Revert "figlet font curriculum"

This reverts commit 29bf78293f.

reasoning_gym/cognition/__init__.py

@@ -3,7 +3,7 @@ Cognition tasks for training reasoning capabilities.
 """
 
 from .color_cube_rotation import ColorCubeRotationConfig, ColorCubeRotationCurriculum, ColorCubeRotationDataset
-from .figlet_fonts import FigletFontConfig, FigletFontCurriculum, FigletFontDataset
+from .figlet_fonts import FigletFontConfig, FigletFontDataset
 from .modulo_grid import ModuloGridConfig, ModuloGridDataset
 from .needle_haystack import NeedleHaystackConfig, NeedleHaystackCurriculum, NeedleHaystackDataset
 from .number_sequences import NumberSequenceConfig, NumberSequenceCurriculum, NumberSequenceDataset
@@ -16,7 +16,6 @@ __all__ = [
     "ColorCubeRotationCurriculum",
     "FigletFontConfig",
     "FigletFontDataset",
-    "FigletFontCurriculum",
     "NumberSequenceConfig",
     "NumberSequenceDataset",
     "NumberSequenceCurriculum",

reasoning_gym/cognition/figlet_fonts.py

@@ -1,16 +1,55 @@
-import json
 from dataclasses import dataclass
 from random import Random
 from typing import Any, Optional
 
 import pyfiglet
 
-from ..coaching import AttributeType, BaseCurriculum, RangeAttributeDefinition
-from ..data import get_data_file_path
 from ..factory import ProceduralDataset, register_dataset
 
+
+@dataclass
+class FigletFontConfig:
+    """Configuration for FigletFont task generation"""
+
+    static_word: Optional[str] = None
+    static_font: Optional[str] = None
+    space_letters: bool = True
+    seed: Optional[int] = None
+    size: int = 500
+
+
+class FigletFontDataset(ProceduralDataset):
+    """Generates FigletFont tasks"""
+
+    def __init__(self, config: FigletFontConfig):
+        from ..data.wordle_words import wordle_words
+
+        self.wordle_words = wordle_words
+        self._prompt_templates = [
+            "What word does this say?\n\n{figlet_render}",
+            "Please read the following figlet font:\n\n{figlet_render}",
+        ]
+        super().__init__(config=config, seed=config.seed, size=config.size)
+
+    def __getitem__(self, idx: int) -> dict:
+        """Generate a single FigletFont task
+
+        Returns:
+            dict with keys:
+                - question: str, the task description with figlet string
+                - answer: str, the figlet encoded word
+                - metadata: dict with generation parameters
+        """
+        rng = Random(self.seed + idx)
+
+        word = self.config.static_word if self.config.static_word is not None else rng.choice(self.wordle_words).upper()
+        if self.config.space_letters:
+            render_word = " ".join(word)
+        else:
+            render_word = word
+
         # These ones are funky and probably aren't good for train/testing
-BAD_FONTS = [
+        bad_fonts = [
             "pyramid",
             "runyc",
             "assalt_m",
@@ -117,69 +156,9 @@ BAD_FONTS = [
             "rally_sp",
             "charset_",
         ]
-ALL_FONTS = pyfiglet.FigletFont.getFonts()
-OK_FONTS = list(filter(lambda x: x not in BAD_FONTS, ALL_FONTS))
-
-
-@dataclass
-class FigletFontConfig:
-    """Configuration for FigletFont task generation"""
-
-    static_word: Optional[str] = None
-    static_font: Optional[str] = None
-    min_word_len: int = 3
-    max_word_len: int = 7
-    space_letters: bool = True
-    seed: Optional[int] = None
-    size: int = 500
-
-    def validate(self):
-        assert self.min_word_len > 0, "min_word_len must be greater than 0"
-        assert self.min_word_len <= self.max_word_len, "min_word_len must be less than or equal to max_word_len"
-        if self.static_word:
-            assert len(self.static_word) > 0, "static_word must have at least one character"
-        if self.static_font:
-            assert len(self.static_font) > 0, "static_font must have at least one character"
-            assert self.static_font in OK_FONTS, f"static_font must be one of {OK_FONTS}"
-
-
-class FigletFontDataset(ProceduralDataset):
-    """Generates FigletFont tasks"""
-
-    def __init__(self, config: FigletFontConfig):
-        with get_data_file_path("anagrams.jsonl").open() as f:
-            self.words = [
-                word
-                for line in f
-                for word in json.loads(line)["words"]
-                if config.min_word_len <= len(word) <= config.max_word_len
-            ]
-            assert len(self.words) > 0, "No words found in the dataset with the specified length range"
-
-        self._prompt_templates = [
-            "What word does this say?\n\n{figlet_render}",
-            "Please read the following figlet font:\n\n{figlet_render}",
-        ]
-        super().__init__(config=config, seed=config.seed, size=config.size)
-
-    def __getitem__(self, idx: int) -> dict:
-        """Generate a single FigletFont task
-
-        Returns:
-            dict with keys:
-                - question: str, the task description with figlet string
-                - answer: str, the figlet encoded word
-                - metadata: dict with generation parameters
-        """
-        rng = Random(self.seed + idx)
-
-        word = self.config.static_word if self.config.static_word is not None else rng.choice(self.words).upper()
-        if self.config.space_letters:
-            render_word = " ".join(word)
-        else:
-            render_word = word
-
-        chosen_font = self.config.static_font if self.config.static_font is not None else rng.choice(OK_FONTS)
+        all_fonts = pyfiglet.FigletFont.getFonts()
+        ok_fonts = list(filter(lambda x: x not in bad_fonts, all_fonts))
+        chosen_font = self.config.static_font if self.config.static_font is not None else rng.choice(ok_fonts)
         figlet_render = pyfiglet.figlet_format(render_word, font=chosen_font)
 
