[Benchmark Backfill] Integrate FSC-147 into lmms-eval

[Luodian]

Summary

• Add a new fsc147 benchmark task with YAML wiring and task utilities under lmms_eval/tasks/fsc147/.
• Implement FSC-147 prompt construction and counting metrics (fsc147_exact_match, fsc147_mae) using the FSC147-derived Hugging Face dataset yifehuang97/CoCount-train-fsc147.
• Register the benchmark in docs by adding FSC-147 to docs/current_tasks.md.

Validation

• uv run python -m lmms_eval --tasks list (confirmed fsc147 appears in available tasks)
• uv run python -m lmms_eval --model dummy_video_reader --model_args response=0,fail_on_missing=false --tasks fsc147 --limit 8 --batch_size 1 --verbosity INFO (run succeeded; metrics emitted for fsc147_exact_match and fsc147_mae)
• lsp_diagnostics clean for lmms_eval/tasks/fsc147/utils.py

Linked Issue

• Closes [Benchmark Backfill] Integrate FSC-147 into lmms-eval #1141
• Linear: LMM-292

Smoke Validation (limit=8)

Status: PASS (LMM-292 / fsc147)

Output Table

Metric	Value
fsc147_exact_match	0.25
fsc147_mae	1.875

Sample Output

Sample 1 (doc_id: 0)

• Input: How many peppers are there in the image? ↵ Answer with only an integer.
• Model Output: 12
• Reference: 13
• Scores: fsc147_exact_match = 0.0 · fsc147_mae = 1.0
• Tokens: output=1316, reasoning=1314

Sample 2 (doc_id: 1)

• Input: How many bread rolls are there in the image? ↵ Answer with only an integer.
• Model Output: 7
• Reference: 8
• Scores: fsc147_exact_match = 0.0 · fsc147_mae = 1.0
• Tokens: output=678, reasoning=677

Test Params

uv run python -m lmms_eval --model openai_compatible --model_args "model_version=bytedance-seed/seed-1.6-flash" --tasks fsc147 --batch_size 1 --limit 8.0 --log_samples