Revert "Restructure {reasoning_gym, tests}/{core, exercises, curricula}"

This reverts commit 10dbb374b0.

reasoning_gym/algorithmic/letter_counting.py

@@ -0,0 +1,66 @@
+"""Letter counting task generator"""
+
+import re
+from dataclasses import dataclass
+from random import Random
+from typing import List, Optional
+
+from reasoning_gym.data import read_data_file
+
+from ..factory import ProceduralDataset, register_dataset
+
+
+@dataclass
+class LetterCountingConfig:
+    """Configuration for letter counting task generation"""
+
+    min_words: int = 5  # Minimum words in span
+    max_words: int = 15  # Maximum words in span
+    seed: Optional[int] = None
+    size: int = 500  # Virtual dataset size
+
+    def validate(self) -> None:
+        """Validate configuration parameters"""
+        assert self.min_words > 0, "min_words must be positive"
+        assert self.max_words >= self.min_words, "max_words must be >= min_words"
+
+
+class LetterCountingDataset(ProceduralDataset):
+    """Generates letter counting tasks from text spans"""
+
+    def __init__(self, config: LetterCountingConfig):
+        super().__init__(config=config, seed=config.seed, size=config.size)
+
+        # Load and preprocess text
+        text = read_data_file("in_the_year_2889.txt")
+        # Extract words and clean them to contain only alphanumeric characters
+        self.words = [word for word in re.findall(r"\b\w+\b", text) if word.isalnum()]
+
+    def __getitem__(self, idx: int) -> dict:
+        """Generate a single letter counting task"""
+        rng = Random(self.seed + idx)
+
+        # Select random span of words
+        span_length = rng.randint(self.config.min_words, self.config.max_words)
+        start_idx = rng.randint(0, len(self.words) - span_length)
+        span = self.words[start_idx : start_idx + span_length]
+
+        # Get all unique letters from span
+        letters = set("".join(span).lower())
+        if not letters:
+            letters = {"a"}  # Fallback if span has no letters
+
+        # Select random letter that appears in the span
+        target_letter = rng.choice(sorted(letters))
+
+        # Count occurrences
+        count = sum(word.lower().count(target_letter) for word in span)
+
+        return {
+            "question": f'How many times does the letter "{target_letter}" appear in the text: "{" ".join(span)}"?',
+            "answer": str(count),
+            "metadata": {"span_length": span_length, "target_letter": target_letter, "span": span},
+        }
+
+
+register_dataset("letter_counting", LetterCountingDataset, LetterCountingConfig)