         return {
@@ -223,24 +202,5 @@ class FigletFontDataset(ProceduralDataset):
         return score
 
 
-class FigletFontCurriculum(BaseCurriculum):
-    def __init__(self):
-        super().__init__(FigletFontCurriculum.__name__, FigletFontConfig)
-
-        # Define attributes
-        self._define_attributes(
-            RangeAttributeDefinition(
-                name="word_len",
-                levels=[3, 5, 10, 15, 20, 30],
-                default_level=1,
-                description="The length of the word to be displayed",
-                attr_type=AttributeType.APPEND,
-                min_value=1,
-                lower_field_name="min_word_len",
-                upper_field_name="max_word_len",
-            ),
-        )
-
-
 # Register the dataset
-register_dataset("figlet_font", FigletFontDataset, FigletFontConfig, FigletFontCurriculum)
+register_dataset("figlet_font", FigletFontDataset, FigletFontConfig)

tests/test_figlet_fonts.py

@@ -1,13 +1,6 @@
 import pytest
 
-from reasoning_gym.cognition.figlet_fonts import FigletFontConfig, FigletFontCurriculum, FigletFontDataset
-
-
-def test_figlet_config_validation():
-    """Test that invalid configs raise appropriate errors"""
-    with pytest.raises(AssertionError):
-        config = FigletFontConfig(min_word_len=2, max_word_len=1)  # max_word_len < min_word_len
-        config.validate()
+from reasoning_gym.cognition.figlet_fonts import FigletFontConfig, FigletFontDataset
 
 
 def test_figlet_deterministic():
@@ -48,24 +41,3 @@ def test_static_figlet():
         assert dataset.score_answer(answer="TESTY", entry=item) == 1.0
         assert dataset.score_answer(answer="WESTY", entry=item) == 0.4
         assert dataset.score_answer(answer=None, entry=item) == 0
-
-
-def test_figlet_curriculum():
-    curriculum = FigletFontCurriculum()
-
-    base_value = {"size": 150, "seed": 1}
-
-    base_cfg: FigletFontConfig = curriculum.generate_configuration(base_value)
-    assert base_cfg.seed == 1
-    assert base_cfg.size == 150
-    assert base_cfg.min_word_len == 3 and base_cfg.max_word_len == 5
-
-    # test incrementing attribute levels
-    curriculum.increment_attr_level("word_len")
-    increased_cfg = curriculum.generate_configuration(base_value)
-    assert increased_cfg.min_word_len == 3 and increased_cfg.max_word_len == 10
-
-    # test decrementing attribute level
-    curriculum.decrement_attr_level("word_len")
-    partially_decreased_cfg = curriculum.generate_configuration(base_value)
-    assert partially_decreased_cfg.min_word_len == 3 and partially_decreased_cfg.max_word_len == 5