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import json
import os
import requests
from transformers import GPT2LMHeadModel, GPT2Tokenizer, pipeline, set_seed
from .modeling_gpt2 import GPT2LMHeadModel as GROVERLMHeadModel
from .preprocess import ArabertPreprocessor
# Taken and Modified from https://huggingface.co/spaces/flax-community/chef-transformer/blob/main/app.py
class TextGeneration:
def __init__(self):
self.debug = False
self.generation_pipline = {}
self.preprocessor = ArabertPreprocessor(model_name="aragpt2-mega")
self.tokenizer = GPT2Tokenizer.from_pretrained(
"aubmindlab/aragpt2-mega", use_fast=False
)
self.tokenizer.pad_token = self.tokenizer.eos_token
self.API_KEY = os.getenv("API_KEY")
self.headers = {"Authorization": f"Bearer {self.API_KEY}"}
# self.model_names_or_paths = {
# "aragpt2-medium": "D:/ML/Models/aragpt2-medium",
# "aragpt2-base": "D:/ML/Models/aragpt2-base",
# }
self.model_names_or_paths = {
"aragpt2-medium": "aubmindlab/aragpt2-medium",
"aragpt2-base": "aubmindlab/aragpt2-base",
"aragpt2-large": "aubmindlab/aragpt2-large",
"aragpt2-mega": "aubmindlab/aragpt2-mega",
}
set_seed(42)
def load_pipeline(self):
for model_name, model_path in self.model_names_or_paths.items():
if "base" in model_name or "medium" in model_name:
self.generation_pipline[model_name] = pipeline(
"text-generation",
model=GPT2LMHeadModel.from_pretrained(model_path),
tokenizer=self.tokenizer,
device=-1,
)
else:
self.generation_pipline[model_name] = pipeline(
"text-generation",
model=GROVERLMHeadModel.from_pretrained(model_path),
tokenizer=self.tokenizer,
device=-1,
)
def load(self):
if not self.debug:
self.load_pipeline()
def generate(
self,
model_name,
prompt,
max_new_tokens: int,
temperature: float,
top_k: int,
top_p: float,
repetition_penalty: float,
no_repeat_ngram_size: int,
do_sample: bool,
num_beams: int,
):
prompt = self.preprocessor.preprocess(prompt)
return_full_text = False
return_text = True
num_return_sequences = 1
pad_token_id = 0
eos_token_id = 0
input_tok = self.tokenizer.tokenize(prompt)
max_length = len(input_tok) + max_new_tokens
if max_length > 1024:
max_length = 1024
if not self.debug:
generated_text = self.generation_pipline[model_name.lower()](
prompt,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
return_full_text=return_full_text,
return_text=return_text,
do_sample=do_sample,
num_beams=num_beams,
num_return_sequences=num_return_sequences,
)[0]["generated_text"]
else:
generated_text = self.generate_by_query(
prompt,
model_name,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
return_full_text=return_full_text,
return_text=return_text,
do_sample=do_sample,
num_beams=num_beams,
num_return_sequences=num_return_sequences,
)
# print(generated_text)
if isinstance(generated_text, dict):
if "error" in generated_text:
if "is currently loading" in generated_text["error"]:
return f"Model is currently loading, estimated time is {generated_text['estimated_time']}"
return generated_text["error"]
else:
return "Something happened 🤷♂️!!"
else:
generated_text = generated_text[0]["generated_text"]
return self.preprocessor.unpreprocess(generated_text)
def query(self, payload, model_name):
data = json.dumps(payload)
url = (
"https://api-inference.huggingface.co/models/aubmindlab/"
+ model_name.lower()
)
response = requests.request("POST", url, headers=self.headers, data=data)
return json.loads(response.content.decode("utf-8"))
def generate_by_query(
self,
prompt: str,
model_name: str,
max_length: int,
temperature: float,
top_k: int,
top_p: float,
repetition_penalty: float,
no_repeat_ngram_size: int,
pad_token_id: int,
eos_token_id: int,
return_full_text: int,
return_text: int,
do_sample: bool,
num_beams: int,
num_return_sequences: int,
):
payload = {
"inputs": prompt,
"parameters": {
"max_length ": max_length,
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"repetition_penalty": repetition_penalty,
"no_repeat_ngram_size": no_repeat_ngram_size,
"pad_token_id": pad_token_id,
"eos_token_id": eos_token_id,
"return_full_text": return_full_text,
"return_text": return_text,
"pad_token_id": pad_token_id,
"do_sample": do_sample,
"num_beams": num_beams,
"num_return_sequences": num_return_sequences,
},
"options": {
"use_cache": True,
},
}
return self.query(payload, model_name)
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