feat: Add word sorting task generation with text transformations

tests/test_word_sorting.py

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+"""Tests for word sorting task generation"""
+
+import pytest
+
+from reasoning_gym.algorithmic.word_sorting import WordSortingConfig, WordSortingDataset, TextTransformation
+
+
+def test_word_sorting_config_validation():
+    """Test that invalid configs raise appropriate errors"""
+    with pytest.raises(AssertionError):
+        config = WordSortingConfig(min_words=0)
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = WordSortingConfig(min_words=10, max_words=5)
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = WordSortingConfig(min_word_length=0)
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = WordSortingConfig(min_word_length=10, max_word_length=5)
+        config.validate()
+
+
+def test_word_sorting_dataset_deterministic():
+    """Test that dataset generates same items with same seed"""
+    config = WordSortingConfig(seed=42, size=10)
+    dataset1 = WordSortingDataset(config)
+    dataset2 = WordSortingDataset(config)
+
+    for i in range(len(dataset1)):
+        assert dataset1[i] == dataset2[i]
+
+
+def test_word_sorting_transformations():
+    """Test different text transformations"""
+    seed = 42
+    size = 5
+    
+    # Test LOWERCASE
+    config = WordSortingConfig(transformation=TextTransformation.LOWERCASE, seed=seed, size=size)
+    dataset = WordSortingDataset(config)
+    for item in dataset:
+        for word in item["metadata"]["transformed_words"]:
+            assert word.islower()
+
+    # Test UPPERCASE
+    config = WordSortingConfig(transformation=TextTransformation.UPPERCASE, seed=seed, size=size)
+    dataset = WordSortingDataset(config)
+    for item in dataset:
+        for word in item["metadata"]["transformed_words"]:
+            assert word.isupper()
+
+    # Test ORIGINAL
+    config = WordSortingConfig(transformation=TextTransformation.ORIGINAL, seed=seed, size=size)
+    dataset = WordSortingDataset(config)
+    for item in dataset:
+        assert item["metadata"]["original_words"] == item["metadata"]["transformed_words"]
+
+
+def test_word_sorting_dataset_items():
+    """Test basic properties of generated items"""
+    config = WordSortingConfig(
+        min_words=3,
+        max_words=6,
+        min_word_length=3,
+        max_word_length=8,
+        size=10,
+        seed=42
+    )
+    dataset = WordSortingDataset(config)
+
+    for i in range(len(dataset)):
+        item = dataset[i]
+        # Check item structure
+        assert isinstance(item, dict)
+        assert "question" in item
+        assert "answer" in item
+        assert "metadata" in item
+
+        # Check metadata
+        assert "original_words" in item["metadata"]
+        assert "transformed_words" in item["metadata"]
+        assert "direction" in item["metadata"]
+        assert "transformation" in item["metadata"]
+        assert "sorted_words" in item["metadata"]
+
+        # Verify word count constraints
+        words = item["metadata"]["transformed_words"]
+        assert len(words) >= config.min_words
+        assert len(words) <= config.max_words
+
+        # Verify word length constraints
+        for word in words:
+            assert len(word) >= config.min_word_length
+            assert len(word) <= config.max_word_length
+
+        # Verify sorting
+        direction = item["metadata"]["direction"]
+        sorted_words = eval(item["answer"])
+        if direction == "ascending":
+            assert sorted_words == sorted(sorted_words)
+        else:
+            assert sorted_words == sorted(sorted_words, reverse=True)
+
+
+def test_word_sorting_dataset_iteration():
+    """Test that iteration respects dataset size"""
+    config = WordSortingConfig(size=5, seed=42)
+    dataset = WordSortingDataset(config)
+
+    items = list(dataset)
+    assert len(items) == config.size
+
+    # Test multiple iterations yield same items
+    assert items == list(dataset)