# RUN: SUPPORT_LIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \
# RUN:   %PYTHON %s | FileCheck %s

import ctypes
import numpy as np
import os
import sys

from mlir import ir
from mlir import runtime as rt

from mlir.dialects import sparse_tensor as st
from mlir.dialects import builtin
from mlir.dialects import func
from mlir.dialects.linalg.opdsl import lang as dsl

_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH)
from tools import sparse_compiler

@dsl.linalg_structured_op
def matmul_dsl(
    A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K),
    B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N),
    C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)):
    C[dsl.D.m, dsl.D.n] += A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]


def build_SpMM(attr: st.EncodingAttr):
    """Build SpMM kernel.

  This method generates a linalg op with for matrix multiplication using
  just the Python API. Effectively, a generic linalg op is constructed
  that computes C(i,j) += A(i,k) * B(k,j) for annotated matrix A.
  """
    module = ir.Module.create()
    f64 = ir.F64Type.get()
    a = ir.RankedTensorType.get([3, 4], f64, attr)
    b = ir.RankedTensorType.get([4, 2], f64)
    c = ir.RankedTensorType.get([3, 2], f64)
    arguments = [a, b, c]
    with ir.InsertionPoint(module.body):

        @func.FuncOp.from_py_func(*arguments)
        def spMxM(*args):
            return matmul_dsl(args[0], args[1], outs=[args[2]])

    return module


def boilerplate(attr: st.EncodingAttr):
    """Returns boilerplate main method.

  This method sets up a boilerplate main method that takes three tensors
  (a, b, c), converts the first tensor a into s sparse tensor, and then
  calls the sparse kernel for matrix multiplication. For convenience,
  this part is purely done as string input.
  """
    return f"""
func.func @main(%ad: tensor<3x4xf64>, %b: tensor<4x2xf64>, %c: tensor<3x2xf64>) -> tensor<3x2xf64>
  attributes {{ llvm.emit_c_interface }} {{
  %a = sparse_tensor.convert %ad : tensor<3x4xf64> to tensor<3x4xf64, {attr}>
  %0 = call @spMxM(%a, %b, %c) : (tensor<3x4xf64, {attr}>,
                                  tensor<4x2xf64>,
                                  tensor<3x2xf64>) -> tensor<3x2xf64>
  return %0 : tensor<3x2xf64>
}}
"""


def build_compile_and_run_SpMM(attr: st.EncodingAttr, compiler):
    # Build.
    module = build_SpMM(attr)
    func = str(module.operation.regions[0].blocks[0].operations[0].operation)
    module = ir.Module.parse(func + boilerplate(attr))

    # Compile.
    engine = compiler.compile_and_jit(module)

    # Set up numpy input and buffer for output.
    a = np.array(
        [[1.1, 0.0, 0.0, 1.4], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 3.3, 0.0]],
        np.float64)
    b = np.array([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0]], np.float64)
    c = np.zeros((3, 2), np.float64)

    mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
    mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
    mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
    # Allocate a MemRefDescriptor to receive the output tensor.
    # The buffer itself is allocated inside the MLIR code generation.
    ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
    mem_out = ctypes.pointer(ctypes.pointer(ref_out))

    # Invoke the kernel and get numpy output.
    # Built-in bufferization uses in-out buffers.
    # TODO: replace with inplace comprehensive bufferization.
    engine.invoke('main', mem_out, mem_a, mem_b, mem_c)

    # Sanity check on computed result.
    expected = np.matmul(a, b);
    c = rt.ranked_memref_to_numpy(mem_out[0])
    if np.allclose(c, expected):
        pass
    else:
        quit(f'FAILURE')


def main():
    support_lib = os.getenv('SUPPORT_LIB')
    assert support_lib is not None, 'SUPPORT_LIB is undefined'
    if not os.path.exists(support_lib):
        raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib)

    # CHECK-LABEL: TEST: testSpMM
    print('\nTEST: testSpMM')
    with ir.Context() as ctx, ir.Location.unknown():
        count = 0
        # Loop over various ways to compile and annotate the SpMM kernel with
        # a *single* sparse tensor. Note that we deliberate do not exhaustively
        # search the full state space to reduce runtime of the test. It is
        # straightforward to adapt the code below to explore more combinations.
        par = 0
        vec = 0
        vl = 1
        e = False
        opt = (f'parallelization-strategy={par} '
               f'vectorization-strategy={vec} '
               f'vl={vl} enable-simd-index32={e}')
        levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
                  [st.DimLevelType.dense, st.DimLevelType.compressed],
                  [st.DimLevelType.compressed, st.DimLevelType.dense],
                  [st.DimLevelType.compressed, st.DimLevelType.compressed]]
        orderings = [
            ir.AffineMap.get_permutation([0, 1]),
            ir.AffineMap.get_permutation([1, 0])
        ]
        bitwidths = [0]
        compiler = sparse_compiler.SparseCompiler(
            options=opt, opt_level=0, shared_libs=[support_lib])
        for level in levels:
            for ordering in orderings:
                for pwidth in bitwidths:
                    for iwidth in bitwidths:
                        attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth)
                        build_compile_and_run_SpMM(attr, compiler)
                        count = count + 1
        # CHECK: Passed 8 tests
        print('Passed ', count, 'tests')


if __name__ == '__main__':
    main()