text: """Test code for broadcasting operators."""
  import numpy as np
  import tvm
  import topi
  
  from common import get_all_backend
  
  def verify_expand_dims(in_shape, out_shape, axis, num_newaxis):
      A = tvm.placeholder(shape=in_shape, name="A")
      B = topi.expand_dims(A, axis, num_newaxis)
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_broadcast(B)
          foo = tvm.build(s, [A, B], device, name="expand_dims")
          data_npy = np.random.uniform(size=in_shape).astype(A.dtype)
          out_npy = data_npy.reshape(out_shape)
          data_nd = tvm.nd.array(data_npy, ctx)
          out_nd = tvm.nd.array(np.empty(out_shape).astype(B.dtype), ctx)
          foo(data_nd, out_nd)
          np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
  
      for device in get_all_backend():
          check_device(device)
  
  
  def verify_tranpose(in_shape, axes):
      A = tvm.placeholder(shape=in_shape, name="A")
      B = topi.transpose(A, axes)
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_injective(B)
          foo = tvm.build(s, [A, B], device, name="tranpose")
          data_npy = np.arange(np.prod(in_shape)).reshape(in_shape).astype(A.dtype)
          out_npy = data_npy.transpose(axes)
          data_nd = tvm.nd.array(data_npy, ctx)
          out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=B.dtype)
          foo(data_nd, out_nd)
          np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
  
      for device in get_all_backend():
          check_device(device)
  
  
  def verify_reshape(src_shape, dst_shape):
      A = tvm.placeholder(shape=src_shape, name="A")
      B = topi.reshape(A, dst_shape)
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_injective(B)
          foo = tvm.build(s, [A, B], device, name="reshape")
          data_npy = np.random.normal(size=src_shape).astype(A.dtype)
          out_npy = np.reshape(data_npy, newshape=dst_shape)
          data_nd = tvm.nd.array(data_npy, ctx)
          out_nd = tvm.nd.empty(dst_shape, ctx=ctx, dtype=B.dtype)
          foo(data_nd, out_nd)
          np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
  
      for device in get_all_backend():
          check_device(device)
  
  
  def verify_squeeze(src_shape, axis):
      A = tvm.placeholder(shape=src_shape, name="A")
      B = topi.squeeze(A, axis=axis)
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_injective(B)
  
          foo = tvm.build(s, [A, B], device, name="squeeze")
          data_npy = np.random.normal(size=src_shape).astype(A.dtype)
          out_npy = np.squeeze(data_npy, axis=axis)
          data_nd = tvm.nd.array(data_npy, ctx)
          if out_npy.shape == ():
              out_nd_shape = (1,)
          else:
              out_nd_shape = out_npy.shape
          out_nd = tvm.nd.empty(out_nd_shape, ctx=ctx, dtype=B.dtype)
          foo(data_nd, out_nd)
          np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
  
      for device in get_all_backend():
          check_device(device)
  
  def verify_concatenate(shapes, axis):
      tensor_l = []
      for i, shape in enumerate(shapes):
          tensor_l.append(tvm.placeholder(shape, name="A" + str(i)))
      out_tensor = topi.concatenate(a_tuple=tensor_l, axis=axis)
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_injective(out_tensor)
  
          foo = tvm.build(s, tensor_l + [out_tensor], device, name="concatenate")
          data_npys = [np.random.normal(size=shape).astype(tensor_l[0].dtype) for shape in shapes]
          out_npy = np.concatenate(data_npys, axis=axis)
          data_nds = [tvm.nd.array(data_npy, ctx) for data_npy in data_npys]
          out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=out_tensor.dtype)
          foo(*(data_nds + [out_nd]))
          np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
  
      for device in get_all_backend():
          check_device(device)
  
  
  def verify_split(src_shape, indices_or_sections, axis):
      A = tvm.placeholder(shape=src_shape, name="A")
      tensor_l = topi.split(A, indices_or_sections, axis=axis)
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_injective(tensor_l)
  
          foo = tvm.build(s, [A] + tensor_l, device, name="split")
          data_npy = np.random.normal(size=src_shape).astype(A.dtype)
          out_npys = np.split(data_npy, indices_or_sections, axis=axis)
          data_nd = tvm.nd.array(data_npy, ctx)
          out_nds = [tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=tensor_l[0].dtype) for out_npy in out_npys]
          foo(*([data_nd] + out_nds))
          for out_nd, out_npy in zip(out_nds, out_npys):
              np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
  
