A script to run Gemini models (#345)

Adding a script to run Gemini models with a text prompt.
Steps:
```shell
python -m venv .venv
pip install -r requirements.txt
gcloud auth login && gcloud auth application-default login && gcloud auth application-default set-quota-project oss-fuzz
python -m experimental.manual.prompter -p prompt.txt -l vertex_ai_gemini-1-5
ls `./responses/`
```

See the [`name`
attribute](https://github.com/google/oss-fuzz-gen/blob/main/llm_toolkit/models.py#L422)
for more supported model names.

experimental/manual/prompter.py

@@ -0,0 +1,71 @@
+"""Play with Gemini models manually
+Usage:
+  # Under venv.
+  python -m experimental.manual.prompter -p <prompt_file> -l <model_name>
+  # <prompt_file> is a plain text file.
+  # <model_name> is `name` attribute of classes in llm_toolkit/models.py.
+  # E.g.,
+  python -m experimental.manual.prompter -p prompt.txt -l vertex_ai_gemini-1-5
+"""
+
+import argparse
+import os
+
+from llm_toolkit import models, prompts
+
+NUM_SAMPLES: int = 1
+TEMPERATURE: float = 0.4
+MAX_TOKENS: int = 4096
+
+
+def parse_args() -> argparse.Namespace:
+  """Parses command line arguments."""
+  parser = argparse.ArgumentParser(
+      description='Run all experiments that evaluates all target functions.')
+  parser.add_argument('-n',
+                      '--num-samples',
+                      type=int,
+                      default=NUM_SAMPLES,
+                      help='The number of samples to request from LLM.')
+  parser.add_argument(
+      '-t',
+      '--temperature',
+      type=float,
+      default=TEMPERATURE,
+      help=('A value between 0 and 2 representing the variety of the targets '
+            'generated by LLM.'))
+  parser.add_argument('-l',
+                      '--model',
+                      default=models.DefaultModel.name,
+                      help=('Models available: '
+                            f'{", ".join(models.LLM.all_llm_names())}'))
+  parser.add_argument('-p', '--prompt', help='Prompt file for LLM.')
+  parser.add_argument('-r',
+                      '--response-dir',
+                      default='./responses',
+                      help='LLM response directory.')
+  return parser.parse_args()
+
+
+def setup_model() -> models.LLM:
+  return models.LLM.setup(
+      ai_binary='',
+      name=args.model,
+      max_tokens=MAX_TOKENS,
+      num_samples=args.num_samples,
+      temperature=args.temperature,
+  )
+
+
+def construct_prompt() -> prompts.Prompt:
+  with open(args.prompt, 'r') as prompt_file:
+    content = prompt_file.read()
+  return model.prompt_type()(initial=content)
+
+
+if __name__ == "__main__":
+  args = parse_args()
+  model = setup_model()
+  prompt = construct_prompt()
+  os.makedirs(args.response_dir, exist_ok=True)
+  model.generate_code(prompt, response_dir='responses')