"""Common utilities that are useful for all the benchmarks."""
import numpy as np

import mlir.all_passes_registration

from mlir import ir
from mlir.dialects import arith
from mlir.dialects import func
from mlir.dialects import memref
from mlir.dialects import scf
from mlir.passmanager import PassManager


def setup_passes(mlir_module):
    """Setup pass pipeline parameters for benchmark functions.
    """
    opt = (
        "parallelization-strategy=0"
        " vectorization-strategy=0 vl=1 enable-simd-index32=False"
    )
    pipeline = f"sparse-compiler{{{opt}}}"
    PassManager.parse(pipeline).run(mlir_module)


def create_sparse_np_tensor(dimensions, number_of_elements):
    """Constructs a numpy tensor of dimensions `dimensions` that has only a
    specific number of nonzero elements, specified by the `number_of_elements`
    argument.
    """
    tensor = np.zeros(dimensions, np.float64)
    tensor_indices_list = [
        [np.random.randint(0, dimension) for dimension in dimensions]
        for _ in range(number_of_elements)
    ]
    for tensor_indices in tensor_indices_list:
        current_tensor = tensor
        for tensor_index in tensor_indices[:-1]:
            current_tensor = current_tensor[tensor_index]
        current_tensor[tensor_indices[-1]] = np.random.uniform(1, 100)
    return tensor


def get_kernel_func_from_module(module: ir.Module) -> func.FuncOp:
    """Takes an mlir module object and extracts the function object out of it.
    This function only works for a module with one region, one block, and one
    operation.
    """
    assert len(module.operation.regions) == 1, \
        "Expected kernel module to have only one region"
    assert len(module.operation.regions[0].blocks) == 1, \
        "Expected kernel module to have only one block"
    assert len(module.operation.regions[0].blocks[0].operations) == 1, \
        "Expected kernel module to have only one operation"
    return module.operation.regions[0].blocks[0].operations[0]


def emit_timer_func() -> func.FuncOp:
    """Returns the declaration of nanoTime function. If nanoTime function is
    used, the `MLIR_RUNNER_UTILS` and `MLIR_C_RUNNER_UTILS` must be included.
    """
    i64_type = ir.IntegerType.get_signless(64)
    nanoTime = func.FuncOp(
        "nanoTime", ([], [i64_type]), visibility="private")
    nanoTime.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()
    return nanoTime


def emit_benchmark_wrapped_main_func(kernel_func, timer_func):
    """Takes a function and a timer function, both represented as FuncOp
    objects, and returns a new function. This new function wraps the call to
    the original function between calls to the timer_func and this wrapping
    in turn is executed inside a loop. The loop is executed
    len(kernel_func.type.results) times. This function can be used to
    create a "time measuring" variant of a function.
    """
    i64_type = ir.IntegerType.get_signless(64)
    memref_of_i64_type = ir.MemRefType.get([-1], i64_type)
    wrapped_func = func.FuncOp(
        # Same signature and an extra buffer of indices to save timings.
        "main",
        (kernel_func.arguments.types + [memref_of_i64_type],
         kernel_func.type.results),
        visibility="public"
    )
    wrapped_func.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()

    num_results = len(kernel_func.type.results)
    with ir.InsertionPoint(wrapped_func.add_entry_block()):
        timer_buffer = wrapped_func.arguments[-1]
        zero = arith.ConstantOp.create_index(0)
        n_iterations = memref.DimOp(ir.IndexType.get(), timer_buffer, zero)
        one = arith.ConstantOp.create_index(1)
        iter_args = list(wrapped_func.arguments[-num_results - 1:-1])
        loop = scf.ForOp(zero, n_iterations, one, iter_args)
        with ir.InsertionPoint(loop.body):
            start = func.CallOp(timer_func, [])
            call = func.CallOp(
                kernel_func,
                wrapped_func.arguments[:-num_results - 1] + loop.inner_iter_args
            )
            end = func.CallOp(timer_func, [])
            time_taken = arith.SubIOp(end, start)
            memref.StoreOp(time_taken, timer_buffer, [loop.induction_variable])
            scf.YieldOp(list(call.results))
        func.ReturnOp(loop)

    return wrapped_func