import dataclasses
from enum import auto, Enum
from typing import List, Any, Dict, Union, Tuple
import re
import base64
from io import BytesIO
from PIL import Image
from transformers import AutoTokenizer
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
MPT = auto()
PLAIN = auto()
CHATML = auto()
LLAMA_2 = auto()
LLAMA_3 = auto()
QWEN = auto()
GEMMA = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: List[str]
messages: List[List[str]]
offset: int
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "###"
sep2: str = None
version: str = "Unknown"
tokenizer_id: str = ""
tokenizer: Any = None
# Stop criteria (the default one is EOS token)
stop_str: Union[str, List[str]] = None
# Stops generation if meeting any token in this list
stop_token_ids: List[int] = None
skip_next: bool = False
def get_prompt(self):
messages = self.messages
if len(messages) > 0 and type(messages[0][1]) is tuple:
messages = self.messages.copy()
init_role, init_msg = messages[0].copy()
init_msg = init_msg[0]
if "mmtag" in self.version:
init_msg = init_msg.replace("<image>", "").strip()
messages[0] = (init_role, init_msg)
messages.insert(0, (self.roles[0], "<Image><image></Image>"))
messages.insert(1, (self.roles[1], "Received."))
elif not init_msg.startswith("<image>"):
init_msg = init_msg.replace("<image>", "").strip()
messages[0] = (init_role, "<image>\n" + init_msg)
else:
messages[0] = (init_role, init_msg)
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system + self.sep
for role, message in messages:
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + ": " + message + self.sep
else:
ret += role + ":"
elif self.sep_style == SeparatorStyle.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
for i, (role, message) in enumerate(messages):
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
elif self.sep_style == SeparatorStyle.CHATML:
ret = "" if self.system == "" else self.system + self.sep + "\n"
for role, message in messages:
if message:
if type(message) is tuple:
message, images, _ = message
message = "<image>" * len(images) + message
ret += role + "\n" + message + self.sep + "\n"
else:
ret += role + "\n"
return ret
elif self.sep_style == SeparatorStyle.LLAMA_3:
chat_template_messages = [{"role": "system", "content": self.system}]
for role, message in messages:
if message:
if type(message) is tuple:
message, images = message
message = "<image>" * len(images) + message
chat_template_messages.append({"role": role, "content": message})
# print(chat_template_messages)
return self.tokenizer.apply_chat_template(chat_template_messages, tokenize=False, add_generation_prompt=True)
# ret = "" if self.system == "" else self.system + self.sep + "\n"
# for role, message in messages:
# if message:
# if type(message) is tuple:
# message, images = message
# message = "<image>" * len(images) + message
# ret += role + "\n" + message + self.sep + "\n"
# else:
# ret += role + "\n"
# return ret
elif self.sep_style == SeparatorStyle.MPT:
ret = self.system + self.sep
for role, message in messages:
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + message + self.sep
else:
ret += role
elif self.sep_style == SeparatorStyle.GEMMA:
ret = ""
for i, (role, message) in enumerate(messages):
assert role == self.roles[i % 2], "Conversation should alternate user/assistant/user/assistant/..."
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + message + self.sep
else:
ret += role
elif self.sep_style == SeparatorStyle.LLAMA_2:
wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n" if len(msg) > 0 else msg
wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
ret = ""
for i, (role, message) in enumerate(messages):
if i == 0:
assert message, "first message should not be none"
assert role == self.roles[0], "first message should come from user"
if message:
if type(message) is tuple:
message, _, _ = message
if i == 0:
message = wrap_sys(self.system) + message
if i % 2 == 0:
message = wrap_inst(message)
ret += self.sep + message
else:
ret += " " + message + " " + self.sep2
else:
ret += ""
ret = ret.lstrip(self.sep)
elif self.sep_style == SeparatorStyle.PLAIN:
seps = [self.sep, self.sep2]
ret = self.system
for i, (role, message) in enumerate(messages):
if message:
if type(message) is tuple:
message, _, _ = message
ret += message + seps[i % 2]
else:
ret += ""
else:
raise ValueError(f"Invalid style: {self.sep_style}")
return ret
def append_message(self, role, message):
self.messages.append([role, message])
def process_image(self, image, image_process_mode, return_pil=False, image_format="PNG"):
if image_process_mode == "Pad":
def expand2square(pil_img, background_color=(122, 116, 104)):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
image = expand2square(image)
elif image_process_mode in ["Default", "Crop"]:
pass
elif image_process_mode == "Resize":
image = image.resize((336, 336))
else:
raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
if type(image) is not Image.Image:
image = Image.open(image).convert("RGB")
max_hw, min_hw = max(image.size), min(image.size)
aspect_ratio = max_hw / min_hw
max_len, min_len = 672, 448
shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
longest_edge = int(shortest_edge * aspect_ratio)
W, H = image.size
if H > W:
