import pytest
from unittest.mock import MagicMock, patch
from transformers import AutoModelForCausalLM, AutoTokenizer

@patch("model.AutoTokenizer")
@patch("model.PydanticOutputParser")
def test_generate_answer_success(self, MockParser, MockTokenizer):
    # 1. Setup Mock Tokenizer
    mock_tokenizer = MockTokenizer.from_pretrained.return_value
    mock_tokenizer.return_value = {"input_ids": torch.tensor([[1, 2, 3]])}
    mock_tokenizer.decode.return_value = '{"answer": "Pikachu is electric type", "source": "Pokedex"}'

    # 2. Setup Mock Model
    mock_model = MagicMock()
    mock_model.generate.return_value = torch.tensor([[1, 2, 3, 4, 5]])

    # 3. Setup Mock Parser (to return a dummy Pydantic object)
    mock_parser = MockParser.return_value
    expected_response = MagicMock()
    mock_parser.parse.return_value = expected_response

    # 4. Execute
    prompt = "Tell me about Pikachu"
    result = generate_answer(mock_model, mock_tokenizer, prompt)

    # 5. Assertions
    self.assertEqual(result, expected_response)
    mock_model.generate.assert_called_once()
    mock_tokenizer.decode.assert_called_once()