# Copyright 2025 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.

from __future__ import annotations

from google.adk.evaluation.eval_case import Invocation
from google.adk.evaluation.eval_metrics import BaseCriterion
from google.adk.evaluation.eval_metrics import EvalMetric
from google.adk.evaluation.eval_metrics import EvalStatus
from google.adk.evaluation.eval_metrics import JudgeModelOptions
from google.adk.evaluation.eval_metrics import PrebuiltMetrics
from google.adk.evaluation.evaluator import PerInvocationResult
from google.adk.evaluation.final_response_match_v2 import _parse_critique
from google.adk.evaluation.final_response_match_v2 import FinalResponseMatchV2Evaluator
from google.adk.evaluation.llm_as_judge import AutoRaterScore
from google.adk.evaluation.llm_as_judge_utils import Label
from google.adk.models.llm_response import LlmResponse
from google.genai import types as genai_types
import pytest


@pytest.mark.parametrize(
    "response_text",
    [
        """```json
  {
    "is_the_agent_response_valid_or_invalid": "valid",
    "reasoning": "The response is valid."
  }
  ```""",
        """```json
  {
    "is_the_agent_response_valid": "undefined label",
  }
  ```""",
    ],
)
def test_parse_critique_label_not_found(response_text):
  label = _parse_critique(response_text)
  assert label == Label.NOT_FOUND


@pytest.mark.parametrize(
    "response_text",
    [
        """```json
  {
    "is_the_agent_response_valid": "valid",
    "reasoning": "The response is valid."
  }
  ```""",
        """```json
  {
    "is_the_agent_response_valid": ["valid"],
    "reasoning": "The response is valid."
  }
  ```""",
        """```json
  {
    "is_the_agent_response_valid":\n    [ "valid\n"],
    "reasoning": "The response is valid."
  }
  ```""",
    ],
)
def test_parse_critique(response_text):
  label = _parse_critique(response_text)
  assert label == Label.VALID


@pytest.mark.parametrize(
    "response_text",
    [
        """```json
  {
    "is_the_agent_response_invalid": "invalid",
    "reasoning": "The response is invalid."
  }
  ```""",
        """```json
  {
    "is_the_agent_response_invalid": ["invalid"],
    "reasoning": "The response is invalid."
  }
  ```""",
        """```json
  {
    "is_the_agent_response_invalid":\n    [ "invalid\n"],
    "reasoning": "The response is invalid."
  }
  ```""",
    ],
)
def test_parse_critique_invalid(response_text):
  label = _parse_critique(response_text)
  assert label == Label.INVALID


def create_test_template() -> str:
  return """
This is a test template.

{{
  "User prompt": {prompt},
  "Agent response": {response},
  "Reference response": {golden_response},
}}

The answer should be a json alone which follows the json structure below:
{{
  "is_the_agent_response_valid": [valid or invalid],
  "reasoning":
}}
"""


def _create_test_evaluator_gemini(
    threshold: float,
) -> FinalResponseMatchV2Evaluator:
  evaluator = FinalResponseMatchV2Evaluator(
      EvalMetric(
          metric_name="final_response_match_v2",
          threshold=threshold,
          criterion=BaseCriterion(
              threshold=0.5,
          ),
      ),
  )
  evaluator._auto_rater_prompt_template = create_test_template()
  return evaluator


def _create_test_invocations(
    candidate: str, reference: str
) -> tuple[Invocation, Invocation]:
  """Returns tuple of (actual_invocation, expected_invocation)."""
  actual_invocation = Invocation(
      user_content=genai_types.Content(
          parts=[genai_types.Part(text="This is a test query.")],
          role="user",
      ),
      final_response=genai_types.Content(
          parts=[genai_types.Part(text=candidate)],
          role="model",
      ),
  )
  expected_invocation = Invocation(
      user_content=genai_types.Content(
          parts=[genai_types.Part(text="This is a test query.")],
          role="user",
      ),
      final_response=genai_types.Content(
          parts=[genai_types.Part(text=reference)],
          role="model",
      ),
  )
  return actual_invocation, expected_invocation


def test_format_auto_rater_prompt():
  evaluator = _create_test_evaluator_gemini(threshold=0.8)
  actual_invocation, expected_invocation = _create_test_invocations(
      "candidate text", "reference text"
  )
  prompt = evaluator.format_auto_rater_prompt(
      actual_invocation, expected_invocation
  )
  assert prompt == """
This is a test template.

