text: # Copyright 2020 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.
  # ============================================================================
  """thor_ops"""
  from ..primitive import prim_attr_register, PrimitiveWithInfer
  from ...common import dtype as mstype
  
  
  __all__ = ["CusBatchMatMul",
             "CusCholeskyTrsm",
             "CusFusedAbsMax1",
             "CusImg2Col",
             "CusMatMulCubeDenseLeft",
             "CusMatMulCubeFraczRightMul",
             "CusMatMulCube",
             "CusMatrixCombine",
             "CusTranspose02314",
             "CusMatMulCubeDenseRight",
             "CusMatMulCubeFraczLeftCast",
             ]
  
  
  class CusBatchMatMul(PrimitiveWithInfer):
      """
      Multiplies matrix `a` by matrix `b` in batch.
  
      The rank of input tensors must be `3`.
  
      Inputs:
          - **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, D, D)`.
          - **input_y** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(N, D, D)`. If
            `transpose_b` is True.
  
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N, D, D)`.
  
      Examples:
          >>> input_x = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32)
          >>> input_y = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32)
          >>> cus_batch_matmul = P.CusBatchMatMul()
          >>> output = cus_batch_matmul(input_x, input_y)
      """
  
      @prim_attr_register
      def __init__(self):
          """init CusBatchMatMul"""
          self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
          from mindspore.ops._op_impl._custom_op.batch_matmul_impl import CusBatchMatMul
  
      def infer_shape(self, data1_shape, data2_shape):
          return data1_shape
  
      def infer_dtype(self, data1_dtype, data2_dtype):
          return data1_dtype
  
  
  class CusCholeskyTrsm(PrimitiveWithInfer):
      """
      L * LT = A.
      LT * (LT)^-1 = I.
      return (LT)^-1.
      Only compute the res of the diag part of input matrix with dim 128.
      The rank of input tensors must be `2`.
  
      Inputs:
          - **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, N)`.
  
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N // Split_dim, Split_dim, Split_dim)`.
  
      Examples:
          >>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float32)
          >>> cus_choleskytrsm = P.CusCholeskyTrsm()
          >>> output = matmul(input_x)
      """
  
      @prim_attr_register
      def __init__(self):
          """init CusCholeskyTrsm"""
          self.init_prim_io_names(inputs=['x1'], outputs=['y'])
          from mindspore.ops._op_impl._custom_op.cholesky_trsm_impl import CusCholeskyTrsm
  
      def infer_shape(self, data1_shape):
          ll = []
          m, _ = data1_shape
          if m >= 128:
              ll = [m // 128, 128, 128]
          else:
              ll = [1, 64, 64]
          return ll
  
      def infer_dtype(self, data1_dtype):
          return data1_dtype
  
  
  class CusFusedAbsMax1(PrimitiveWithInfer):
      """
      Compute the abs max of Tensor input.
  
      The rank of input tensors must be `4` or `2`.
      Inputs:
          - **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N0, M0, N1, M1)`
            or math:`(32, 64)`.
      Outputs:
          Tensor, the shape of the output tensor is :math:`(32, 64)` or math:`(1, )`.
  
      Examples:
          >>> input_x = Tensor(np.ones(shape=[1, 3]), mindspore.float32)
          >>> cus_fused_abs_max1 = P.CusFusedAbsMax1()
          >>> output = cus_fused_abs_max1(input_x)
      """
  
      @prim_attr_register
      def __init__(self, origin_shape=[-1, -1]):
          """init CusFusedAbsMax1"""
          self.init_prim_io_names(inputs=['x1'], outputs=['y'])
          self.origin_shape = origin_shape
          from mindspore.ops._op_impl._custom_op.fused_abs_max1_impl import CusFusedAbsMax1
  
      def infer_shape(self, data1_shape):
          ll = []
          if len(data1_shape) == 2:
              ll = [1,]
          else:
              ll = [32, 64]
          return ll
  
      def infer_dtype(self, data1_dtype):
          return data1_dtype
  
  
  class CusImg2Col(PrimitiveWithInfer):
      """
      Img2col the feature map and the result in reorganized in NC1HWC0.
  
      Args:
          - **strides** (listInt) - the stride of the ops.
          - **ksizes** (listInt) - the kernel size of the ops.
      Inputs:
          - **input_x** (Tensor) - The shape of the tensor is :math:`(N, C, H, W)`.
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N * H_O * W_O, C1 * K_W * K_H * C0)`.
      Examples:
          >>> input_x = Tensor(np.ones(shape=[32, 3, 224, 224]), mindspore.float16)
          >>> cusimg2col = P.CusImg2Col()
          >>> output = cusimg2col(input_x)
      """
  
