class_docstr: This synthetic scenario is intended for conducting efficiency-oriented
  benchmarking. In particular, we seek to address the following questions:
  
  1. What is the dependence of runtime on number of tokens in the prompt and
     number of generated output tokens? How about number of completions?
  2. How much variance do we observe for each query?
  3. How do different models (across providers) behave?
  4. Can we reverse engineer the hardware used by providers?
  
  We gather input text from fixed public domain sources and vary various parameters,
  including the model the number of input and output tokens, the number of
  input instances, the number of output completions.
  
  The dataset is stored at https://worksheets.codalab.org/bundles/0x17a361bc066b4b0e87d968069759d361.
code: def get_instances(self) -> List[Instance]:
      instances: List[Instance] = []
      assert self.tokenizer in [
          "huggingface/gpt2",
          "ai21/j1",
          "cohere/cohere",
          "meta/opt",
          "yandex/yalm",
          "bigscience/bloom",
          "bigscience/t0pp",
          "google/t5",
          "google/ul2",
          "tsinghua/glm",
          "eleutherai/gptj",
          "eleutherai/gptneox",
      ]
      for instance_id in range(self.num_instances):
          file_name: str = (
              f"num_prompt_tokens={self.num_prompt_tokens},"
              f"tokenizer={self.tokenizer.replace('/', '_')},"
              f"id={instance_id}.txt"
          )
          data_path: str = os.path.join(self.output_path, file_name)
          ensure_file_downloaded(
              source_url=f"https://worksheets.codalab.org/rest/bundles/0x17a361bc066b4b0e87d968069759d361/"
              f"contents/blob/{file_name}",
              target_path=data_path,
              unpack=False,
          )
  
          with open(data_path, "r") as f:
              prompt: str = f.read()
              instance = Instance(
                  input=Input(text=prompt),
                  references=[
                      Reference(Output(text=""), tags=[CORRECT_TAG])
                  ],
                  split=TEST_SPLIT,
              )
              instances.append(instance)
  
      return instances
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/synthetic_efficiency_scenario.py
imports: import os
  from typing import List
  from helm.common.general import ensure_file_downloaded
  from .scenario import Scenario, Instance, Reference, TEST_SPLIT, CORRECT_TAG, Input, Output
label_desc: Use image node_id 2 for calling the SyntheticEfficiencyScenario obj's underlying member method code with example usage: obj.get_instances() and returns: instances