{
  "User prompt": This is a test query.,
  "Agent response": candidate text,
  "Reference response": reference text,
}

The answer should be a json alone which follows the json structure below:
{
  "is_the_agent_response_valid": [valid or invalid],
  "reasoning":
}
"""


def test_convert_auto_rater_response_to_score_valid():
  evaluator = _create_test_evaluator_gemini(threshold=0.8)
  auto_rater_response = """```json
{
  "is_the_agent_response_valid": "valid",
  "reasoning": "The response is valid."
}
```"""
  llm_response = LlmResponse(
      content=genai_types.Content(
          parts=[genai_types.Part(text=auto_rater_response)],
          role="model",
      )
  )
  auto_rater_score = evaluator.convert_auto_rater_response_to_score(
      llm_response
  )
  assert auto_rater_score == AutoRaterScore(score=1.0)


def test_convert_auto_rater_response_to_score_invalid():
  evaluator = _create_test_evaluator_gemini(threshold=0.8)
  auto_rater_response = """```json
{
  "is_the_agent_response_valid": "invalid",
  "reasoning": "The response is invalid."
}
```"""
  llm_response = LlmResponse(
      content=genai_types.Content(
          parts=[genai_types.Part(text=auto_rater_response)],
          role="model",
      )
  )
  auto_rater_score = evaluator.convert_auto_rater_response_to_score(
      llm_response
  )
  assert auto_rater_score == AutoRaterScore(score=0.0)


def test_convert_auto_rater_response_to_score_invalid_json():
  evaluator = _create_test_evaluator_gemini(threshold=0.8)
  llm_response = LlmResponse(
      content=genai_types.Content(
          parts=[genai_types.Part(text="invalid json")],
          role="model",
      )
  )
  auto_rater_score = evaluator.convert_auto_rater_response_to_score(
      llm_response
  )
  assert auto_rater_score == AutoRaterScore()


def test_convert_auto_rater_response_to_score_missing_key():
  evaluator = _create_test_evaluator_gemini(threshold=0.8)
  llm_response = LlmResponse(
      content=genai_types.Content(
          parts=[genai_types.Part(text="{}")],
          role="model",
      )
  )
  auto_rater_score = evaluator.convert_auto_rater_response_to_score(
      llm_response
  )
  assert auto_rater_score == AutoRaterScore()


def test_aggregate_per_invocation_samples_none_evaluated():
  evaluator = _create_test_evaluator_gemini(threshold=0.5)

  actual_invocation, expected_invocation = _create_test_invocations(
      "candidate text", "reference text"
  )

  per_invocation_result_samples = [
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=None,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=None,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
  ]

  assert (
      evaluator.aggregate_per_invocation_samples(per_invocation_result_samples)
      == per_invocation_result_samples[0]
  )


def test_aggregate_per_invocation_samples_valid():
  evaluator = _create_test_evaluator_gemini(threshold=0.5)

  actual_invocation, expected_invocation = _create_test_invocations(
      "candidate text", "reference text"
  )

  per_invocation_result_samples = [
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.FAILED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.FAILED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=None,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
  ]

  per_invocation_result = evaluator.aggregate_per_invocation_samples(
      per_invocation_result_samples
  )

  assert per_invocation_result.score == 1.0
  assert per_invocation_result.eval_status == EvalStatus.PASSED


def test_aggregate_per_invocation_samples_invalid():
  evaluator = _create_test_evaluator_gemini(threshold=0.5)

  actual_invocation, expected_invocation = _create_test_invocations(
      "candidate text", "reference text"
  )

  per_invocation_result_samples = [
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.FAILED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.FAILED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.FAILED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=None,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
  ]

  per_invocation_result = evaluator.aggregate_per_invocation_samples(
      per_invocation_result_samples
  )

  assert per_invocation_result.score == 0.0
  assert per_invocation_result.eval_status == EvalStatus.FAILED


def test_aggregate_invocation_results():
  evaluator = _create_test_evaluator_gemini(threshold=0.5)

  actual_invocation, expected_invocation = _create_test_invocations(
      "candidate text", "reference text"
  )

  per_invocation_results = [
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=1.0,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.FAILED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=0.0,
          eval_status=EvalStatus.FAILED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=None,
          eval_status=EvalStatus.PASSED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=100.0,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
      PerInvocationResult(
          actual_invocation=actual_invocation,
          expected_invocation=expected_invocation,
          score=None,
          eval_status=EvalStatus.NOT_EVALUATED,
      ),
  ]

  aggregated_result = evaluator.aggregate_invocation_results(
      per_invocation_results
  )

  # Only 4 / 8 invocations are evaluated, and 2 / 4 are valid.
  assert aggregated_result.overall_score == 0.5
  assert aggregated_result.overall_eval_status == EvalStatus.PASSED


def test_get_metric_info():
  """Test get_metric_info function for Final Response Match V2 metric."""
  metric_info = FinalResponseMatchV2Evaluator.get_metric_info()
  assert (
      metric_info.metric_name == PrebuiltMetrics.FINAL_RESPONSE_MATCH_V2.value
  )
  assert metric_info.metric_value_info.interval.min_value == 0.0
  assert metric_info.metric_value_info.interval.max_value == 1.0
