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| import dataclasses | |
| from enum import auto, Enum | |
| from typing import List, Tuple | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image | |
| import re | |
| def replace_last(original_string, target, replacement): | |
| last_index = original_string.rfind(target) | |
| if last_index == -1: | |
| return original_string | |
| before = original_string[:last_index] | |
| after = original_string[last_index + len(target):] | |
| return before + replacement + after | |
| def replace_sequentially_regex(original_string, target, content_list): | |
| # 创建 content_list 的迭代器 | |
| replacements_iter = iter(content_list) | |
| def get_replacement(match): | |
| try: | |
| return str(next(replacements_iter)) # 确保替换内容是字符串 | |
| except StopIteration: | |
| return match.group(0) | |
| new_string = re.sub(re.escape(target), get_replacement, original_string) | |
| return new_string | |
| def replace_ratio_tags_in_text(text): | |
| # The pattern remains the same, capturing the two numbers | |
| pattern = r"<height_(\d+)><width_(\d+)>" | |
| # Define a helper function that will be called for each match | |
| # This function receives the match object as input | |
| def _replacer_function(match, resolution_indicator=64): | |
| try: | |
| # Extract captured groups (height and width numbers as strings) | |
| h_tag_str = match.group(1) | |
| w_tag_str = match.group(2) | |
| # Convert to integers | |
| h_tag = int(h_tag_str) | |
| w_tag = int(w_tag_str) | |
| # Calculate final dimensions | |
| h = h_tag * resolution_indicator | |
| w = w_tag * resolution_indicator | |
| # Return the replacement string for this specific match | |
| return f'{h}x{w}' | |
| except (ValueError, IndexError): | |
| # In case of unexpected errors (e.g., regex issue, conversion error) | |
| # return the original matched text to avoid breaking the string | |
| # match.group(0) returns the entire substring that matched the pattern | |
| print(f"Warning: Could not process tag '{match.group(0)}'. Keeping original.") | |
| return match.group(0) | |
| # Use re.sub() to find all matches of the pattern in the text | |
| # and replace each match using the result of _replacer_function | |
| processed_text = re.sub(pattern, _replacer_function, text) | |
| return processed_text | |
| def process_think_answer_tag_for_gradio(input_text): | |
| # Replace <think></think> and <answer></answer> with collapsible sections | |
| output_text = input_text.replace( | |
| "<think>", | |
| '<div class="collapsible"><button class="collapsible-btn">Think</button><div class="content">').replace( | |
| "</think>", "</div></div>" | |
| ) | |
| output_text = output_text.replace( | |
| "<answer>", | |
| '<div class="collapsible"><button class="collapsible-btn">Answer</button><div class="content">').replace( | |
| "</answer>", "</div></div>" | |
| ) | |
| return output_text | |
| class SeparatorStyle(Enum): | |
| """Different separator style.""" | |
| SINGLE = auto() | |
| TWO = auto() | |
| MPT = auto() | |
| PLAIN = auto() | |
| LLAMA_2 = auto() | |
| GLM4 = auto() | |
| 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 role == self.roles[0]: | |
| if type(message) is tuple: | |
| message, _, _ = message | |
| ret += role + message + self.sep | |
| else: | |
| if type(message) is tuple: | |
| # process the discrete image token in the output. | |
| message, _, image_token_lists = message | |
| message = replace_sequentially_regex(message, '<image>', image_token_lists) | |
| 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 += "" | |
| elif self.sep_style == SeparatorStyle.GLM4: | |
| role = ("<|user|>", "<|assistant|>") | |
| ret = self.system + role[0] | |
| for i, (role, message) in enumerate(messages): | |
| if message: | |
| if type(message) is tuple: | |
| message, _, _ = message | |
| ret += self.sep + message + role[(i + 1) % 2] | |
| else: | |
| ret += "" | |
| else: | |
| raise ValueError(f"Invalid style: {self.sep_style}") | |
| return ret | |
| def append_message(self, role, message): | |
| if isinstance(self.messages, tuple): | |
| self.messages += ([role, message],) | |
| else: | |
| self.messages.append([role, message]) | |
| def process_image(self, image, image_process_mode, return_pil=False, image_format='PNG', max_len=1344, min_len=672): | |
| 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 max(image.size) > max_len: | |
| max_hw, min_hw = max(image.size), min(image.size) | |
| aspect_ratio = max_hw / min_hw | |
| 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): | |
| 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 | |
| image = self.process_image(image, image_process_mode, return_pil=return_pil) | |
| images.append(image) | |
| 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: | |
| msg, image, image_process_mode = msg | |
| img_b64_str = self.process_image( | |
| image, "Default", return_pil=False, | |
| image_format='JPEG') | |
| img_str = f'<img src="data:image/jpeg;base64,{img_b64_str}" alt="user upload image" />' | |
| msg = img_str + msg.replace('<image>', '').strip() | |
| msg = replace_ratio_tags_in_text(msg) | |
| ret.append([msg, None]) | |
| else: | |
| if type(msg) is tuple: | |
