The file `feal.py` (in the working directory) implements a FEAL-like encryption
function. Implement a chosen-plaintext attack that recovers the value of
`key[5]`.

Your attack must be implemented in a file `attack.py` in the working directory,
and must define a function `attack(encrypt_fn)` that returns the uint32 value of
`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as
`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer
ciphertext, using secret round keys you cannot read directly). Your attack should
run in less than 30 seconds.

Each of the 6 round keys is derived from a 16-bit seed. You still can't brute
force the entire key space, but this should make it easier to perform a
differential attack that recovers the round key.


In this task you will implement an **LLM inference batching scheduler
(shape-aware)** for a static-graph LLM inference system.

**Background**

When running large language models on hardware accelerators (such as TPUs, RDUs,
or bespoke inference chips) the compiled execution graph has to operate on
fixed-sized tensors. Requests arrive with arbitrary prompt lengths and
generation lengths, but at runtime everything must be rounded up to a multiple of
a common granularity. A naive strategy of batching each request independently
wastes compute, increases the number of cold compilations, and leads to poor
end-to-end latency.

**Goal**

Read the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`
and `task_file/input_data/requests_bucket_2.jsonl` and produce plans in
`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`
(all paths relative to the working directory) that assign every request to a
batch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,
`shape.hidden_align`). Each input entry is a JSON object with:

  * `request_id`: a unique string identifier (e.g. `"r-000010"`)
  * `prompt_len`: the input prompt length in tokens (integer)
  * `gen_len`: the number of tokens to generate (integer)

Pack these into batches so that:

  * All input requests are included exactly once (no missing/duplicate request_ids).
  * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where
    seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.
  * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both
    buckets (MAX_SHAPES=8).
  * One record per request_id, identical shapes within each batch_id.

To aid development you are given (in the working directory):

  * `task_file/scripts/cost_model.py` – an analytical cost and latency model.
  * `task_file/scripts/baseline_packer.py` – a slow baseline to compare against.

**Cost model**

`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform
your packing strategy. During evaluation, a copy of this cost model is used to
measure your solution's performance.

  * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).
  * Decode cost/latency depend on the batch decode bound `G_max` and `S`.
  * There is a per-batch overhead cost/latency term.
  * There is a per-shape compilation/bring-up cost over the set of unique shapes.
  * `pad_ratio` is padded tokens divided by real tokens.

**Thresholds** (your plans must be strictly below all of these):

| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |
|-------|------|-----------|------------------|--------------------------|
| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |
| requests_bucket_2.jsonl | 4.8e10 | 0.15  | 2.1e5 | 3.2e7 |

**Deliverables**

Generate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and
`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the
constraints and thresholds above, and you must keep the `input_data` files
unchanged. Example output line:

```
{"request_id": "r-000010", "batch_id": "b-0002", "shape": {"seq_align": 192, "heads_align": 32, "hidden_align": 4096}}
```


Write a file `eval.scm` that is a metacircular evaluator capable of interpreting
the scheme-like language implemented by `interp.py` (in the working directory).

The `eval.scm` file should read exactly one line from STDIN: this will be a file
path to a new `.scm` file that should be interpreted. Any remaining input should
be redirected to the interpreted program, and any output from that program
should be sent to STDOUT.

Your interpreter should be able to interpret (1) each of the test programs in
`test/`, and (2) itself.

For example, the following three commands should all do the same thing:

```
echo '(+ 7 8)' | python3 interp.py test/calculator.scm
echo -e 'test/calculator.scm\n(+ 7 8)' | python3 interp.py eval.scm
echo -e 'eval.scm\ntest/calculator.scm\n(+ 7 8)' | python3 interp.py eval.scm
```

- The first runs `test/calculator.scm` directly under `interp.py`, which reads
  `(+ 7 8)` and adds 7 and 8.
- The second runs `eval.scm` under `interp.py`; `eval.scm` takes
  `test/calculator.scm` as the file to run and runs it, which then computes 7+8.
- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;
  the outer `eval.scm` interprets the inner `eval.scm`, which then runs the
  calculator as above.

For every test program, running it through `eval.scm` (and, for the
self-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the
same output as running it directly through `interp.py`.


container produced no result sentinel (exit 137)


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