# RUN: %PYTHON %s | FileCheck %s

from mlir.ir import *
from mlir.dialects import sparse_tensor as st

def run(f):
  print("\nTEST:", f.__name__)
  f()
  return f


# CHECK-LABEL: TEST: testEncodingAttr1D
@run
def testEncodingAttr1D():
  with Context() as ctx:
    parsed = Attribute.parse('#sparse_tensor.encoding<{'
                             '  lvlTypes = [ "compressed" ],'
                             '  posWidth = 16,'
                             '  crdWidth = 32'
                             '}>')
    # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 16, crdWidth = 32 }>
    print(parsed)

    casted = st.EncodingAttr(parsed)
    # CHECK: equal: True
    print(f"equal: {casted == parsed}")

    # CHECK: lvl_types: [<DimLevelType.compressed: 8>]
    print(f"lvl_types: {casted.lvl_types}")
    # CHECK: dim_ordering: None
    print(f"dim_ordering: {casted.dim_ordering}")
    # CHECK: pos_width: 16
    print(f"pos_width: {casted.pos_width}")
    # CHECK: crd_width: 32
    print(f"crd_width: {casted.crd_width}")

    created = st.EncodingAttr.get(casted.lvl_types, None, None, 0, 0)
    # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
    print(created)
    # CHECK: created_equal: False
    print(f"created_equal: {created == casted}")

    # Verify that the factory creates an instance of the proper type.
    # CHECK: is_proper_instance: True
    print(f"is_proper_instance: {isinstance(created, st.EncodingAttr)}")
    # CHECK: created_pos_width: 0
    print(f"created_pos_width: {created.pos_width}")


# CHECK-LABEL: TEST: testEncodingAttr2D
@run
def testEncodingAttr2D():
  with Context() as ctx:
    parsed = Attribute.parse('#sparse_tensor.encoding<{'
                             '  lvlTypes = [ "dense", "compressed" ],'
                             '  dimOrdering = affine_map<(d0, d1) -> (d1, d0)>,'
                             '  posWidth = 8,'
                             '  crdWidth = 32'
                             '}>')
    # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
    print(parsed)

    casted = st.EncodingAttr(parsed)
    # CHECK: equal: True
    print(f"equal: {casted == parsed}")

    # CHECK: lvl_types: [<DimLevelType.dense: 4>, <DimLevelType.compressed: 8>]
    print(f"lvl_types: {casted.lvl_types}")
    # CHECK: dim_ordering: (d0, d1) -> (d1, d0)
    print(f"dim_ordering: {casted.dim_ordering}")
    # CHECK: pos_width: 8
    print(f"pos_width: {casted.pos_width}")
    # CHECK: crd_width: 32
    print(f"crd_width: {casted.crd_width}")

    created = st.EncodingAttr.get(casted.lvl_types, casted.dim_ordering,
                                  casted.higher_ordering, 8, 32)
    # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
    print(created)
    # CHECK: created_equal: True
    print(f"created_equal: {created == casted}")


# CHECK-LABEL: TEST: testEncodingAttrOnTensorType
@run
def testEncodingAttrOnTensorType():
  with Context() as ctx, Location.unknown():
    encoding = st.EncodingAttr(
        Attribute.parse('#sparse_tensor.encoding<{'
                        '  lvlTypes = [ "compressed" ], '
                        '  posWidth = 64,'
                        '  crdWidth = 32'
                        '}>'))
    tt = RankedTensorType.get((1024,), F32Type.get(), encoding=encoding)
    # CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 64, crdWidth = 32 }>>
    print(tt)
    # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 64, crdWidth = 32 }>
    print(tt.encoding)
    assert tt.encoding == encoding