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import dataclasses
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from enum import auto, Enum
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from typing import List, Any, Dict, Union, Tuple
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import re
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import base64
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from io import BytesIO
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from PIL import Image
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from transformers import AutoTokenizer
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class SeparatorStyle(Enum):
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"""Different separator style."""
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SINGLE = auto()
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TWO = auto()
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MPT = auto()
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PLAIN = auto()
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CHATML = auto()
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LLAMA_2 = auto()
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LLAMA_3 = auto()
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QWEN = auto()
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GEMMA = auto()
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@dataclasses.dataclass
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class Conversation:
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"""A class that keeps all conversation history."""
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system: str
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roles: List[str]
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messages: List[List[str]]
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offset: int
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sep_style: SeparatorStyle = SeparatorStyle.SINGLE
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sep: str = "###"
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sep2: str = None
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version: str = "Unknown"
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tokenizer_id: str = ""
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tokenizer: Any = None
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stop_str: Union[str, List[str]] = None
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stop_token_ids: List[int] = None
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skip_next: bool = False
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def get_prompt(self):
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messages = self.messages
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if len(messages) > 0 and type(messages[0][1]) is tuple:
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messages = self.messages.copy()
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init_role, init_msg = messages[0].copy()
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init_msg = init_msg[0]
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if "mmtag" in self.version:
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init_msg = init_msg.replace("<image>", "").strip()
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messages[0] = (init_role, init_msg)
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messages.insert(0, (self.roles[0], "<Image><image></Image>"))
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messages.insert(1, (self.roles[1], "Received."))
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elif not init_msg.startswith("<image>"):
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init_msg = init_msg.replace("<image>", "").strip()
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messages[0] = (init_role, "<image>\n" + init_msg)
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else:
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messages[0] = (init_role, init_msg)
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if self.sep_style == SeparatorStyle.SINGLE:
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ret = self.system + self.sep
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for role, message in messages:
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if message:
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if type(message) is tuple:
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message, _, _ = message
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ret += role + ": " + message + self.sep
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else:
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ret += role + ":"
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elif self.sep_style == SeparatorStyle.TWO:
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seps = [self.sep, self.sep2]
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ret = self.system + seps[0]
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for i, (role, message) in enumerate(messages):
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if message:
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if type(message) is tuple:
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message, _, _ = message
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ret += role + ": " + message + seps[i % 2]
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else:
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ret += role + ":"
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elif self.sep_style == SeparatorStyle.CHATML:
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ret = "" if self.system == "" else self.system + self.sep + "\n"
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for role, message in messages:
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if message:
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if type(message) is tuple:
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message, images, _ = message
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message = "<image>" * len(images) + message
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ret += role + "\n" + message + self.sep + "\n"
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else:
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ret += role + "\n"
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return ret
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elif self.sep_style == SeparatorStyle.LLAMA_3:
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chat_template_messages = [{"role": "system", "content": self.system}]
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for role, message in messages:
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if message:
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if type(message) is tuple:
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message, images = message
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message = "<image>" * len(images) + message
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chat_template_messages.append({"role": role, "content": message})
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return self.tokenizer.apply_chat_template(chat_template_messages, tokenize=False, add_generation_prompt=True)
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elif self.sep_style == SeparatorStyle.MPT:
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ret = self.system + self.sep
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for role, message in messages:
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if message:
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if type(message) is tuple:
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message, _, _ = message
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ret += role + message + self.sep
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else:
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ret += role
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elif self.sep_style == SeparatorStyle.GEMMA:
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ret = ""
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for i, (role, message) in enumerate(messages):
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assert role == self.roles[i % 2], "Conversation should alternate user/assistant/user/assistant/..."
