from __future__ import annotations

from typing_extensions import deprecated

from sentence_transformers import InputExample
from sentence_transformers.cross_encoder.evaluation.CEClassificationEvaluator import CEClassificationEvaluator


@deprecated(
    "This evaluator has been deprecated in favor of the more general CEClassificationEvaluator. "
    "Please use CEClassificationEvaluator instead, which supports both binary and multi-class "
    "evaluation. It accepts approximately the same inputs as this evaluator."
)
class CESoftmaxAccuracyEvaluator(CEClassificationEvaluator):
    """
    This evaluator has been deprecated in favor of the more general CEClassificationEvaluator.
    """

    @classmethod
    def from_input_examples(cls, examples: list[InputExample], **kwargs):
        sentence_pairs = []
        labels = []

        for example in examples:
            sentence_pairs.append(example.texts)
            labels.append(example.label)
        return cls(sentence_pairs, labels, **kwargs)

from __future__ import annotations

import csv
import logging
import os

import numpy as np

from sentence_transformers import InputExample

logger = logging.getLogger(__name__)


class CESoftmaxAccuracyEvaluator:
    """
    This evaluator can be used with the CrossEncoder class.

    It is designed for CrossEncoders with 2 or more outputs. It measure the
    accuracy of the predict class vs. the gold labels.
    """

    def __init__(self, sentence_pairs: list[list[str]], labels: list[int], name: str = "", write_csv: bool = True):
        self.sentence_pairs = sentence_pairs
        self.labels = labels
        self.name = name

        self.csv_file = "CESoftmaxAccuracyEvaluator" + ("_" + name if name else "") + "_results.csv"
        self.csv_headers = ["epoch", "steps", "Accuracy"]
        self.write_csv = write_csv

    @classmethod
    def from_input_examples(cls, examples: list[InputExample], **kwargs):
        sentence_pairs = []
        labels = []

        for example in examples:
            sentence_pairs.append(example.texts)
            labels.append(example.label)
        return cls(sentence_pairs, labels, **kwargs)

    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
        if epoch != -1:
            if steps == -1:
                out_txt = f" after epoch {epoch}:"
            else:
                out_txt = f" in epoch {epoch} after {steps} steps:"
        else:
            out_txt = ":"

        logger.info("CESoftmaxAccuracyEvaluator: Evaluating the model on " + self.name + " dataset" + out_txt)
        pred_scores = model.predict(self.sentence_pairs, convert_to_numpy=True, show_progress_bar=False)
        pred_labels = np.argmax(pred_scores, axis=1)

        assert len(pred_labels) == len(self.labels)

        acc = np.sum(pred_labels == self.labels) / len(self.labels)

        logger.info(f"Accuracy: {acc * 100:.2f}")

        if output_path is not None and self.write_csv:
            csv_path = os.path.join(output_path, self.csv_file)
            output_file_exists = os.path.isfile(csv_path)
            with open(csv_path, mode="a" if output_file_exists else "w", encoding="utf-8") as f:
                writer = csv.writer(f)
                if not output_file_exists:
                    writer.writerow(self.csv_headers)

                writer.writerow([epoch, steps, acc])

        return acc
