fix: guard division-by-zero and uninitialized state in metrics/masking code

[mooreneural]

• mlm_memmap.py: normalize codon weights only when mean > 0 to prevent NaN propagating into np.random.binomial when all token weights are zero
• mlm_memmap.py: clamp conditional random-replace probability to [0, 1] and guard against mask_replace_prob == 1.0 causing ZeroDivisionError
• dead_latents.py: initialize _last_avg_nonzero in init so get_stats() is safe to call before the first update()
• evo2_dataset.py: remove duplicate ASCII 45 ('-') entry in VALID_DNA_AND_DEGENERATE

Description

Four defensive bug fixes across the codon MLM preprocessing pipeline and SAE evaluation utilities.

Usage

mlm_memmap.py — codon weight normalization: Previously, passing a codon_weights array where all weights for the sequence tokens were zero caused division-by-zero → NaN → crash inside np.random.binomial. No API change; the normalization is simply skipped when the mean is zero and masking proceeds with the flat mlm_probability.

mlm_memmap.py — random replacement probability: Calling process_item with mask_replace_prob=1.0 (or any combo where random_replace_prob / (1 - mask_replace_prob) > 1.0) raised ZeroDivisionError or ValueError. Now guarded with a zero-denominator check and clamped to [0, 1].

dead_latents.py — pre-update get_stats(): DeadLatentTracker().get_stats() now works immediately after construction without needing a prior update() call.

tracker = DeadLatentTracker(hidden_dim=4096)
stats = tracker.get_stats()  # previously AttributeError, now returns 0.0 avg_nonzero

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