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if message:
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if type(message) is tuple:
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message, _, _ = message
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ret += role + message + self.sep
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else:
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ret += role
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elif self.sep_style == SeparatorStyle.LLAMA_2:
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wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n" if len(msg) > 0 else msg
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wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
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ret = ""
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for i, (role, message) in enumerate(messages):
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if i == 0:
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assert message, "first message should not be none"
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assert role == self.roles[0], "first message should come from user"
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if message:
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if type(message) is tuple:
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message, _, _ = message
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if i == 0:
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message = wrap_sys(self.system) + message
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if i % 2 == 0:
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message = wrap_inst(message)
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ret += self.sep + message
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else:
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ret += " " + message + " " + self.sep2
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else:
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ret += ""
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ret = ret.lstrip(self.sep)
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elif self.sep_style == SeparatorStyle.PLAIN:
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seps = [self.sep, self.sep2]
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ret = self.system
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for i, (role, message) in enumerate(messages):
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if message:
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if type(message) is tuple:
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message, _, _ = message
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ret += message + seps[i % 2]
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else:
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ret += ""
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else:
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raise ValueError(f"Invalid style: {self.sep_style}")
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return ret
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def append_message(self, role, message):
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self.messages.append([role, message])
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def process_image(self, image, image_process_mode, return_pil=False, image_format="PNG"):
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if image_process_mode == "Pad":
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def expand2square(pil_img, background_color=(122, 116, 104)):
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width, height = pil_img.size
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if width == height:
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return pil_img
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elif width > height:
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result = Image.new(pil_img.mode, (width, width), background_color)
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result.paste(pil_img, (0, (width - height) // 2))
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return result
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else:
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result = Image.new(pil_img.mode, (height, height), background_color)
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result.paste(pil_img, ((height - width) // 2, 0))
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return result
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image = expand2square(image)
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elif image_process_mode in ["Default", "Crop"]:
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pass
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elif image_process_mode == "Resize":
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image = image.resize((336, 336))
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else:
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raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
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if type(image) is not Image.Image:
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image = Image.open(image).convert("RGB")
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max_hw, min_hw = max(image.size), min(image.size)
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aspect_ratio = max_hw / min_hw
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max_len, min_len = 672, 448
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shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
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longest_edge = int(shortest_edge * aspect_ratio)
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W, H = image.size
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if H > W:
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H, W = longest_edge, shortest_edge
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else:
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H, W = shortest_edge, longest_edge
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image = image.resize((W, H))
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if return_pil:
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return image
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else:
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buffered = BytesIO()
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image.save(buffered, format=image_format)
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img_b64_str = base64.b64encode(buffered.getvalue()).decode()
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return img_b64_str
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def get_images(self, return_pil=False, return_path=False):
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images = []
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for i, (role, msg) in enumerate(self.messages[self.offset :]):
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if i % 2 == 0:
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if type(msg) is tuple:
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msg, image, image_process_mode = msg
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if type(image) != list:
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image = [image]
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for img in image:
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if not return_path and self.is_image_file(img):
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img = self.process_image(img, image_process_mode, return_pil=return_pil)
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else:
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images.append(img)
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return images
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def is_image_file(self, filename):
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image_extensions = [".png", ".jpg", ".jpeg", ".gif", ".bmp", ".tiff", ".webp"]
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return any(filename.lower().endswith(ext) for ext in image_extensions)
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def is_video_file(self, filename):
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video_extensions = [".mp4", ".mov", ".avi", ".mkv", ".wmv", ".flv", ".mpeg", ".mpg"]
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return any(filename.lower().endswith(ext) for ext in video_extensions)
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def to_gradio_chatbot(self):
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ret = []
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for i, (role, msg) in enumerate(self.messages[self.offset :]):
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if i % 2 == 0:
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if type(msg) is tuple:
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msg, image, image_process_mode = msg
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if type(image) != list:
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image = [image]
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if len(image) == 1:
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msg = "<image>\n" + msg.replace("<image>", "").strip()
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else:
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msg = re.sub(r"(<image>)\n(?=<image>)", r"\1 ", msg)
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img_str_list = []
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for img in image:
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if self.is_image_file(img):
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img_b64_str = self.process_image(img, "Default", return_pil=False, image_format="JPEG")
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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;"/>'
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img_str_list.append(img_str)
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elif self.is_video_file(img):
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ret.append(((img,), None))
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msg = msg.strip()
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img_place_holder = ""
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for img_str in img_str_list:
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img_place_holder += f"{img_str}\n\n"
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if len(img_str_list) > 0:
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msg = f"{img_place_holder}\n\n{msg}"
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if len(msg) > 0:
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ret.append([msg, None])
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else:
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ret.append([msg, None])
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else:
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ret[-1][-1] = msg
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return ret
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def copy(self):
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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)
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def dict(self):
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if len(self.get_images()) > 0:
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return {
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"system": self.system,
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"roles": self.roles,
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"messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
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"offset": self.offset,
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"sep": self.sep,
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"sep2": self.sep2,
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}
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return {
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"system": self.system,
