's arguments.
                y, = self.saved_tensors
                return y * (1-y)

    Then, the function can be used in the following way::

        func = sigmoid()
        x = mx.nd.random.uniform(shape=(10,))
        x.attach_grad()

        with mx.autograd.record():
            m = func(x)
            m.backward()
        dx = x.grad.asnumpy()

    """
    _bwd_functype = CFUNCTYPE(c_int, c_int, c_int, POINTER(c_void_p),
                              POINTER(c_int), c_int, c_void_p)
    _del_functype = CFUNCTYPE(c_int, c_void_p)
    class _Registry(object):
        """CustomOp registry."""
        def __init__(self):
            self.ref_holder = {}
            self.counter = 0
            self.lock = Lock()

        def inc(self):
            """Get index for new entry."""
            self.lock.acquire()
            cur = self.counter
            self.counter += 1
            self.lock.release()
            return cur

    _registry = _Registry()

    def __init__(self):
        self._used = False
        self.saved_tensors = ()

    def save_for_backward(self, *args):
        self.saved_tensors = args

    def __call__(self, *inputs):
        assert not self._used, \
            "Each Function instance can only be called once. "\
            "Please create another instance."
        self._used = True

        prev_recording = set_recording(False)
        outputs = self.forward(*inputs)
        set_recording(prev_recording)

        if not prev_recording:
            return outputs

        ret_outputs = outputs
        if isinstance(outputs, NDArray):
            outputs = (outputs,)

        key = Function._registry.inc()

        def backward_entry(num_ograds, num_igrads, ptrs, reqs, is_train, _):
            """entry point for backward."""
            # pylint: disable=W0613
            try:
                output_grads = [NDArray(ctypes.cast(i, NDArrayHandle), writable=False) \
                                for i in ptrs[:num_ograds]]
                input_grads = [NDArray(ctypes.cast(i, NDArrayHandle), writable=True) \
                               for i in ptrs[num_ograds:num_ograds+num_igrads]]
                reqs = [reqs[i] for i in range(num_igrads)]
                rets = self.backward(*output_grads)
                if isinstance(rets, NDArray):
                    rets = (rets,)
                assert len(rets) == len(input_grads), \
                    "%s.backward must return exactly the same number " \
                    "of NDArrays as the number of NDArrays arguments to forward." \
                    "Expecting %d got %d"%(self.__class__.name, len(input_grads), len(rets))
                for igrad, ret, req in zip(input_grads, rets, reqs):
                    assert isinstance(ret, NDArray), \
                        "autograd.Function.backward must return NDArrays, not %s"%type(ret)
                    if req == 0:  # null
                        return
                    elif req == 1 or req == 2:  # write or inplace
                        igrad[:] = ret
                    elif req == 'add':
                        igrad[:] += ret
            except Exception:  # pylint: disable=broad-except
                print('Error in Function.backward: %s' % traceback.format_exc())
                return False
            return True

        def delete_entry(_):
            """C Callback for CustomFunction::delete"""
            try:
                del Function._registry.ref_holder[key]
            except Exception:  # pylint: disable=broad-except
                print('Error in autograd.Function.delete: %s' % traceback.format_exc())
                return False
            return True

        callbacks = [Function._bwd_functype(backward_entry),
                     Function._del_functype(delete_entry)]
        callbacks = [cast(i, CFUNCTYPE(c_int)) for i in callbacks]
        context = MXCallbackList(c_int(len(callbacks)),
                                 cast(c_array(CFUNCTYPE(c_int), callbacks),
                                      POINTER(CFUNCTYPE(c_int))),
                                 cast(c_array(c_void_p, [None]*len(callbacks)),
                                      POINTER(c_void_p)))
        check_call(_LIB.MXCustomFunctionRecord(
            c_int(len(inputs)),
            c_handle_array(inputs),
            c_int(len(outputs)),
            c_handle_array(outputs),
            ctypes.byref(context)))

        Function._registry.ref_holder[key] = context

        return ret_outputs

    def forward(self, *inputs):
        """Forward computation."""
        raise NotImplementedError

    def backward(self, *output_grads):
        """Backward computation.

        Takes as many inputs as forward's outputs,
        and returns as many NDArrays as forward's inputs.
        """
        raise NotImplementedError