      for device in get_all_backend():
          check_device(device)
  
  
  def verify_expand_like(in_shape, out_shape, axis):
      A = tvm.placeholder(shape=in_shape, name="A")
      B = tvm.placeholder(shape=out_shape, name="B")
      C = topi.expand_like(A, B, axis)
      s = tvm.create_schedule([C.op])
  
      def check_device(device):
          if not tvm.module.enabled(device):
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
  
          ctx = tvm.context(device, 0)
          f = tvm.build(s, [A, B, C], device, name="expand_like")
          input = np.random.uniform(size=in_shape).astype(A.dtype)
          tvm_input = tvm.nd.array(input, ctx)
  
          odim = len(out_shape)
          real_axis = [x if x >= 0 else x + odim for x in axis]
          real_axis = sorted(real_axis)
          for x in real_axis:
              input = np.expand_dims(input, x).astype(A.dtype)
          for x in real_axis:
              input = np.concatenate([input]*out_shape[x], axis=x).astype(A.dtype)
          assert input.shape == out_shape
  
          tvm_shape_like = tvm.nd.array(np.zeros(out_shape).astype(B.dtype), ctx)
          out = tvm.nd.array(np.zeros(out_shape).astype(A.dtype), ctx)
          f(tvm_input, tvm_shape_like, out)
          np.testing.assert_allclose(out.asnumpy(), input)
  
      for device in ["llvm"]:
          check_device(device)
  
  def verify_flip(in_shape, axis):
      A = tvm.placeholder(shape=in_shape, name="A")
      B = topi.flip(A, axis) + 1
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_injective(B)
  
          foo = tvm.build(s, [A, B], device, name="reverse")
          x_np = np.random.uniform(size=in_shape).astype(A.dtype)
          out_npy = np.flip(x_np, axis) + 1
          data_nd = tvm.nd.array(x_np, ctx)
          out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=A.dtype)
          foo(data_nd, out_nd)
          np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
  
      for device in ["llvm", "cuda", "opencl", "sdaccel", "aocl_sw_emu"]:
          check_device(device)
  
  def verify_take(src_shape, indices_src, axis=None):
      src_dtype = "float32"
      indices_dtype = "int32"
      indices_src = np.array(indices_src, dtype=indices_dtype)
      A = tvm.placeholder(shape=src_shape, dtype=src_dtype, name="A")
      indices = tvm.placeholder(shape=indices_src.shape, dtype=indices_dtype, name="indices")
      if axis is None:
          out_tensor = topi.take(a=A, indices=indices)
      else:
          out_tensor = topi.take(a=A, indices=indices, axis=axis)
  
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_injective(out_tensor)
  
          foo = tvm.build(s, [A] + [indices] + [out_tensor] , device, name="take")
          shape_size = 1
          for i in range(len(src_shape)):
              shape_size = shape_size * src_shape[i]
          data_npy = np.arange(shape_size, dtype=src_dtype).reshape((src_shape))
  
          if axis is None:
              out_npys = np.take(data_npy, indices_src)
          else:
              out_npys = np.take(data_npy, indices_src, axis=axis)
          data_nd = tvm.nd.array(data_npy, ctx)
          indices_nd = tvm.nd.array(indices_src, ctx)
          out_nd = tvm.nd.empty(out_npys.shape, ctx=ctx, dtype=src_dtype)
          foo(data_nd, indices_nd, out_nd)
          np.testing.assert_allclose(out_nd.asnumpy(), out_npys)
  
      for device in ["llvm", "opencl", "sdaccel", "aocl_sw_emu"]:
          check_device(device)
  
  def verify_strided_slice(in_shape, begin, end, stride=None):
      stride = stride if stride else [1, 1, 1]
      A = tvm.placeholder(shape=in_shape, name="A")
      B = topi.strided_slice(A, begin, end, stride) + 1
      def test_forward(x, begin, end, stride):
          return x[begin[0]:end[0]:stride[0],
                      begin[1]:end[1]:stride[1], begin[2]:end[2]:stride[2]] + 1
      def check_device(device):
          ctx = tvm.context(device, 0)
          if not ctx.exist:
              print("Skip because %s is not enabled" % device)
              return
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              s = topi.generic.schedule_injective(B)
  
          foo = tvm.build(s, [A, B], device, name="stride_slice")
          x_np = np.random.uniform(size=in_shape).astype(A.dtype)
          out_npy = test_forward(x_np, begin, end, stride)
          data_nd = tvm.nd.array(x_np, ctx)
          out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=A.dtype)
          foo(data_nd, out_nd)
          np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
  