H, W = longest_edge, shortest_edge
else:
H, W = shortest_edge, longest_edge
image = image.resize((W, H))
if return_pil:
return image
else:
buffered = BytesIO()
image.save(buffered, format=image_format)
img_b64_str = base64.b64encode(buffered.getvalue()).decode()
return img_b64_str
def get_images(self, return_pil=False, return_path=False):
images = []
for i, (role, msg) in enumerate(self.messages[self.offset :]):
if i % 2 == 0:
if type(msg) is tuple:
msg, image, image_process_mode = msg
if type(image) != list:
image = [image]
for img in image:
if not return_path and self.is_image_file(img):
img = self.process_image(img, image_process_mode, return_pil=return_pil)
else:
images.append(img)
return images
def is_image_file(self, filename):
image_extensions = [".png", ".jpg", ".jpeg", ".gif", ".bmp", ".tiff", ".webp"]
return any(filename.lower().endswith(ext) for ext in image_extensions)
def is_video_file(self, filename):
video_extensions = [".mp4", ".mov", ".avi", ".mkv", ".wmv", ".flv", ".mpeg", ".mpg"]
return any(filename.lower().endswith(ext) for ext in video_extensions)
def to_gradio_chatbot(self):
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset :]):
if i % 2 == 0:
if type(msg) is tuple:
msg, image, image_process_mode = msg
if type(image) != list:
image = [image]
if len(image) == 1:
msg = "<image>\n" + msg.replace("<image>", "").strip()
else:
msg = re.sub(r"(<image>)\n(?=<image>)", r"\1 ", msg)
img_str_list = []
for img in image:
if self.is_image_file(img):
img_b64_str = self.process_image(img, "Default", return_pil=False, image_format="JPEG")
img_str = f'<img src="data:image/jpeg;base64,{img_b64_str}" style="max-width: 256px; max-height: 256px; width: auto; height: auto; object-fit: contain;"/>'
img_str_list.append(img_str)
elif self.is_video_file(img):
ret.append(((img,), None))
msg = msg.strip()
img_place_holder = ""
for img_str in img_str_list:
img_place_holder += f"{img_str}\n\n"
if len(img_str_list) > 0:
msg = f"{img_place_holder}\n\n{msg}"
if len(msg) > 0:
ret.append([msg, None])
else:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def copy(self):
return Conversation(system=self.system, 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, version=self.version)
def dict(self):
if len(self.get_images()) > 0:
return {
"system": self.system,
"roles": self.roles,
"messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
}
return {
"system": self.system,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
}
conv_vicuna_v0 = Conversation(
system="A chat between a curious human and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the human's questions.",
roles=("Human", "Assistant"),
messages=[
["Human", "What are the key differences between renewable and non-renewable energy sources?"],
[
"Assistant",
"Renewable energy sources are those that can be replenished naturally in a relatively "
"short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
"Non-renewable energy sources, on the other hand, are finite and will eventually be "
"depleted, such as coal, oil, and natural gas. Here are some key differences between "
"renewable and non-renewable energy sources:\n"
"1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
"energy sources are finite and will eventually run out.\n"
"2. Environmental impact: Renewable energy sources have a much lower environmental impact "
"than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
"and other negative effects.\n"
"3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
"have lower operational costs than non-renewable sources.\n"
"4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
"locations than non-renewable sources.\n"
"5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
"situations and needs, while non-renewable sources are more rigid and inflexible.\n"
"6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
"non-renewable sources are not, and their depletion can lead to economic and social instability.\n",
],
],
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
conv_vicuna_v1 = Conversation(
system="A chat between a curious user and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the user's questions.",
roles=("USER", "ASSISTANT"),
version="v1",
messages=[],
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
conv_llama_2 = Conversation(
system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""",
roles=("USER", "ASSISTANT"),
version="llama_v2",
messages=[],
offset=0,
sep_style=SeparatorStyle.LLAMA_2,
sep="<s>",
sep2="</s>",
)
conv_llava_llama_2 = Conversation(
system="You are a helpful language and vision assistant. " "You are able to understand the visual content that the user provides, " "and assist the user with a variety of tasks using natural language.",
roles=("USER", "ASSISTANT"),
version="llama_v2",
messages=[],
offset=0,
sep_style=SeparatorStyle.LLAMA_2,
sep="<s>",
sep2="</s>",
)
# conv_llava_llama_3 = Conversation(
# system="You are a helpful language and vision assistant. " "You are able to understand the visual content that the user provides, " "and assist the user with a variety of tasks using natural language.",
# roles=("user", "assistant"),
# version="llama_v3",
# messages=[],
# offset=0,
# sep="<|eot_id|>",
# sep_style=SeparatorStyle.LLAMA_3,
# tokenizer_id="meta-llama/Meta-Llama-3-8B-Instruct",
# tokenizer=AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct"),
# stop_token_ids=[128009],
# )
conv_mistral_instruct = Conversation(
