"""Tests for metrics calculator
"""

import unittest
from deepeval.scorer import Scorer


class TestScorer(unittest.TestCase):
    pass
    # tests for exact_match_score

    # def test_exact_equal_strings(self):
    #     target = "Winners of the FIFA world cup were the French national football team"
    #     prediction = "Winners of the FIFA world cup were the French national football team"
    #     self.assertEqual(Scorer.exact_match_score(target, prediction), 1)

    # def test_exact_match_score_unequal_strings(self):
    #     target = "Hello, World!"
    #     prediction = "Goodbye, World!"
    #     self.assertEqual(Scorer.exact_match_score(target, prediction), 0)

    # def test_exact_match_score_whitespace_difference(self):
    #     target = "Hello, World!"
    #     prediction = " Hello, World! "
    #     self.assertEqual(Scorer.exact_match_score(target, prediction), 1)

    # def test_exact_match_score_empty_prediction(self):
    #     target = "Hello, World!"
    #     prediction = ""
    #     self.assertEqual(Scorer.exact_match_score(target, prediction), 0)

    # def test_exact_match_score_empty_target(self):
    #     target = ""
    #     prediction = "Hello, World!"
    #     self.assertEqual(Scorer.exact_match_score(target, prediction), 0)

    # test for quasi_exact_match_score

    # def test_exact_match(self):
    #     target = "The quick brown fox"
    #     prediction = "The quick brown fox"
    #     self.assertEqual(Scorer.quasi_exact_match_score(target, prediction), 1)

    # def test_case_insensitive_match(self):
    #     target = "The quick brown fox"
    #     prediction = "the quick brown fox"
    #     self.assertEqual(Scorer.quasi_exact_match_score(target, prediction), 1)

    # def test_partial_match(self):
    #     target = "The quick brown fox"
    #     prediction = "The brown fox"
    #     self.assertEqual(Scorer.quasi_exact_match_score(target, prediction), 0)

    # def test_empty_prediction(self):
    #     target = "The quick brown fox"
    #     prediction = ""
    #     self.assertEqual(Scorer.quasi_exact_match_score(target, prediction), 0)

    # Testing for rouge score 1/2/L

    # def test_rouge_score_rouge1(self):
    #     target = "The quick brown fox"
    #     prediction = "The quick brown fox"
    #     score_type = "rouge1"
    #     rouge_score = Scorer.rouge_score(target, prediction, score_type)
    #     self.assertAlmostEqual(rouge_score, 1.0, places=2)

    # def test_rouge_score_rouge2(self):
    #     target = "The quick brown fox"
    #     prediction = "The quick brown fox"
    #     score_type = "rouge2"
    #     rouge_score = Scorer.rouge_score(target, prediction, score_type)
    #     self.assertAlmostEqual(rouge_score, 1.0, places=2)

    # def test_rouge_score_rougeL(self):
    #     target = "The quick brown fox"
    #     prediction = "The quick brown fox"
    #     score_type = "rougeL"
    #     rouge_score = Scorer.rouge_score(target, prediction, score_type)
    #     self.assertAlmostEqual(rouge_score, 1.0, places=2)

    # Testing sentence BLEU score 1/4

    # def test_sentence_bleu_score_bleu1(self):
    #     references = ["The quick brown fox jumps over the lazy dog"]
    #     prediction = "The quick brown fox jumps over the lazy dog"
    #     bleu_type = "bleu1"
    #     bleu_score = Scorer.sentence_bleu_score(
    #         references, prediction, bleu_type
    #     )
    #     self.assertAlmostEqual(bleu_score, 1.0, places=2)

    # def test_sentence_bleu_score_bleu4(self):
    #     references = ["The quick brown fox jumps over the lazy dog"]
    #     prediction = "The quick brown fox jumps over the lazy dog"
    #     bleu_type = "bleu4"
    #     bleu_score = Scorer.sentence_bleu_score(
    #         references, prediction, bleu_type
    #     )
    #     self.assertAlmostEqual(bleu_score, 1.0, places=2)

    # Adding tests for mismatch for rouge and sentence BLEU

    # def test_rouge_score_mismatch(self):
    #     target = "The quick brown fox"
    #     prediction = "The lazy dog"
    #     score_type = "rouge1"
    #     rouge_score = Scorer.rouge_score(target, prediction, score_type)
    #     self.assertNotAlmostEqual(rouge_score, 0.0, places=2)

    # def test_sentence_bleu_score_mismatch(self):
    #     references = ["The quick brown fox jumps over the lazy dog"]
    #     prediction = "The lazy cat"
    #     bleu_type = "bleu1"
    #     bleu_score = Scorer.sentence_bleu_score(
    #         references, prediction, bleu_type
    #     )
    #     self.assertNotAlmostEqual(bleu_score, 0.0, places=2)