| msg, image, _ = msg | |
| if not isinstance(image, list): | |
| image = [image] | |
| image_str_list = [] | |
| for img_idx, img in enumerate(image): | |
| 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}" alt="generated image {img_idx}" />' | |
| image_str_list.append(img_str) | |
| msg = replace_sequentially_regex(msg, '<image>', image_str_list) | |
| if msg and 'think' in self.version: | |
| msg = process_think_answer_tag_for_gradio(msg) | |
| 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_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.", | |
| 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_instruct = Conversation( | |
| system="", | |
| roles=("USER", "ASSISTANT"), | |
| version="llama_v2", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.LLAMA_2, | |
| sep="", | |
| sep2="</s>", | |
| ) | |
| 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|>", | |
| ) | |
| conv_llama3 = Conversation( | |
| system="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""", | |
| roles=("<|start_header_id|>user<|end_header_id|>\n\n", "<|start_header_id|>assistant<|end_header_id|>\n\n"), | |
| version="llama3", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="<|eot_id|>", | |
| ) | |
| conv_llama3_without_system = Conversation( | |
| system="", | |
| roles=("<|start_header_id|>user<|end_header_id|>\n\n", "<|start_header_id|>assistant<|end_header_id|>\n\n"), | |
| version="llama3_without_system", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="<|eot_id|>", | |
| ) | |
| conv_llama3_base = Conversation( | |
| system="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""", | |
| roles=("<|start_header_id|>user<|end_header_id|>\n\n", "<|start_header_id|>assistant<|end_header_id|>\n\n"), | |
| version="llama3_base", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="<|eot_id|>", | |
| ) | |
| conv_llama3_expand = Conversation( | |
| system="""[BOS]SYSTEM\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""", | |
| roles=("[BOS]USER:\n", "[BOS]ASSISTANT:\n"), | |
| version="llama3_expand", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="[EOT]", | |
| ) | |
| conv_llama3_expandv2 = Conversation( | |
| system="""[BOS]SYSTEM\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""", | |
| roles=("[BOS]USER:\n", "[BOS]ASSISTANT:\n"), | |
| version="llama3_expand", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="[unused0]", # use different eos token. | |
| ) | |
| conv_llama3_expandV2 = Conversation( | |
| system="""[BOS]SYSTEM\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""", | |
| roles=("[BOS]USER:\n", "[BOS]ASSISTANT:\n"), | |
| version="llama3_expand", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="[unused0]", | |
| ) | |
| conv_qwen2 = Conversation( | |
| system='<|im_start|>system\nYou are a helpful assistant.', | |
| roles=("<|im_start|>user\n", "<|im_start|>assistant\n"), | |
| version="qwen2", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="<|im_end|>\n", | |
| ) | |
| conv_qwen2_think = Conversation( | |
| system='<|im_start|>system\nYou are a helpful assistant.' | |
| "You will first thinks about the reasoning process in the mind and then provides the user with the answer. " | |
| "The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., " | |
| "<think> reasoning process here </think><answer> answer here </answer>", | |
| roles=("<|im_start|>user\n", "<|im_start|>assistant\n"), | |
| version="qwen2_think", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="<|im_end|>\n", | |
| ) | |
| qwen2_image_gen_with_think = Conversation( | |
| system='<|im_start|>system\nYou are a helpful assistant.' | |
| "You will first thinks about the reasoning process in the mind and then provides the user with the answer. " | |
| "The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., " | |
| "<think> reasoning process here </think><answer> answer here </answer>. " | |
| "If an image needs to be generated inside <think>, generate it at resolution <height_4><width_4>. " | |
| "In <answer>, generate any requested images at the resolution specified by the user.", | |
| roles=("<|im_start|>user\n", "<|im_start|>assistant\n"), | |
| version="qwen2_think", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.MPT, | |
| sep="<|im_end|>\n", | |
| ) | |
| conv_glm4 = Conversation( | |
| system='[gMASK]<sop>', | |
| roles=("<|user|>\n", "<|assistant|>"), | |
| version="glm4", | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.GLM4, | |
| sep="\n", | |
| ) | |
| default_conversation = conv_vicuna_v1 | |
| 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, | |
| "chatml_direct": conv_chatml_direct, | |
| "mistral_direct": conv_chatml_direct, | |
| "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, | |
| "llama3": conv_llama3, | |
| "llama3_without_system": conv_llama3_without_system, | |
| "llama3_expand": conv_llama3_expand, | |
| "llama3_expand_v2": conv_llama3_expandv2, | |
| "llama3_base": conv_llama3_base, | |
| "mpt": conv_mpt, | |
| "qwen2": conv_qwen2, | |
| "qwen2_think": conv_qwen2_think, | |
| "qwen2_image_gen_with_think": conv_qwen2_think, | |
| "glm4": conv_glm4, | |
| } | |
| if __name__ == "__main__": | |
| print(default_conversation.get_prompt()) | |