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import os
from PIL import Image
from io import BytesIO
import base64
from transformers import AutoTokenizer
import torch
from transformers import StoppingCriteria, PhiForCausalLM
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
class KeywordsStoppingCriteria(StoppingCriteria):
def __init__(self, keywords, tokenizer, input_ids):
self.keywords = keywords
self.keyword_ids = []
self.max_keyword_len = 0
for keyword in keywords:
cur_keyword_ids = tokenizer(keyword).input_ids
if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
cur_keyword_ids = cur_keyword_ids[1:]
if len(cur_keyword_ids) > self.max_keyword_len:
self.max_keyword_len = len(cur_keyword_ids)
self.keyword_ids.append(torch.tensor(cur_keyword_ids))
self.tokenizer = tokenizer
self.start_len = input_ids.shape[1]
def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
for keyword_id in self.keyword_ids:
if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all():
return True
outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
for keyword in self.keywords:
if keyword in outputs:
return True
return False
def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
outputs = []
for i in range(output_ids.shape[0]):
outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores))
return all(outputs)
def load_image_from_base64(image):
return Image.open(BytesIO(base64.b64decode(image)))
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