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import mxnet as mx
from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
from benchmark.opperf.utils.op_registry_utils import get_all_loss_operators

"""Performance benchmark tests for MXNet Neural Network Loss Operators

1. smooth_l1
2. CTCLoss
3. MakeLoss
4. softmax_cross_entropy
"""


def run_loss_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', int64_tensor='off', warmup=25, runs=100):
    """Runs benchmarks with the given context, precision (dtype), and data size (int64_tensor) for all the
    Neural Network loss operators in MXNet.

    Parameters
    ----------
    ctx: mx.ctx
        Context to run benchmarks
    dtype: str, default 'float32'
        Precision to use for benchmarks
    profiler: str, default 'native'
        Type of Profiler to use (native/python)
    int64_tensor: str, default 'off'
        Input tensor size to use for tests (if on, dimensions >= 2**32)
    warmup: int, default 25
        Number of times to run for warmup
    runs: int, default 100
        Number of runs to capture benchmark results

    Returns
    -------
    Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.

    """
    # Fetch all loss operators
    mx_loss_ops = get_all_loss_operators()

    # Run benchmarks
    mx_loss_op_results = run_op_benchmarks(mx_loss_ops, dtype, ctx, profiler, int64_tensor, warmup, runs)
    return mx_loss_op_results
