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import argparse
import time
from PIL import Image
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
from transformers import StoppingCriteria, StoppingCriteriaList
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
THREE = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: List[str]
messages: List[List[str]]
offset: int
# system_img: List[Image.Image] = []
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "###"
sep2: str = None
skip_next: bool = False
conv_id: Any = None
def get_prompt(self):
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system + self.sep
for role, message in self.messages:
if message:
ret += role + ": " + message + self.sep
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.THREE:
ret = self.system
for i, (role, message) in enumerate(self.messages):
if message:
if type(message) == list:
message = message[0]
ret += role + ": " + message
else:
ret += role + ":"
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
def append_message(self, role, message):
self.messages.append([role, message])
def to_gradio_chatbot(self):
print('to_gradio_chatbot')
print(self.messages)
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset:]):
if i % 2 == 0:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def copy(self):
return Conversation(
system=self.system,
# system_img=self.system_img,
roles=self.roles,
messages=[[x, y] for x, y in self.messages],
offset=self.offset,
sep_style=self.sep_style,
sep=self.sep,
sep2=self.sep2,
conv_id=self.conv_id)
def dict(self):
return {
"system": self.system,
# "system_img": self.system_img,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
"conv_id": self.conv_id,
}
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = stops
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
for stop in self.stops:
if torch.all((stop == input_ids[0][-len(stop):])).item():
return True
return False
CONV_VISION = Conversation(
system="A chat between human who asks question and you give helpful, detailed, and insightful answers to his question.",
roles=(" Question", " Answer"),
messages=[],
offset=2,
sep_style=SeparatorStyle.THREE,
sep="###",
)
CONV_DIRECT= Conversation(
system="",
roles=("", ""),
messages=[],
offset=2,
sep_style=SeparatorStyle.THREE,
sep="###",
)
class Chat:
def __init__(self, model, vis_processor, device='cuda:0'):
self.device = device
self.model = model
self.vis_processor = vis_processor
def ask(self, text, conv):
#conv.messages = [] #hack not keeping history.
conv.append_message(conv.roles[0], text)
def answer(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,
repetition_penalty=1.0, length_penalty=1, temperature=1.0, max_length=2000):
conv.append_message(conv.roles[1], None)
question = conv.get_prompt()
image = img_list[0] #torch.stack(img_list).to(self.device)
output_text = self.model.generate({"image": image, "prompt": question}, num_beams=num_beams, temperature=temperature)
conv.messages[-1][1] = output_text
return output_text, ''
def upload_img(self, image, conv, img_list):
if isinstance(image, str): # is a image path
raw_image = Image.open(image).convert('RGB')
image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
elif isinstance(image, Image.Image):
raw_image = image
raw_image = raw_image.convert('RGB')
image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
elif isinstance(image, torch.Tensor):
if len(image.shape) == 3:
image = image.unsqueeze(0)
image = image.to(self.device)
#image_emb, _ = self.model.encode_img(image)
img_list.append(image)
#conv.append_message(conv.roles[0], "")
msg = "Received."
# self.conv.append_message(self.conv.roles[1], msg)
return msg
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