# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================

"""bprop primitives"""
from mindspore.ops import _constants
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from mindspore.ops.composite import multitype_ops as C
from mindspore.ops.composite.multitype_ops.zeros_like_impl import zeros_like
from mindspore.ops._grad_experimental.grad_base import bprops
from mindspore.common import dtype as mstype

get_dtype = P.DType()
# Unused parameters are placeholders.


@bprops.register(_constants.kScalarPow)
def bprop_scalar_pow(x, y, out, dout):
    """Backpropagator for primitive `scalar_pow`."""
    return dout * (y * (x ** (y - 1))), dout * (F.scalar_log(x) * out)


@bprops.register("scalar_exp")
def bprop_scalar_exp(x, out, dout):
    """Backpropagator for primitive `scalar_exp`."""
    return (dout * out,)


@bprops.register(_constants.kScalarUadd)
def bprop_scalar_uadd(x, out, dout):
    """Backpropagator for primitive `scalar_uadd`."""
    return (dout,)


@bprops.register(_constants.kScalarUsub)
def bprop_scalar_usub(x, out, dout):
    """Backpropagator for primitive `scalar_usub`."""
    return (-dout,)


@bprops.register("ScalarLog")
def bprop_scalar_log(x, out, dout):
    """Backpropagator for primitive `scalar_usub`."""
    return (dout * (1 / x),)


@bprops.register("scalar_cast")
def bprop_scalar_cast(x, t, out, dout):
    """Backpropagator for primitive `scalar_cast`."""
    return F.scalar_cast(dout, F.typeof(x)), t


@bprops.register(_constants.kTupleGetItem)
def bprop_tuple_getitem(data, idx, out, dout):
    """Backpropagator for primitive `tuple_getitem`."""
    return F.tuple_setitem(zeros_like(data), idx, dout), zeros_like(idx)


@bprops.register("list_getitem")
def bprop_list_getitem(data, idx, out, dout):
    """Backpropagator for primitive `list_getitem`."""
    return F.list_setitem(zeros_like(data), idx, dout), zeros_like(idx)


@bprops.register("dict_getitem")
def bprop_dict_getitem(data, key, out, dout):
    """Backpropagator for primitive `dict_getitem`."""
    return F.dict_setitem(C.zeros_like(data), key, dout), key


@bprops.register("identity")
def METHOD_NAME(x, out, dout):
    """Backpropagator for primitive `identity`."""
    return (dout,)


@bprops.register("array_to_scalar")
def bprop_array_to_scalar(x, out, dout):
    """Backpropagator for primitive `array_to_scalar`."""
    if isinstance(dout, int):
        ret = F.scalar_to_tensor(dout, mstype.int32)
    else:
        ret = F.scalar_to_tensor(dout)
    return (ret,)


@bprops.register("reshape")
def bprop_reshape(xs, shp, out, dout):
    """Backpropagator for primitive `reshape`."""
    return F.reshape(dout, F.shape(xs)), C.zeros_like(shp)


@bprops.register("distribute")
def bprop_distribute(arr, shp, out, dout):
    """Backpropagator for primitive `distribute`."""
    return F.array_reduce(F.scalar_add, dout, F.shape(arr)), C.zeros_like(shp)


@bprops.register("shape")
def bprop_shape(arr, out, dout):
    """Backpropagator for primitive `shape`."""
    return (C.zeros_like(arr),)


@bprops.register("broadcast_shape")
def bprop_broadcast_shape(shp1, shp2, out, dout):
    """Backpropagator for primitive `broadcast_shape`."""
    return C.zeros_like(shp1), C.zeros_like(shp2)


@bprops.register("array_reduce")
def bprop_array_reduce(fn, x, shp, out, dout):
    """Backpropagator for primitive `array_reduce`."""
    return F.distribute(dout, F.shape(x)), C.zeros_like(shp)


@bprops.register("Depend")
def bprop_depend(x, y, out, dout):
    """Backpropagator for primitive `depend`."""
    return dout, C.zeros_like(y)


@bprops.register("embed")
def bprop_embed(x, out, dout):
    """Backpropagator for primitive `embed`."""
    return (C.zeros_like(x),)


@bprops.register("Switch")
def bprop_switch(cond, tb, fb, out, dout):
    """Backpropagator for primitive `switch`."""
    return C.zeros_like(cond), F.switch(cond, dout, C.zeros_like(tb)), \
           F.switch(cond, C.zeros_like(fb), dout)


def _fprop_switch_layer(index, layers):
    """Backpropagator for primitive `switch_layer`."""

    def _bprop_switch_layer(dout):
        return dout, C.zeros_like(index), ()

    return F.switch_layer(index, layers), _bprop_switch_layer


@bprops.register("Load")
def bprop_load(param, u_monad, out, dout):
    """Backpropagator for primitive `load`."""
    return dout, C.zeros_like(u_monad)


@bprops.register("scalar_ne")
@bprops.register("bool_and")
@bprops.register("bool_or")
@bprops.register("bit_xor")
@bprops.register("bit_left_shift")
@bprops.register("bit_right_shift")
@bprops.register("string_eq")
@bprops.register("string_lt")
@bprops.register("string_gt")
@bprops.register("string_le")
@bprops.register("string_ge")
@bprops.register("string_in")
@bprops.register("string_concat")
@bprops.register("string_mul")
@bprops.register("string_getitem")
def bprop_scalar_calc(x, y, out, dout):
    """Backpropagator for scalar calculation."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("bool_not")
@bprops.register("string_not")
def bprop_scalar_not(x, out, dout):
    """Backpropagator for primitive `bool_not` and `string_not`."""
    return (C.zeros_like(x),)


@bprops.register("TensorMove")
def bprop_tensor_move(x, out, dout):
    """Backpropagator for primitive `TensorMove`."""
    return (dout,)