    # # Tests for Bert score

    # def test_bert_score_single_reference_single_prediction(self):
    #     reference = "The quick brown fox jumps over the lazy dog"
    #     prediction = "The quick brown fox jumps over the lazy dog"
    #     bert_scores = Scorer.bert_score(reference, prediction)
    #     self.assertTrue(isinstance(bert_scores, dict))
    #     self.assertIn("bert-precision", bert_scores)
    #     self.assertIn("bert-recall", bert_scores)
    #     self.assertIn("bert-f1", bert_scores)

    # def test_bert_score_single_reference_multiple_predictions(self):
    #     reference = "The quick brown fox jumps over the lazy dog"
    #     predictions = [
    #         "The quick brown fox jumps over the lazy dog",
    #         "A fast brown fox jumps over a sleeping dog",
    #     ]
    #     bert_scores = Scorer.bert_score(reference, predictions)
    #     self.assertTrue(isinstance(bert_scores, dict))
    #     self.assertIn("bert-precision", bert_scores)
    #     self.assertIn("bert-recall", bert_scores)
    #     self.assertIn("bert-f1", bert_scores)

    # def test_bert_score_multiple_references_single_prediction(self):
    #     references = [
    #         "The quick brown fox jumps over the lazy dog",
    #         "A fast brown fox jumps over a sleeping dog",
    #     ]
    #     prediction = "The quick brown fox jumps over the lazy dog"
    #     bert_scores = Scorer.bert_score(references, prediction)
    #     self.assertTrue(isinstance(bert_scores, dict))
    #     self.assertIn("bert-precision", bert_scores)
    #     self.assertIn("bert-recall", bert_scores)
    #     self.assertIn("bert-f1", bert_scores)

    # def test_bert_score_multiple_references_multiple_predictions(self):
    #     references = [
    #         "The quick brown fox jumps over the lazy dog",
    #         "A fast brown fox jumps over a sleeping dog",
    #     ]
    #     predictions = [
    #         "The quick brown fox jumps over the lazy dog",
    #         "A fast brown fox jumps over a sleeping dog",
    #     ]
    #     bert_scores = Scorer.bert_score(references, predictions)
    #     self.assertTrue(isinstance(bert_scores, dict))
    #     self.assertIn("bert-precision", bert_scores)
    #     self.assertIn("bert-recall", bert_scores)
    #     self.assertIn("bert-f1", bert_scores)

    # # Tests for faithfulness score

    # def test_faithfulness_score_identical_strings(self):
    #     target = "This is an example target text."
    #     prediction = "This is an example target text."
    #     # When the target and prediction are identical, the faithfulness score should be 1.0.
    #     self.assertAlmostEqual(
    #         1.0 - Scorer.faithfulness_score(target, prediction), 0.0, places=1
    #     )

    # def test_faithfulness_score_different_strings(self):
    #     target = "The quick brown fox jumps over the lazy dog."
    #     prediction = "A fast brown fox leaped over the sleeping dog."
    #     # When the prediction is different from the target, the faithfulness score should be less than 1.0.
    #     self.assertLess(Scorer.faithfulness_score(target, prediction), 1.0)

    # Tests for toxicity scores
    # Todo: add more different tests for dict and different types of models.

    # TODO: uncomment once detoxify fixed
    # def test_neural_toxic_score_original_model(self):
    #     prediction = "This is a non-toxic text."
    #     mean_toxicity_score, _ = Scorer.neural_toxic_score(
    #         prediction, model="original"
    #     )
    #     self.assertTrue(0 <= mean_toxicity_score <= 1)

    # def test_neural_toxic_score_unbiased_model(self):
    #     prediction = "This is a non-toxic text."
    #     mean_toxicity_score, _ = Scorer.neural_toxic_score(
    #         prediction, model="unbiased"
    #     )
    #     self.assertTrue(0 <= mean_toxicity_score <= 1)

    # def test_neural_toxic_score_multilingual_model(self):
    #     prediction = "This is a non-toxic text."
    #     mean_toxicity_score, _ = Scorer.neural_toxic_score(
    #         prediction, model="multilingual"
    #     )
    #     self.assertTrue(0 <= mean_toxicity_score <= 1)

    # def test_neural_toxic_score_default_model(self):
    #     prediction = "This is a non-toxic text."
    #     mean_toxicity_score, _ = Scorer.neural_toxic_score(prediction)
    #     self.assertTrue(0 <= mean_toxicity_score <= 1)

    # def test_neural_toxic_score_invalid_model(self):
    #     prediction = "This is a non-toxic text."
    #     with self.assertRaises(AssertionError):
    #         Scorer.neural_toxic_score(prediction, model="invalid_model")

    # def test_hallucination_score(self):
    #     prediction = "A blond drinking water in public."
    #     source = "A man with blond-hair, and a brown shirt drinking out of a public water fountain."
    #     score = Scorer.hallucination_score(source, prediction)
    #     self.assertTrue(0 <= score <= 1)