system="",
roles=("USER", "ASSISTANT"),
version="llama_v2",
messages=[],
offset=0,
sep_style=SeparatorStyle.LLAMA_2,
sep="",
sep2="</s>",
)
conv_llava_llama_2_simple = Conversation(
system="Answer the questions about the visual content that the user provides.",
roles=("USER", "ASSISTANT"),
version="llama_v2",
messages=[],
offset=0,
sep_style=SeparatorStyle.LLAMA_2,
sep="<s>",
sep2="</s>",
)
conv_llava_llama_2_mmtag = Conversation(
system="Answer the questions about the visual content that the user provides." "The visual content will be provided with the following format: <Image>visual content</Image>.",
roles=("USER", "ASSISTANT"),
version="llama_v2_mmtag",
messages=[],
offset=0,
sep_style=SeparatorStyle.LLAMA_2,
sep="<s>",
sep2="</s>",
)
conv_mpt = Conversation(
system="""<|im_start|>system
A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
version="mpt",
messages=[],
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
conv_qwen = Conversation(
system="""<|im_start|>system
You are a helpful assistant.""",
roles=("<|im_start|>user", "<|im_start|>assistant"),
version="qwen",
messages=[],
offset=0,
sep_style=SeparatorStyle.CHATML,
sep="<|im_end|>",
)
conv_internlm_2 = Conversation(
system="""<|im_start|>system
You are a helpful assistant.""",
roles=("<|im_start|>user", "<|im_start|>assistant"),
version="internlm_2",
messages=[],
offset=0,
sep_style=SeparatorStyle.CHATML,
sep="<|im_end|>",
)
conv_gemma_instruct = Conversation(system="", roles=("<start_of_turn>user\n", "<start_of_turn>model\n"), version="gemma", messages=[], offset=0, sep_style=SeparatorStyle.GEMMA, sep="<end_of_turn>\n")
conv_llava_plain = Conversation(
system="",
roles=("", ""),
messages=[],
offset=0,
sep_style=SeparatorStyle.PLAIN,
sep="\n",
)
conv_llava_v0 = Conversation(
system="A chat between a curious human and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the human's questions.",
roles=("Human", "Assistant"),
messages=[],
offset=0,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
conv_llava_v0_mmtag = Conversation(
system="A chat between a curious user and an artificial intelligence assistant. "
"The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
"The visual content will be provided with the following format: <Image>visual content</Image>.",
roles=("Human", "Assistant"),
messages=[],
offset=0,
sep_style=SeparatorStyle.SINGLE,
sep="###",
version="v0_mmtag",
)
conv_llava_v1 = Conversation(
system="A chat between a curious human and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the human's questions.",
roles=("USER", "ASSISTANT"),
version="v1",
messages=[],
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
conv_llava_v1_mmtag = Conversation(
system="A chat between a curious user and an artificial intelligence assistant. "
"The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
"The visual content will be provided with the following format: <Image>visual content</Image>.",
roles=("USER", "ASSISTANT"),
messages=[],
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
version="v1_mmtag",
)
conv_mistral_orca = Conversation(
system="""<|im_start|>system
You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!""",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
version="mpt",
messages=[],
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
conv_mistral_zephyr = Conversation(
system="""<|system|>
You are a helpful AI assistant.""",
roles=("<|user|>\n", "<|assistant|>\n"),
version="mpt",
messages=[],
offset=0,
sep_style=SeparatorStyle.MPT,
sep="</s>",
)
conv_mistral_direct = Conversation(
system="""<|im_start|>system
Answer the questions.""",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
version="mpt",
messages=[],
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
conv_chatml_direct = Conversation(
system="""<|im_start|>system
Answer the questions.""",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
version="mpt",
messages=[],
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
default_conversation = conv_vicuna_v0
conv_templates = {
"default": conv_vicuna_v0,
"v0": conv_vicuna_v0,
"v1": conv_vicuna_v1,
"vicuna_v1": conv_vicuna_v1,
"llama_2": conv_llama_2,
"mistral_instruct": conv_mistral_instruct,
"mistral_orca": conv_mistral_orca,
"mistral_zephyr": conv_mistral_zephyr,
"mistral_direct": conv_mistral_direct,
"plain": conv_llava_plain,
"v0_plain": conv_llava_plain,
"chatml_direct": conv_chatml_direct,
"llava_v0": conv_llava_v0,
"llava_v0_mmtag": conv_llava_v0_mmtag,
"llava_v1": conv_llava_v1,
"llava_v1_mmtag": conv_llava_v1_mmtag,
"llava_llama_2": conv_llava_llama_2,
# "llava_llama_3": conv_llava_llama_3,
"llava_llama_2_simple": conv_llava_llama_2_simple,
"llava_llama_2_mmtag": conv_llava_llama_2_mmtag,
"llava_mistral_instruct": conv_mistral_instruct,
"mpt": conv_mpt,
"qwen_1_5": conv_qwen,
"qwen_2": conv_qwen,
"internlm_2": conv_internlm_2,
"gemma_instruct": conv_gemma_instruct,
}
if __name__ == "__main__":
print(default_conversation.get_prompt())
print(default_conversation)