      for device in ["llvm", "opencl", "sdaccel", "aocl_sw_emu"]:
          check_device(device)
  
  def test_strided_slice():
      verify_strided_slice((3, 4, 3), [0, 0, 0], [4, -5, 4], [1, -1, 2])
      verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1])
      verify_strided_slice((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1])
      verify_strided_slice((3, 4, 3), [1, 0, 0], [2, 2, 3], [1, 1, 2])
      verify_strided_slice((3, 4, 3), [1, -1, 0], [2, -3, 3], [1, -1, 1])
      verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4, 3])
  
  def test_expand_dims():
      verify_expand_dims((3, 10), (3, 10, 1, 1), 2, 2)
      verify_expand_dims((3, 10), (1, 3, 10), -3, 1)
  
  
  def test_tranpose():
      verify_tranpose((3, 10, 2), (1, 0, 2))
      verify_tranpose((3, 10, 5), (2, 0, 1))
      verify_tranpose((3, 10), None)
  
  
  def test_reshape():
      verify_reshape((1, 2, 3, 4), (2, 3, 4))
      verify_reshape((4, 2, 3, 4), (2, 4, 12))
      verify_reshape((4, 2, 3, 4), (2, 48))
      verify_reshape((16, ), (2, 2, 2, 2))
  
  
  def test_squeeze():
      verify_squeeze((1, 2, 3, 4), 0)
      verify_squeeze((1, 2, 1, 4), None)
      verify_squeeze((1, 1, 1, 4), (1, 2))
      verify_squeeze((1, 1, 1, 1), None)
  
      # a special case to trigger inline let expression
      A = tvm.placeholder((2,), 'float32', 'A')
      E = topi.squeeze(A)
      C = tvm.compute((1,), lambda i: E[(2 * A[0] - 1).astype('int32')])
      for device in ['cuda', 'opencl']:
          ctx = tvm.context(device, 0)
          if ctx.exist:
              with tvm.target.create(device):
                  s = topi.generic.schedule_injective(C)
                  func = tvm.build(s, [A, C])
              a = tvm.nd.array(np.array((1, 2)).astype('float32'), ctx=ctx)
              c = tvm.nd.empty((1,), dtype='float32', ctx=ctx)
              func(a, c)
              assert c.asnumpy()[0] == 2
  
  
  def test_concatenate():
      verify_concatenate([(2,), (2,), (2,)], 0)
      verify_concatenate([(2, 3, 4), (2, 2, 4), (2, 5, 4)], 1)
      verify_concatenate([(1, 2, 4), (1, 2, 3), (1, 2, 7), (1, 2, 8), (1, 2, 1)], -1)
      verify_concatenate([(5, 6, 7, 3),
                          (16, 6, 7, 3),
                          (12, 6, 7, 3),
                          (8, 6, 7, 3),
                          (2, 6, 7, 3)], 0)
  
  
  def test_split():
      verify_split((2, 12, 3), 3, 1)
      verify_split((2, 12, 3), [2, 4], 1)
      verify_split((10, 12, 24), [5, 7, 9], -1)
  
  def test_flip():
      verify_flip((3, 4, 3), 1)
      verify_flip((3, 4, 3), 0)
      verify_flip((3, 4, 3), 2)
      verify_flip((3, 4, 3), -1)
      verify_flip((3, 4, 3), -3)
      verify_flip((3, 4, 3), -2)
  
  def test_expand_like():
      verify_expand_like((3,), (2, 3), [0])
      verify_expand_like((2,), (2, 3), [1])
      verify_expand_like((3, 4), (3, 5, 4), [1])
      verify_expand_like((5, 7), (5, 6, 7, 8), [1, 3])
  
  def test_take():
      verify_take((4,), [1])
      verify_take((4,), [[0,1,2,3]])
      verify_take((3,3,3), [[11,25]])
      verify_take((4,), [[0,1],[2,3]])
      verify_take((4,), [1], 0)
      verify_take((2,2), [[[1,0],[0,1]]], 0)
      verify_take((2,2), [[[1,0],[0,1]]], 1)
      verify_take((4,3,5,6), [[2,1,0,0]], -2)
  
  if __name__ == "__main__":
      test_concatenate()
      test_tranpose()
      test_expand_dims()
      test_reshape()
      test_squeeze()
      test_split()
      test_flip()
      test_expand_like()
      test_take()
      test_strided_slice()
  