import dataclasses from enum import auto, Enum from typing import List, Tuple class SeparatorStyle(Enum): """不同的分离器样式。""" SINGLE = auto() TWO = auto() MPT = auto() PLAIN = auto() LLAMA_2 = auto() @dataclasses.dataclass class Conversation: """一个保存所有对话历史记录的类。""" 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("", "").strip() if 'mmtag' in self.version: messages[0] = (init_role, init_msg) messages.insert(0, (self.roles[0], "")) messages.insert(1, (self.roles[1], "已接收。")) else: messages[0] = (init_role, "\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"<>\n{msg}\n<>\n\n" wrap_inst = lambda msg: f"[INST] {msg} [/INST]" ret = "" for i, (role, message) in enumerate(messages): if i == 0: assert message, "第一个消息不能为 None" assert role == self.roles[0], "第一个消息必须来自于用户" 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"不正确的样式:{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"不正确的 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'用户上传的图像' msg = img_str + msg.replace('', '').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.", roles=("USER", "ASSISTANT"), version="v1", messages=(), offset=0, sep_style=SeparatorStyle.TWO, sep=" ", sep2="", ) 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="", sep2="", ) 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="", sep2="", ) 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: visual content.", 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="", ) 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: visual content.", roles=("USER", "ASSISTANT"), messages=(), offset=0, sep_style=SeparatorStyle.TWO, sep=" ", sep2="", version="v1_mmtag", ) 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, "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, "mpt": conv_mpt, } if __name__ == "__main__": print(default_conversation.get_prompt())