      @prim_attr_register
      def __init__(self, ksizes, strides, dilates=(1, 1, 1, 1), mode="NC1HWC0"):
          """init CusImg2Col"""
          self.init_prim_io_names(inputs=['x1'], outputs=['y'])
          self.ksizes = ksizes
          self.strides = strides
          self.dilates = dilates
          self.mode = mode
          from mindspore.ops._op_impl._custom_op.img2col_impl import CusImg2Col
  
      def infer_shape(self, data1_shape):
          bs, c, h, w = data1_shape
          _, stride_h, stride_w, _ = self.strides
          _, k_w, k_h, _ = self.ksizes
          # assert m == n
          c0 = 16
          c1 = c // 16
          if c1 == 0:
              c1 = 1
          shape = [bs * int(h // stride_h) * int(w // stride_w), k_w * k_h * c1 * c0]
          return shape
  
      def infer_dtype(self, data1_dtype):
          return data1_dtype
  
  
  class CusMatMulCubeDenseLeft(PrimitiveWithInfer):
      """
      Multiplies matrix `a` by matrix `b`.
  
      The rank of input_x1 must be `4`, the fractal format of the normal matrix.
      The rank of input_x2 must be `2`.
  
      Inputs:
          - **input_x1** (Tensor) - The first tensor to be multiplied.
            The shape of the tensor is :math:`(N0, M0, N1, M1)`.
          - **input_x2** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(M, C)`.
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N, C)`.
      Examples:
          >>> input_x = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16)
          >>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
          >>> matmulcubedenseleft = P.CusMatMulCubeDenseLeft()
          >>> output = matmulcubedenseleft(input_x, input_y)
      """
  
      @prim_attr_register
      def __init__(self):
          """init CusMatMulCubeDenseLeft"""
          self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
          from mindspore.ops._op_impl._custom_op.matmul_cube_dense_left_impl import CusMatMulCubeDenseLeft
  
      def infer_shape(self, data1_shape, data2_shape):
          return data2_shape
  
      def infer_dtype(self, data1_dtype, data2_dtype):
          return mstype.float16
  
  
  class CusMatMulCubeFraczRightMul(PrimitiveWithInfer):
      """
      Multiplies matrix `a` by matrix `b` and muls the result by scalar `c`.
  
      The rank of input_x1 tensors must be `2`.
      The rank of input_x2 tensors must be `4`.
  
      Inputs:
          - **input_x1** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, C)`.
          - **input_x2** (Tensor) - The second tensor to be multiplied.
            The shape of the tensor is :math:`(C1, M1, C0, M0)`.
          - **input_x3** (Tensor) - The third tensor to be multiplied. The shape of the tensor if :math`(1, )`.
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N, M)`.
      Examples:
          >>> input_x1 = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
          >>> input_x2 = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16)
          >>> input_x3 = Tensor(np.ones(shape=[1, ]), mindspore.float16)
          >>> cusmatmulfraczrightmul = P.CusMatMulCubeFraczRightMul()
          >>> output = cusmatmulfraczrightmul(input_x1, input_x2, input_x3)
      """
  
      @prim_attr_register
      def __init__(self):
          """init CusMatMulCubeFraczRightMul"""
          self.init_prim_io_names(inputs=['x1', 'x2', 'x3'], outputs=['y'])
          from mindspore.ops._op_impl._custom_op.matmul_cube_fracz_right_mul_impl import CusMatMulCubeFraczRightMul
  
      def infer_shape(self, data1_shape, data2_shape, data3_shape):
          return data1_shape
  
      def infer_dtype(self, data1_dtype, data2_dtype, data3_dtype):
          return mstype.float32
  
  
  class CusMatMulCube(PrimitiveWithInfer):
      """
      Multiplies matrix `a` by matrix `b`.
  
      The rank of input tensors must be `2`.
  
      Args:
          transpose_a (bool): If True, `a` is transposed before multiplication. Default: False.
          transpose_b (bool): If True, `b` is transposed before multiplication. Default: False.
  
      Inputs:
          - **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, C)`. If
            `transpose_a` is True, its shape should be :math:`(N, C)` after transposing.
          - **input_y** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(C, M)`. If
            `transpose_b` is True, its shape should be :math:`(C, M)` after transpose.
  
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N, M)`.
  
      Examples:
          >>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
          >>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
          >>> cusmatmulcube = P.CusMatMulCube()
          >>> output = matmul(input_x, input_y)
      """
  
      @prim_attr_register
      def __init__(self, transpose_a=False, transpose_b=False):
          """init CusMatMulCube"""
          self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
          self.transpose_a = transpose_a
          self.transpose_b = transpose_b
          from mindspore.ops._op_impl._custom_op.matmul_cube_impl import CusMatMulCube
  
      def infer_shape(self, data1_shape, data2_shape):
          # shape = [1, data1_shape[1], data2_shape[2], 16, 16]
          # return shape
          if self.transpose_a:
              k1, m = data1_shape
          else:
              m, k1 = data1_shape
          if self.transpose_b:
              n, k2 = data2_shape
          else:
              k2, n = data2_shape
          assert k1 == k2
          shape = [m, n]
          return shape
  
      def infer_dtype(self, data1_dtype, data2_dtype):
          return mstype.float32
  
  
  class CusMatrixCombine(PrimitiveWithInfer):
      """
      move the batch matrix to result matrix diag part.
      The rank of input tensors must be `3`.
  
