fix: add guards against ZeroDivisionError across cloud-edge LLM metrics and backends

[Fahmid-Arman]

What type of PR is this?
/kind bug

What this PR does / why we need it:
The cloud-edge-collaborative-inference-for-llm benchmark previously crashed with a ZeroDivisionError when executed against an empty dataset or when a model generated $\le1$ token (e.g., immediate EOS).

This PR implements comprehensive, zero-safe division guards across the 5 metric modules and 4 backend wrappers to ensure the framework returns 0.0 gracefully under these edge cases, preserving the execution of the broader benchmark suite.

Files patched:
Metrics:

• testenv/accuracy.py
• testenv/throughput.py
• testenv/internal_token_latency.py
• testenv/time_to_first_token.py
• testenv/edge_ratio.py

Backends:

• models/huggingface_llm.py
• models/vllm_llm.py
• models/eagle_llm.py
• models/api_llm.py (Identified and patched proactively)

Which issue(s) this PR fixes:
Fixes #337

[kubeedge-bot]

[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by: Fahmid-Arman
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[gemini-code-assist]

Code Review

This pull request improves the robustness of LLM inference and evaluation scripts by adding checks to prevent division-by-zero errors when calculating token latency, throughput, and other metrics. The feedback recommends adhering to PEP 8 guidelines by using implicit boolean evaluation for empty sequences instead of checking their length with len() > 0.

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[Fahmid-Arman]

Feedback addressed:

• Refactored empty sequence checks to use PEP 8 compliant implicit boolean evaluation (if internal_token_latency:) instead of len() > 0.

/assign @MooreZheng