src/lighteval/metrics/metrics_sample.py [1195:1212]:
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        self.normalize_gold = normalize_gold
        self.normalize_pred = normalize_pred
        self.strip_strings = strip_strings

        # Managed the logic of the per prediction of sample scoring
        if callable(sample_scoring_function):
            self.score_sample = sample_scoring_function
            self.type_exact_match = None
        else:
            if isinstance(sample_scoring_function, str):
                if sample_scoring_function not in ["prefix", "suffix", "full"]:
                    raise ValueError(
                        f"type_exact_match (used in parametrized_exact_match) must be one of prefix, suffix, or full. Was {sample_scoring_function} instead."
                    )
                self.type_exact_match = sample_scoring_function
            else:
                self.type_exact_match = "full"
            self.score_sample = self.default_sample_scoring
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src/lighteval/metrics/metrics_sample.py [1326:1343]:
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        self.normalize_gold = normalize_gold
        self.normalize_pred = normalize_pred
        self.strip_strings = strip_strings

        # Managed the logic of the per prediction of sample scoring
        if callable(sample_scoring_function):
            self.score_sample = sample_scoring_function
            self.type_exact_match = None
        else:
            if isinstance(sample_scoring_function, str):
                if sample_scoring_function not in ["prefix", "suffix", "full"]:
                    raise ValueError(
                        f"type_exact_match (used in parametrized_exact_match) must be one of prefix, suffix, or full. Was {sample_scoring_function} instead."
                    )
                self.type_exact_match = sample_scoring_function
            else:
                self.type_exact_match = "full"
            self.score_sample = self.default_sample_scoring
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