# -*- coding: utf-8 -*-

# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""Model-based Pairwise Metric."""

from typing import Callable, Optional, Union
import warnings

from vertexai import generative_models
from vertexai.evaluation.metrics import _base
from vertexai.evaluation.metrics import (
    metric_prompt_template as metric_prompt_template_base,
)


class PairwiseMetric(_base._ModelBasedMetric):  # pylint: disable=protected-access
    """A Model-based Pairwise Metric.

    A model-based evaluation metric that compares two generative models' responses
    side-by-side, and allows users to A/B test their generative models to
    determine which model is performing better.

    For more details on when to use pairwise metrics, see
    [Evaluation methods and
    metrics](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval#pointwise_versus_pairwise).

    Result Details:

        * In `EvalResult.summary_metrics`, win rates for both the baseline and
        candidate model are computed. The win rate is computed as proportion of
        wins of one model's responses to total attempts as a decimal value
        between 0 and 1.

        * In `EvalResult.metrics_table`, a pairwise metric produces two
        evaluation results per dataset row:
            * `pairwise_choice`: The choice shows whether the candidate model or
              the baseline model performs better, or if they are equally good.
            * `explanation`: The rationale behind each verdict using
              chain-of-thought reasoning. The explanation helps users scrutinize
              the judgment and builds appropriate trust in the decisions.

        See [documentation
        page](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval#understand-results)
        for more details on understanding the metric results.

    Usage Examples:

        ```
        baseline_model = "gemini-2.5-pro"
        candidate_model = "gemini-2.5-flash"

        pairwise_groundedness = PairwiseMetric(
            metric_prompt_template=MetricPromptTemplateExamples.get_prompt_template(
                "pairwise_groundedness"
            ),
            baseline_model=baseline_model,
        )
        eval_dataset = pd.DataFrame({
              "prompt"  : [...],
        })
        pairwise_task = EvalTask(
            dataset=eval_dataset,
            metrics=[pairwise_groundedness],
            experiment="my-pairwise-experiment",
        )
        pairwise_result = pairwise_task.evaluate(
            model=candidate_model,
            experiment_run_name="gemini-pairwise-eval-run",
        )
        ```
    """

    def __init__(
        self,
        *,
        metric: str,
        metric_prompt_template: Union[
            metric_prompt_template_base.PairwiseMetricPromptTemplate, str
        ],
        baseline_model: Optional[
            Union[str, generative_models.GenerativeModel, Callable[[str], str]]
        ] = None,
    ):
        """Initializes a pairwise evaluation metric.

        Args:
          metric: The pairwise evaluation metric name.
          metric_prompt_template: Pairwise metric prompt template for performing
            the pairwise model-based evaluation. A freeform string is also accepted.
          baseline_model: A model name string, a GenerativeModel instance, or a
            custom model function for the baseline model for side-by-side
            comparison. If not specified, `baseline_model_response` column is
            required in the dataset to perform bring-your-own-response(BYOR)
            evaluation.
        """
        super().__init__(
            metric_prompt_template=metric_prompt_template,
            metric=metric,
        )
        if isinstance(baseline_model, generative_models.GenerativeModel):
            warnings.warn(
                "vertexai.generative_models.GenerativeModel is deprecated for "
                "evaluation and will be removed in June 2026. Please pass a string "
                "model name instead.",
                DeprecationWarning,
                stacklevel=2,
            )
        self._baseline_model = baseline_model

    @property
    def baseline_model(
        self,
    ) -> Union[str, generative_models.GenerativeModel, Callable[[str], str]]:
        return self._baseline_model
