chore: import upstream snapshot with attribution

This commit is contained in:
wehub-resource-sync
2026-07-13 13:17:40 +08:00
commit f1825c8ceb
10096 changed files with 2364182 additions and 0 deletions
@@ -0,0 +1,131 @@
# flake8: noqa
# __example_start__
from starlette.requests import Request
from typing import Dict
import ray
from ray import serve
from ray.serve.handle import DeploymentHandle
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
@serve.deployment
class Translator:
def __init__(self):
self.language = "french"
self.prefix = "translate English to French: "
self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
def translate(self, text: str) -> str:
input_ids = self.tokenizer(
f"{self.prefix}{text}", return_tensors="pt"
).input_ids
output_ids = self.model.generate(
input_ids, num_beams=4, early_stopping=True, max_length=300
)
translation = self.tokenizer.decode(
output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return translation
def reconfigure(self, config: Dict):
self.language = config.get("language", "french")
if self.language.lower() == "french":
self.prefix = "translate English to French: "
elif self.language.lower() == "german":
self.prefix = "translate English to German: "
elif self.language.lower() == "romanian":
self.prefix = "translate English to Romanian: "
else:
pass
@serve.deployment
class Summarizer:
def __init__(self, translator: DeploymentHandle):
# Load model
self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
self.translator = translator
self.min_length = 5
self.max_length = 15
def summarize(self, text: str) -> str:
# Run inference
input_ids = self.tokenizer(f"summarize: {text}", return_tensors="pt").input_ids
output_ids = self.model.generate(
input_ids,
num_beams=4,
early_stopping=True,
min_length=self.min_length,
max_length=self.max_length,
)
# Post-process output to return only the summary text
summary = self.tokenizer.decode(
output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return summary
async def __call__(self, http_request: Request) -> str:
english_text: str = await http_request.json()
summary = self.summarize(english_text)
return await self.translator.translate.remote(summary)
def reconfigure(self, config: Dict):
self.min_length = config.get("min_length", 5)
self.max_length = config.get("max_length", 15)
app = Summarizer.bind(Translator.bind())
# __example_end__
serve.run(app)
# __start_client__
import requests
english_text = (
"It was the best of times, it was the worst of times, it was the age "
"of wisdom, it was the age of foolishness, it was the epoch of belief"
)
response = requests.post("http://127.0.0.1:8000/", json=english_text)
french_text = response.text
print(french_text)
# 'C'était le meilleur des temps, c'était le pire des temps,'
# __end_client__
assert french_text == "C'était le meilleur des temps, c'était le pire des temps,"
serve.run(
Summarizer.bind(Translator.options(user_config={"language": "german"}).bind())
)
# __start_second_client__
import requests
english_text = (
"It was the best of times, it was the worst of times, it was the age "
"of wisdom, it was the age of foolishness, it was the epoch of belief"
)
response = requests.post("http://127.0.0.1:8000/", json=english_text)
german_text = response.text
print(german_text)
# 'es war die beste Zeit, es war die schlimmste Zeit,'
# __end_second_client__
assert german_text == "es war die beste Zeit, es war die schlimmste Zeit,"
serve.shutdown()
ray.shutdown()