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  # distributed with this work for additional information
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  # to you 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.
  # pylint: disable=invalid-name,unused-argument
  """Generic nn operators"""
  from __future__ import absolute_import as _abs
  import tvm
  from .. import cpp
  
  def _default_schedule(outs, auto_inline):
      """Default schedule for llvm."""
      target = tvm.target.current_target(allow_none=False)
      outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
      if target.target_name not in ("llvm", "c"):
          raise RuntimeError("schedule not registered for '%s'" % target)
      s = tvm.create_schedule([x.op for x in outs])
      if auto_inline:
          x = outs[0]
          tvm.schedule.AutoInlineInjective(s)
          s[x].fuse(s[x].op.axis)
      return s
  
  
  @tvm.target.generic_func
  def schedule_conv2d_hwcn(outs):
      """Schedule for conv2d_hwcn
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of conv2d_hwcn
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_conv2d_nchw(outs):
      """Schedule for conv2d_nchw
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of conv2d_nchw
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_conv2d_nhwc_pack(outs):
      """Schedule for conv2d_nhwc_pack
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of conv2d_nhwc_pack
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_conv2d_nhwc(outs):
      """Schedule for conv2d_nhwc
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of conv2d_nchw
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_conv2d_NCHWc(outs):
      """Schedule for conv2d_NCHW[x]c
  
      Parameters
      ----------
      outs : Array of Tensor
          The computation graph description of conv2d_NCHWc
          in the format of an array of tensors.
          The number of filter, i.e., the output channel.
  
      Returns
      -------
      sch : Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_conv2d_NCHWc_int8(outs):
      """Schedule for conv2d_NCHW[x]c_int8
  
      Parameters
      ----------
      outs : Array of Tensor
          The computation graph description of conv2d_NCHWc_int8
          in the format of an array of tensors.
          The number of filter, i.e., the output channel.
  
      Returns
      -------
      sch : Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_conv2d_winograd_weight_transform(outs):
      """Schedule for weight transformation of winograd
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of this operator
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      # Typically this is computed in nnvm PreCompute pass
      # so we make a schedule here for cpu llvm
      s = tvm.create_schedule([x.op for x in outs])
      output = outs[0]
      _, G = s[output].op.input_tensors
      s[G].compute_inline()
      eps, nu, co, ci = s[output].op.axis
      r_kh, r_kw = s[output].op.reduce_axis
      s[output].reorder(co, ci, r_kh, r_kw, eps, nu)
      for axis in [r_kh, r_kw, eps, nu]:
          s[output].unroll(axis)
      s[output].parallel(co)
      return s
  
  
  @tvm.target.generic_func
  def schedule_conv2d_winograd_without_weight_transform(outs):
      """Schedule for winograd without weight transformation
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of this operator
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_conv2d_winograd_nnpack_weight_transform(outs):
      """Schedule for weight transformation of winograd
       Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of this operator
            in the format of an array of tensors.
       Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      # Typically this is computed in nnvm PreCompute pass
      s = tvm.create_schedule([x.op for x in outs])
      return s
  
  @tvm.target.generic_func
  def schedule_conv2d_winograd_nnpack_without_weight_transform(outs):
      """Schedule for winograd without weight transformation
       Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of this operator
            in the format of an array of tensors.
       Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_conv2d_transpose_nchw(outs):
      """Schedule for conv2d_transpose_nchw
  
      Parameters
      ----------
      outs: Array of Tensor
          The computation graph description of conv2d_transpose_nchw
          in the format of an array of tensors.
  
      Returns
      -------
      s: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_depthwise_conv2d_nchw(outs):
      """Schedule for depthwise_conv2d_nchw
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of depthwise_conv2d_nchw
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_depthwise_conv2d_nhwc(outs):
      """Schedule for depthwise_conv2d_nhwc
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of depthwise_conv2d_nhwc
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_depthwise_conv2d_NCHWc(outs):
      """Schedule for depthwise_conv2d_NCHWc
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of depthwise_conv2d_nhwc
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_group_conv2d_nchw(outs):
      """Schedule for group_conv2d_nchw
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of group_conv2d_nchw
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_deformable_conv2d_nchw(outs):
      """Schedule for deformable_conv2d_nchw
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of deformable_conv2d_nchw
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_bitserial_conv2d_nchw(outs):
      """Schedule for bitserial_conv2d_nchw
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of bitserial_conv2d_nchw
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_bitserial_conv2d_nhwc(outs):
      """Schedule for bitserial_conv2d_nhwc
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of bitserial_conv2d_nchw
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_bitserial_dense(outs):
      """Schedule for bitserial_dense
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of bitserial_dense
            in the format of an array of tensors.
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.override_native_generic_func("schedule_reduce")
  def schedule_reduce(outs):
      """Schedule for reduction
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of reduce
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, True)
  
  
  @tvm.target.override_native_generic_func("schedule_softmax")
  def schedule_softmax(outs):
      """Schedule for softmax
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of softmax
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.override_native_generic_func("schedule_dense")
  def schedule_dense(outs):
      """Schedule for dense
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of dense
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.override_native_generic_func("schedule_pool")
  def schedule_pool(outs, layout):
      """Schedule for pool
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of pool
            in the format of an array of tensors.
  
      layout: str
          Data layout.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_pool_grad(outs):
      """Schedule for pool_grad
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of pool
            in the format of an array of tensors.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.override_native_generic_func("schedule_adaptive_pool")
  def schedule_adaptive_pool(outs):
      """Schedule for adaptive pool
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of adaptive pool
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.override_native_generic_func("schedule_binarize_pack")
  def schedule_binarize_pack(outs):
      """Schedule for binarize_pack
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of binarize_pack
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.override_native_generic_func("schedule_bitpack")
  def schedule_bitpack(outs):
      """Schedule for bitpack
      Parameters
      ----------
      outs: Array of Tensor
          The computation graph description of bitpack
          in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.override_native_generic_func("schedule_binary_dense")
  def schedule_binary_dense(outs):
      """Schedule for binary_dense
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of binary_dense
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  
  @tvm.target.generic_func
  def schedule_lrn(outs):
      """Schedule for lrn
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of lrn
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      target = tvm.target.current_target(allow_none=False)
      cpp_target = cpp.TEST_create_target(target.target_name)
      return cpp.generic.default_schedule(cpp_target, outs, False)
  
  @tvm.target.generic_func
  def schedule_l2_normalize(outs):
      """Schedule for l2 normalize
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of l2 normalize
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      target = tvm.target.current_target(allow_none=False)
      cpp_target = cpp.TEST_create_target(target.target_name)
      return cpp.generic.default_schedule(cpp_target, outs, False)
  
  @tvm.target.generic_func
  def schedule_sparse_dense(outs):
      """Schedule for sparse_dense
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of sparse_dense
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  @tvm.target.generic_func
  def schedule_sparse_transpose(outs):
      """Schedule for sparse_transpose
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of sparse_transpose
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      return _default_schedule(outs, False)
  
  @tvm.target.generic_func
  def schedule_batch_matmul(outs):
      target = tvm.target.current_target(allow_none=False)
      cpp_target = cpp.TEST_create_target(target.target_name)
      return cpp.generic.default_schedule(cpp_target, outs, False)
  