      Inputs:
          - **input_x** (Tensor) - The shape of the tensor is :math:`(N, D, D)`.
  
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N * D, N * D)`.
  
      Examples:
          >>> input_x = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32)
          >>> cusmatrixcombine = P.CusMatrixCombine()
          >>> output = cusmatrixcombine(input_x)
      """
  
      @prim_attr_register
      def __init__(self):
          """init CusMatrixCombine"""
          self.init_prim_io_names(inputs=['x'], outputs=['y'])
          from mindspore.ops._op_impl._custom_op.matrix_combine_impl import CusMatrixCombine
  
      def infer_shape(self, data_shape):
          a, b, c = data_shape
          shape = [a * b, a * c]
  
          return shape
  
      def infer_dtype(self, data_dtype):
          return data_dtype
  
  
  class CusTranspose02314(PrimitiveWithInfer):
      """
      Permute input tensor with perm (0, 2, 3, 1, 4)
  
      The rank of input tensors must be `5` with format NC1HWC0.
  
      Inputs:
          - **input_x** (Tensor) - The shape of the tensor is :math:`(N, C1, H, W, C0)`.
  
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N, H, W, C1, C0)`.
  
      Examples:
          >>> input_x = Tensor(np.ones(shape=[32, 1, 224, 224, 16]), mindspore.float16)
          >>> custranspose02314 = P.CusTranspose02314()
          >>> output = custranspose02314(input_x)
      """
  
      @prim_attr_register
      def __init__(self):
          """init CusTranspose02314"""
          self.init_prim_io_names(inputs=['x1'], outputs=['y'])
          from mindspore.ops._op_impl._custom_op.transpose02314_impl import CusTranspose02314
      def get_bprop(self):
          def bprop(x, out, dout):
              return (C.zeros_like(x),)
  
          return bprop
  
      def infer_shape(self, data1_shape):
          assert len(data1_shape) == 4
          n, c, h, w = data1_shape
          c0 = 16
          c1 = c // 16
          shape = (n * h * w, c1 * c0)
          return shape
  
      def infer_dtype(self, data1_dtype):
          return data1_dtype
  
  
  class CusMatMulCubeDenseRight(PrimitiveWithInfer):
      """
      Multiplies matrix `a` by matrix `b`.
  
      The rank of input_x1 tensor must be `2`.
      The rank of input_x2 tensor must be `4`.
  
      Inputs:
          - **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, C)`.
          - **input_y** (Tensor) - The second tensor to be multiplied.
            The shape of the tensor is :math:`(C1, M1, M0, C0)`.
  
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N, M)`.
  
      Examples:
          >>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
          >>> input_y = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16)
          >>> cusmatmulcubedenseright = P.CusMatMulCubeDenseRight()
          >>> output = cusmatmulcubedenseright(input_x, input_y)
      """
  
      @prim_attr_register
      def __init__(self):
          """init CusMatMulCubeDenseRight"""
          self.init_prim_io_names(inputs=['x1', 'x2', 'x3'], outputs=['y'])
          from mindspore.ops._op_impl._custom_op.matmul_cube_dense_right_impl import CusMatMulCubeDenseRight
  
      def infer_shape(self, data1_shape, data2_shape, data3_shape):
          return data1_shape
  
      def infer_dtype(self, data1_dtype, data2_dtype, data3_dtype):
          return mstype.float32
  
  
  class CusMatMulCubeFraczLeftCast(PrimitiveWithInfer):
      """
      Multiplies matrix `a` by matrix `b`.
  
      The rank of input_x1 tensor must be `4`.
      The rank of input_x2 tensors must be `2`.
  
      Inputs:
          - **input_x1** (Tensor) - The first tensor to be multiplied.
            The shape of the tensor is :math:`(C1, N1, N0, C0)`.
          - **input_x2** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(C, M)`.
  
      Outputs:
          Tensor, the shape of the output tensor is :math:`(N, M)`.
  
      Examples:
          >>> input_x = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16)
          >>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
          >>> cusmatmulcubefraczleftcast = P.CusMatMulCubeFraczLeftCast()
          >>> output = cusmatmulcubefraczleftcast(input_x, input_y)
      """
  
      @prim_attr_register
      def __init__(self):
          """init CusMatMulCubeFraczLeftCast"""
          self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
          from mindspore.ops._op_impl._custom_op.matmul_cube_fracz_left_cast_impl import CusMatMulCubeFraczLeftCast
  
      def infer_shape(self, data1_shape, data2_shape):
          return data2_shape
  
      def infer_dtype(self, data1_dtype, data2_dtype):
          return mstype.float16
  