File size: 2,936 Bytes
38bc0dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | import sys
class DLCodeGenerator:
_instance = None
_initialized = False
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, model_name: str = "Salesforce/codet5-base", enable_dl: bool = True):
if self._initialized:
return
self.model_name = model_name
self.enable_dl = enable_dl
self.tokenizer = None
self.model = None
self.is_mock = not enable_dl
self._initialized = True
def _ensure_loaded(self) -> bool:
"""Lazily load the CodeT5 model and tokenizer only when generate is first called."""
if not self.enable_dl:
return False
if self.model is not None and self.tokenizer is not None:
return True
print(f"Lazy loading DL model: {self.model_name}...")
try:
from transformers import RobertaTokenizer, AutoModelForSeq2SeqLM, AutoTokenizer
try:
self.tokenizer = RobertaTokenizer.from_pretrained(
self.model_name, extra_ids=0, additional_special_tokens=[], local_files_only=False
)
except Exception:
self.tokenizer = AutoTokenizer.from_pretrained(
self.model_name, local_files_only=False
)
self.model = AutoModelForSeq2SeqLM.from_pretrained(
self.model_name, local_files_only=False
)
self.is_mock = False
print("DL Model loaded successfully.")
return True
except Exception as e:
print(
f"Note: DL Model '{self.model_name}' skipped ({e}). Using ultra-fast AST rule generator."
)
self.is_mock = True
return False
def generate(
self,
code: str,
prompt: str,
temperature: float = 0.7,
max_length: int = 512,
) -> str:
if not self._ensure_loaded():
return code
try:
input_text = f"{prompt}:\n{code}"
input_ids = self.tokenizer(
input_text,
return_tensors="pt",
truncation=True,
max_length=max_length,
).input_ids
outputs = self.model.generate(
input_ids,
max_new_tokens=max_length,
temperature=temperature,
do_sample=True if temperature > 0 else False,
top_p=0.95,
num_return_sequences=1,
)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
except Exception as e:
print(f"DL Generation error: {e}. Falling back to source code.")
return code
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