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# Source code for langchain_community.example_selectors.ngram_overlap

    """Select and order examples based on ngram overlap score (sentence_bleu score).

    https://www.nltk.org/_modules/nltk/translate/bleu_score.html
    https://aclanthology.org/P02-1040.pdf
    """
    from typing import Dict, List

    import numpy as np
    from langchain_core.example_selectors import BaseExampleSelector
    from langchain_core.prompts import PromptTemplate
    from langchain_core.pydantic_v1 import BaseModel, root_validator

    def ngram_overlap_score(source: List[str], example: List[str]) -> float:
        """Compute ngram overlap score of source and example as sentence_bleu score
        from NLTK package.

        Use sentence_bleu with method1 smoothing function and auto reweighting.
        Return float value between 0.0 and 1.0 inclusive.
        https://www.nltk.org/_modules/nltk/translate/bleu_score.html
        https://aclanthology.org/P02-1040.pdf
        """
        from nltk.translate.bleu_score import (
            SmoothingFunction,  # type: ignore
            sentence_bleu,
        )

        hypotheses = source[0].split()
        references = [s.split() for s in example]

        return float(
            sentence_bleu(
                references,
                hypotheses,
                smoothing_function=SmoothingFunction().method1,
                auto_reweigh=True,
            )
        )

    class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel):
        """Select and order examples based on ngram overlap score (sentence_bleu score
        from NLTK package).

        https://www.nltk.org/_modules/nltk/translate/bleu_score.html
        https://aclanthology.org/P02-1040.pdf
        """

        examples: List[dict]
        """A list of the examples that the prompt template expects."""

        example_prompt: PromptTemplate
        """Prompt template used to format the examples."""

        threshold: float = -1.0
        """Threshold at which algorithm stops. Set to -1.0 by default.

        For negative threshold:
        select_examples sorts examples by ngram_overlap_score, but excludes none.
        For threshold greater than 1.0:
        select_examples excludes all examples, and returns an empty list.
        For threshold equal to 0.0:
        select_examples sorts examples by ngram_overlap_score,
        and excludes examples with no ngram overlap with input.
        """

        @root_validator(pre=True)
        def check_dependencies(cls, values: Dict) -> Dict:
            """Check that valid dependencies exist."""
            try:
                from nltk.translate.bleu_score import (  # noqa: F401
                    SmoothingFunction,
                    sentence_bleu,
                )
            except ImportError as e:
                raise ImportError(
                    "Not all the correct dependencies for this ExampleSelect exist."
                    "Please install nltk with `pip install nltk`."
                ) from e

            return values

        def add_example(self, example: Dict[str, str]) -> None:
            """Add new example to list."""
            self.examples.append(example)

        def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
            """Return list of examples sorted by ngram_overlap_score with input.

            Descending order.
            Excludes any examples with ngram_overlap_score less than or equal to threshold.
            """
            inputs = list(input_variables.values())
            examples = []
            k = len(self.examples)
            score = [0.0] * k
            first_prompt_template_key = self.example_prompt.input_variables[0]

            for i in range(k):
                score[i] = ngram_overlap_score(
                    inputs, [self.examples[i][first_prompt_template_key]]
                )

            while True:
                arg_max = np.argmax(score)
                if (score[arg_max] < self.threshold) or abs(
                    score[arg_max] - self.threshold
                ) < 1e-9:
                    break

                examples.append(self.examples[arg_max])
                score[arg_max] = self.threshold - 1.0

            return examples