benchmarks/dlrm/ootb/dlrm_data_caffe2.py [772:823]:
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            f.write(s[1 : len(s) - 1] + "\n")
    except Exception:
        print("Wrong file or file path")


if __name__ == "__main__":
    import sys
    import operator
    import argparse

    ### parse arguments ###
    parser = argparse.ArgumentParser(description="Generate Synthetic Distributions")
    parser.add_argument("--trace-file", type=str, default="./input/trace.log")
    parser.add_argument("--trace-file-binary-type", type=bool, default=False)
    parser.add_argument("--trace-enable-padding", type=bool, default=False)
    parser.add_argument("--dist-file", type=str, default="./input/dist.log")
    parser.add_argument(
        "--synthetic-file", type=str, default="./input/trace_synthetic.log"
    )
    parser.add_argument("--numpy-rand-seed", type=int, default=123)
    parser.add_argument("--print-precision", type=int, default=5)
    args = parser.parse_args()

    ### some basic setup ###
    np.random.seed(args.numpy_rand_seed)
    np.set_printoptions(precision=args.print_precision)

    ### read trace ###
    trace = read_trace_from_file(args.trace_file)
    # print(trace)

    ### profile trace ###
    (_, stack_distances, line_accesses) = trace_profile(
        trace, args.trace_enable_padding
    )
    stack_distances.reverse()
    line_accesses.reverse()
    # print(line_accesses)
    # print(stack_distances)

    ### compute probability distribution ###
    # count items
    l = len(stack_distances)
    dc = sorted(
        collections.Counter(stack_distances).items(), key=operator.itemgetter(0)
    )

    # create a distribution
    list_sd = list(map(lambda tuple_x_k: tuple_x_k[0], dc))  # x = tuple_x_k[0]
    dist_sd = list(
        map(lambda tuple_x_k: tuple_x_k[1] / float(l), dc)
    )  # k = tuple_x_k[1]
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benchmarks/dlrm/ootb/dlrm_data_pytorch.py [1239:1289]:
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            f.write(s[1 : len(s) - 1] + "\n")
    except Exception:
        print("Wrong file or file path")


if __name__ == "__main__":
    import operator
    import argparse

    ### parse arguments ###
    parser = argparse.ArgumentParser(description="Generate Synthetic Distributions")
    parser.add_argument("--trace-file", type=str, default="./input/trace.log")
    parser.add_argument("--trace-file-binary-type", type=bool, default=False)
    parser.add_argument("--trace-enable-padding", type=bool, default=False)
    parser.add_argument("--dist-file", type=str, default="./input/dist.log")
    parser.add_argument(
        "--synthetic-file", type=str, default="./input/trace_synthetic.log"
    )
    parser.add_argument("--numpy-rand-seed", type=int, default=123)
    parser.add_argument("--print-precision", type=int, default=5)
    args = parser.parse_args()

    ### some basic setup ###
    np.random.seed(args.numpy_rand_seed)
    np.set_printoptions(precision=args.print_precision)

    ### read trace ###
    trace = read_trace_from_file(args.trace_file)
    # print(trace)

    ### profile trace ###
    (_, stack_distances, line_accesses) = trace_profile(
        trace, args.trace_enable_padding
    )
    stack_distances.reverse()
    line_accesses.reverse()
    # print(line_accesses)
    # print(stack_distances)

    ### compute probability distribution ###
    # count items
    l = len(stack_distances)
    dc = sorted(
        collections.Counter(stack_distances).items(), key=operator.itemgetter(0)
    )

    # create a distribution
    list_sd = list(map(lambda tuple_x_k: tuple_x_k[0], dc))  # x = tuple_x_k[0]
    dist_sd = list(
        map(lambda tuple_x_k: tuple_x_k[1] / float(l), dc)
    )  # k = tuple_x_k[1]
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