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import dataclasses
from enum import auto, Enum
from typing import List, Tuple
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
MPT = auto()
PLAIN = auto()
LLAMA_2 = 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"
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].replace("<image>", "").strip()
if 'mmtag' in self.version:
messages[0] = (init_role, init_msg)
messages.insert(0, (self.roles[0], "<Image><image></Image>"))
messages.insert(1, (self.roles[1], "Received."))
else:
messages[0] = (init_role, "<image>\n" + 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.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.LLAMA_2:
wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n"
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 get_images(self, return_pil=False):
images = []
for i, (role, msg) in enumerate(self.messages[self.offset:]):
if i % 2 == 0:
if type(msg) is tuple:
import base64
from io import BytesIO
from PIL import Image
msg, image, image_process_mode = msg
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}")
max_hw, min_hw = max(image.size), min(image.size)
aspect_ratio = max_hw / min_hw
max_len, min_len = 800, 400
shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
longest_edge = int(shortest_edge * aspect_ratio)
W, H = image.size
if longest_edge != max(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:
images.append(image)
else:
buffered = BytesIO()
image.save(buffered, format="PNG")
img_b64_str = base64.b64encode(buffered.getvalue()).decode()
images.append(img_b64_str)
return images
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:
import base64
from io import BytesIO
msg, image, image_process_mode = msg
max_hw, min_hw = max(image.size), min(image.size)
aspect_ratio = max_hw / min_hw
max_len, min_len = 800, 400
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))
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_b64_str = base64.b64encode(buffered.getvalue()).decode()
img_str = f'<img src="data:image/png;base64,{img_b64_str}" alt="user upload image" />'
msg = img_str + msg.replace('<image>', '').strip()
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.",
system="A chat between a curious user and an artificial intelligence assistant acting as an experienced radiologist. "
"The assistant gives professional, 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_llava_med = Conversation(
system="A chat between a curious user and an artificial intelligence assistant acting as an experienced radiologist. "
"The assistant gives professional, detailed, and polite answers to the user's questions.",
roles=("USER", "ASSISTANT"),
version="v1",
messages=[],
offset=2,
sep_style=SeparatorStyle.TWO,
sep="###",
sep2="</s>"
)
simple_conv_multimodal = Conversation(
system="You are LLaVA-Med, a large language and vision assistant trained by a group of researchers at Microsoft, based on the general domain LLaVA architecture."
"You are able to understand the visual content that the user provides, and assist the user with a variety of medical and clinical tasks using natural language."
"Follow the instructions carefully and explain your answers in detail.",
roles=("Human", "Assistant"),
messages=(
("Human", "Hi!"),
("Assistant", "Hi there! How can I help you today?\n")
),
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
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_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_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.",
system="A chat between a curious user and an artificial intelligence assistant acting as an experienced radiologist. "
"The assistant gives professional, 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_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",
)
default_conversation = conv_vicuna_v1
conv_templates = {
"default": conv_vicuna_v0,
"v0": conv_vicuna_v0,
"v1": conv_vicuna_v1,
"llava_med": conv_llava_med,
"vicuna_v1": conv_vicuna_v1,
"llama_2": conv_llama_2,
"plain": conv_llava_plain,
"v0_plain": conv_llava_plain,
"llava_v0": conv_llava_v0,
"v0_mmtag": conv_llava_v0_mmtag,
"llava_v1": conv_llava_v1,
"v1_mmtag": conv_llava_v1_mmtag,
"llava_llama_2": conv_llava_llama_2,
"multimodal": simple_conv_multimodal,
"mpt": conv_mpt,
}
if __name__ == "__main__":
print(default_conversation.get_prompt())
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