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"""Example 7.

This example shows simple usage of the DPNP
to calculate square matrix multiplication

Also, it produces performance comparison between regular NumPy
and DPNP for several matrix multiplication

"""

import time

import dpctl
import numpy

import dpnp


def run_function(executor, name, size, test_type, repetition):
    x = executor.reshape(
        executor.arange(size * size, dtype=test_type), (size, size)
    )

    times = []
    for _ in range(repetition):
        start_time = time.perf_counter()
        result = executor.sum(x)
        # print("result[5]=%f" % (result.item(5)))
        end_time = time.perf_counter()
        times.append(end_time - start_time)

    execution_time = numpy.median(times)
    return execution_time, result


def get_dtypes():
    _dtypes_list = [numpy.int32, numpy.int64, numpy.float32]
    device = dpctl.select_default_device()
    if device.has_aspect_fp64:
        _dtypes_list.append(numpy.float64)
    return _dtypes_list


if __name__ == "__main__":
    test_repetition = 5
    for test_type in get_dtypes():
        type_name = numpy.dtype(test_type).name
        print(
            f"...Test data type is {test_type}, each test repetitions {test_repetition}"
        )

        for size in [256, 512, 1024, 2048, 4096, 8192]:
            time_python, result_python = run_function(
                numpy, "<NumPy>", size, test_type, test_repetition
            )
            time_mkl, result_mkl = run_function(
                dpnp, " <DPNP>", size, test_type, test_repetition
            )

            if result_mkl == result_python:
                verification = True
            else:
                verification = f"({result_mkl} != {result_python})"

            msg = f"type:{type_name}:N:{size:4}:NumPy:{time_python:.3e}:DPNP:{time_mkl:.3e}"
            msg += f":ratio:{time_python/time_mkl:6.2f}:verification:{verification}"
            print(msg)