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"roles": self.roles,
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"messages": self.messages,
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"offset": self.offset,
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"sep": self.sep,
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"sep2": self.sep2,
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}
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conv_vicuna_v0 = Conversation(
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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.",
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roles=("Human", "Assistant"),
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messages=[
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["Human", "What are the key differences between renewable and non-renewable energy sources?"],
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[
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"Assistant",
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"Renewable energy sources are those that can be replenished naturally in a relatively "
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"short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
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"Non-renewable energy sources, on the other hand, are finite and will eventually be "
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"depleted, such as coal, oil, and natural gas. Here are some key differences between "
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"renewable and non-renewable energy sources:\n"
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"1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
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"energy sources are finite and will eventually run out.\n"
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"2. Environmental impact: Renewable energy sources have a much lower environmental impact "
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"than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
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"and other negative effects.\n"
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"3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
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"have lower operational costs than non-renewable sources.\n"
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"4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
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"locations than non-renewable sources.\n"
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"5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
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"situations and needs, while non-renewable sources are more rigid and inflexible.\n"
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"6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
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"non-renewable sources are not, and their depletion can lead to economic and social instability.\n",
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],
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],
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offset=2,
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sep_style=SeparatorStyle.SINGLE,
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sep="###",
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)
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conv_vicuna_v1 = Conversation(
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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.",
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roles=("USER", "ASSISTANT"),
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version="v1",
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.TWO,
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sep=" ",
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sep2="</s>",
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)
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conv_llama_2 = Conversation(
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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.
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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.""",
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roles=("USER", "ASSISTANT"),
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version="llama_v2",
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.LLAMA_2,
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sep="<s>",
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sep2="</s>",
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)
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conv_llava_llama_2 = Conversation(
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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.",
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roles=("USER", "ASSISTANT"),
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version="llama_v2",
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.LLAMA_2,
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sep="<s>",
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sep2="</s>",
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)
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conv_llava_llama_3 = Conversation(
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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.",
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roles=("user", "assistant"),
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version="llama_v3",
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messages=[],
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offset=0,
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sep="<|eot_id|>",
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sep_style=SeparatorStyle.LLAMA_3,
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tokenizer_id="meta-llama/Meta-Llama-3-8B-Instruct",
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tokenizer=AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct"),
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stop_token_ids=[128009],
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)
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conv_mistral_instruct = Conversation(
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system="",
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roles=("USER", "ASSISTANT"),
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version="llama_v2",
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.LLAMA_2,
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sep="",
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sep2="</s>",
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)
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conv_llava_llama_2_simple = Conversation(
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system="Answer the questions about the visual content that the user provides.",
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roles=("USER", "ASSISTANT"),
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version="llama_v2",
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.LLAMA_2,
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sep="<s>",
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sep2="</s>",
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)
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conv_llava_llama_2_mmtag = Conversation(
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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>.",
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roles=("USER", "ASSISTANT"),
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version="llama_v2_mmtag",
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.LLAMA_2,
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sep="<s>",
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sep2="</s>",
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)
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conv_mpt = Conversation(
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system="""<|im_start|>system
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A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
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roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
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version="mpt",
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.MPT,
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sep="<|im_end|>",
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)
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conv_qwen = Conversation(
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system="""<|im_start|>system
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You are a helpful assistant.""",
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roles=("<|im_start|>user", "<|im_start|>assistant"),
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version="qwen",
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.CHATML,
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sep="<|im_end|>",
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)
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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")
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conv_llava_plain = Conversation(
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system="",
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roles=("", ""),
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.PLAIN,
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sep="\n",
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)
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conv_llava_v0 = Conversation(
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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.",
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roles=("Human", "Assistant"),
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messages=[],
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offset=0,
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sep_style=SeparatorStyle.SINGLE,
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sep="###",
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)
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conv_llava_v0_mmtag = Conversation(
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system="A chat between a curious user and an artificial intelligence assistant. "
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"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>.",
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roles=("Human", "Assistant"),
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messages=[],
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offset=0,
|
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sep_style=SeparatorStyle.SINGLE,
|
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sep="###",
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version="v0_mmtag",
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)
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|
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conv_llava_v1 = Conversation(
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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.",
|
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roles=("USER", "ASSISTANT"),
|
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version="v1",
|
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messages=[],
|
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offset=0,
|
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sep_style=SeparatorStyle.TWO,
|
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sep=" ",
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sep2="</s>",
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)
|
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|
|
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,
|
|
"gemma_instruct": conv_gemma_instruct,
|
|
}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print(default_conversation.get_prompt())
|
|
|
