benchmarks/fp8/transformer_engine/ddp.py [82:100]:
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    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
    )
    assert trained_model_results["f1"] > base_model_results["f1"], (
        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
    )

    return base_model_results, trained_model_results


def train_integration():
    FP8_RECIPE_KWARGS = {"fp8_format": "HYBRID", "amax_history_len": 32, "amax_compute_algo": "max"}
    kwargs_handlers = [FP8RecipeKwargs(backend="TE", **FP8_RECIPE_KWARGS)]
    AcceleratorState()._reset_state(True)
    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=kwargs_handlers)
    set_seed(42)
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
        MODEL_NAME, accelerator=accelerator
    )
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benchmarks/fp8/transformer_engine/non_distributed.py [73:91]:
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    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
    )
    assert trained_model_results["f1"] > base_model_results["f1"], (
        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
    )

    return base_model_results, trained_model_results


def train_integration():
    FP8_RECIPE_KWARGS = {"fp8_format": "HYBRID", "amax_history_len": 32, "amax_compute_algo": "max"}
    kwargs_handlers = [FP8RecipeKwargs(backend="TE", **FP8_RECIPE_KWARGS)]
    AcceleratorState()._reset_state(True)
    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=kwargs_handlers)
    set_seed(42)
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
        MODEL_NAME, accelerator=accelerator
    )
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