import unittest
from unittest.mock import MagicMock
from cognitum.model import Model
from cognitum.types import Dataset, Predictions
from cognitum.exceptions import ModelError

class TestModel(unittest.TestCase):

    def setUp(self):
        # Mock the language model
        self.mock_model = MagicMock()
        
        # Example prompt and valid labels
        self.prompt = "Classify the following text: {text}"
        self.valid_labels = ["label1", "label2", "label3"]
        
        # Initialize the Model with the mock model
        self.model = Model(prompt=self.prompt, valid_labels=self.valid_labels, model=self.mock_model)
        
        # Example dataset
        self.dataset = [
            ("id1", "text1"),
            ("id2", "text2"),
            ("id3", "text3")
        ]

    def test_predict(self):
        # Mock the _get_model_prediction method
        self.model._get_model_prediction = MagicMock(return_value=[
            ("text1", ["label1"]),
            ("text2", ["label2"]),
            ("text3", ["label3"])
        ])
        
        predictions = self.model.predict(self.dataset)
        expected_predictions = [
            ("id1", ["label1"]),
            ("id2", ["label2"]),
            ("id3", ["label3"])
        ]
        
        self.assertEqual(predictions, expected_predictions)

    def test_predict_with_confidences(self):
        # Mock the _get_model_prediction method with confidences
        self.model._get_model_prediction = MagicMock(return_value=[
            ("text1", ["label1"], [0.9]),
            ("text2", ["label2"], [0.8]),
            ("text3", ["label3"], [0.7])
        ])
        
        predictions = self.model.predict(self.dataset, return_confidences=True)
        expected_predictions = [
            ("id1", ["label1"], [0.9]),
            ("id2", ["label2"], [0.8]),
            ("id3", ["label3"], [0.7])
        ]
        
        self.assertEqual(predictions, expected_predictions)

    def test_evaluate(self):
        # Placeholder test for evaluate method
        # You would need to implement this based on the actual logic
        evaluation_metrics = self.model.evaluate(self.dataset, self.dataset)
        self.assertEqual(evaluation_metrics.exact, 1.0)
        self.assertEqual(evaluation_metrics.partial, 0.0)
        self.assertEqual(evaluation_metrics.false_positives, 0.0)

    def test_invalid_model_prediction(self):
        # Test handling of invalid model output
        self.model._get_model_prediction = MagicMock(side_effect=ModelError("Invalid format"))
        
        with self.assertRaises(ModelError):
            self.model.predict(self.dataset)

if __name__ == '__main__':
    unittest.main()
