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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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from __future__ import annotations

"""Tests for the Response Evaluator."""

from google.adk.dependencies.vertexai import vertexai
from google.adk.evaluation.eval_case import Invocation
from google.adk.evaluation.eval_metrics import PrebuiltMetrics
from google.adk.evaluation.evaluator import EvalStatus
from google.adk.evaluation.response_evaluator import ResponseEvaluator
from google.genai import types as genai_types
import pytest

vertexai_types = vertexai.types


class TestResponseEvaluator:
  """A class to help organize "patch" that are applicable to all tests."""

  def test_evaluate_invocations_rouge_metric(self, mocker):
    """Test evaluate_invocations function for Rouge metric."""
    mock_perform_eval = mocker.patch(
        "google.adk.evaluation.vertex_ai_eval_facade._VertexAiEvalFacade._perform_eval"
    )
    actual_invocations = [
        Invocation(
            user_content=genai_types.Content(
                parts=[genai_types.Part(text="This is a test query.")]
            ),
            final_response=genai_types.Content(
                parts=[
                    genai_types.Part(text="This is a test candidate response.")
                ]
            ),
        )
    ]
    expected_invocations = [
        Invocation(
            user_content=genai_types.Content(
                parts=[genai_types.Part(text="This is a test query.")]
            ),
            final_response=genai_types.Content(
                parts=[genai_types.Part(text="This is a test reference.")]
            ),
        )
    ]
    evaluator = ResponseEvaluator(
        threshold=0.8, metric_name="response_match_score"
    )

    evaluation_result = evaluator.evaluate_invocations(
        actual_invocations, expected_invocations
    )

    assert evaluation_result.overall_score == pytest.approx(8 / 11)
    # ROUGE-1 F1 is approx. 0.73 < 0.8 threshold, so eval status is FAILED.
    assert evaluation_result.overall_eval_status == EvalStatus.FAILED
    mock_perform_eval.assert_not_called()  # Ensure _perform_eval was not called

  def test_evaluate_invocations_coherence_metric_passed(self, mocker):
    """Test evaluate_invocations function for Coherence metric."""
    mock_perform_eval = mocker.patch(
        "google.adk.evaluation.vertex_ai_eval_facade._VertexAiEvalFacade._perform_eval"
    )
    actual_invocations = [
        Invocation(
            user_content=genai_types.Content(
                parts=[genai_types.Part(text="This is a test query.")]
            ),
            final_response=genai_types.Content(
                parts=[
                    genai_types.Part(text="This is a test candidate response.")
                ]
            ),
        )
    ]
    expected_invocations = [
        Invocation(
            user_content=genai_types.Content(
                parts=[genai_types.Part(text="This is a test query.")]
            ),
            final_response=genai_types.Content(
                parts=[genai_types.Part(text="This is a test reference.")]
            ),
        )
    ]
    evaluator = ResponseEvaluator(
        threshold=0.8, metric_name="response_evaluation_score"
    )
    # Mock the return value of _perform_eval
    mock_perform_eval.return_value = vertexai_types.EvaluationResult(
        summary_metrics=[vertexai_types.AggregatedMetricResult(mean_score=0.9)],
        eval_case_results=[],
    )

    evaluation_result = evaluator.evaluate_invocations(
        actual_invocations, expected_invocations
    )

    assert evaluation_result.overall_score == 0.9
    assert evaluation_result.overall_eval_status == EvalStatus.PASSED
    mock_perform_eval.assert_called_once()
    _, mock_kwargs = mock_perform_eval.call_args
    # Compare the names of the metrics.
    assert [m.name for m in mock_kwargs["metrics"]] == [
        vertexai_types.PrebuiltMetric.COHERENCE.name
    ]
