app_module/utils.py

# -*- coding:utf-8 -*-
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
import logging
import json
import os
import datetime
import hashlib
import csv
import requests
import re
import html
import markdown2
import torch 
import sys
import gc
from pygments.lexers import guess_lexer, ClassNotFound

import gradio as gr
from pypinyin import lazy_pinyin
import tiktoken
import mdtex2html
from markdown import markdown
from pygments import highlight
from pygments.lexers import guess_lexer,get_lexer_by_name
from pygments.formatters import HtmlFormatter
import transformers
from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer

from app_modules.presets import *

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
)


def markdown_to_html_with_syntax_highlight(md_str):
    def replacer(match):
        lang = match.group(1) or "text"
        code = match.group(2)
        lang = lang.strip()
        #print(1,lang)
        if lang=="text":
            lexer = guess_lexer(code)
            lang = lexer.name
            #print(2,lang)
        try:
            lexer = get_lexer_by_name(lang, stripall=True)
        except ValueError:
            lexer = get_lexer_by_name("python", stripall=True)
        formatter = HtmlFormatter()
        #print(3,lexer.name)
        highlighted_code = highlight(code, lexer, formatter)

        return f'<pre><code class="{lang}">{highlighted_code}</code></pre>'

    code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```"
    md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE)

    html_str = markdown(md_str)
    return html_str


def normalize_markdown(md_text: str) -> str:
    lines = md_text.split("\n")
    normalized_lines = []
    inside_list = False

    for i, line in enumerate(lines):
        if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()):
            if not inside_list and i > 0 and lines[i - 1].strip() != "":
                normalized_lines.append("")
            inside_list = True
            normalized_lines.append(line)
        elif inside_list and line.strip() == "":
            if i < len(lines) - 1 and not re.match(
                r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip()
            ):
                normalized_lines.append(line)
            continue
        else:
            inside_list = False
            normalized_lines.append(line)

    return "\n".join(normalized_lines)


def convert_mdtext(md_text):
    code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL)
    inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL)
    code_blocks = code_block_pattern.findall(md_text)
    non_code_parts = code_block_pattern.split(md_text)[::2]

    result = []
    for non_code, code in zip(non_code_parts, code_blocks + [""]):
        if non_code.strip():
            non_code = normalize_markdown(non_code)
            if inline_code_pattern.search(non_code):
                result.append(markdown(non_code, extensions=["tables"]))
            else:
                result.append(mdtex2html.convert(non_code, extensions=["tables"]))
        if code.strip():
            code = f"\n```{code}\n\n```"
            code = markdown_to_html_with_syntax_highlight(code)
            result.append(code)
    result = "".join(result)
    result += ALREADY_CONVERTED_MARK
    return result

def convert_asis(userinput):
    return f"<p style=\"white-space:pre-wrap;\">{html.escape(userinput)}</p>"+ALREADY_CONVERTED_MARK

def detect_converted_mark(userinput):
    if userinput.endswith(ALREADY_CONVERTED_MARK):
        return True
    else:
        return False



def detect_language(code):
    if code.startswith("\n"):
        first_line = ""
    else:
        first_line = code.strip().split("\n", 1)[0]
    language = first_line.lower() if first_line else ""
    code_without_language = code[len(first_line) :].lstrip() if first_line else code
    return language, code_without_language

def convert_to_markdown(text):
    text = text.replace("$","&#36;")
    def replace_leading_tabs_and_spaces(line):
        new_line = []
        
        for char in line:
            if char == "\t":
                new_line.append("&#9;")
            elif char == " ":
                new_line.append("&nbsp;")
            else:
                break
        return "".join(new_line) + line[len(new_line):]

    markdown_text = ""
    lines = text.split("\n")
    in_code_block = False

    for line in lines:
        if in_code_block is False and line.startswith("```"):
            in_code_block = True
            markdown_text += f"{line}\n"
        elif in_code_block is True and line.startswith("```"):
            in_code_block = False
            markdown_text += f"{line}\n"
        elif in_code_block:
            markdown_text += f"{line}\n"
        else:
            line = replace_leading_tabs_and_spaces(line)
            line = re.sub(r"^(#)", r"\\\1", line)
            markdown_text += f"{line}  \n"

    return markdown_text

def add_language_tag(text):
    def detect_language(code_block):
        try:
            lexer = guess_lexer(code_block)
            return lexer.name.lower()
        except ClassNotFound:
            return ""

    code_block_pattern = re.compile(r"(```)(\w*\n[^`]+```)", re.MULTILINE)

    def replacement(match):
        code_block = match.group(2)
        if match.group(2).startswith("\n"):
            language = detect_language(code_block)
            if language:
                return f"```{language}{code_block}```"
            else:
                return f"```\n{code_block}```"
        else:
            return match.group(1) + code_block + "```"

    text2 = code_block_pattern.sub(replacement, text)
    return text2

def delete_last_conversation(chatbot, history):
    if len(chatbot) > 0:
        chatbot.pop()

    if len(history) > 0:
        history.pop()
        
    return (
        chatbot,
        history,
        "Delete Done",
    )

def reset_state():
    return [], [], "Reset Done"

def reset_textbox():
    return gr.update(value=""),""

def cancel_outputing():
    return "Stop Done"

def transfer_input(inputs):
    # 一次性返回，降低延迟
    textbox = reset_textbox()
    return (
        inputs,
        gr.update(value=""),
        gr.Button.update(visible=True),
    )


class State:
    interrupted = False

    def interrupt(self):
        self.interrupted = True

    def recover(self):
        self.interrupted = False
shared_state = State()





# Greedy Search
def greedy_search(input_ids: torch.Tensor,
                  model: torch.nn.Module,
                  tokenizer: transformers.PreTrainedTokenizer,
                  stop_words: list,
                  max_length: int,
                  temperature: float = 1.0,
                  top_p: float = 1.0,
                  top_k: int = 25) -> Iterator[str]:
    generated_tokens = []
    past_key_values = None
    current_length = 1
    for i in range(max_length):
        with torch.no_grad():
            if past_key_values is None:
                outputs = model(input_ids)
            else:
                outputs = model(input_ids[:, -1:], past_key_values=past_key_values)
            logits = outputs.logits[:, -1, :]
            past_key_values = outputs.past_key_values

            # apply temperature
            logits /= temperature
    
            probs = torch.softmax(logits, dim=-1)
            # apply top_p
            probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
            probs_sum = torch.cumsum(probs_sort, dim=-1)
            mask = probs_sum - probs_sort > top_p
            probs_sort[mask] = 0.0
    
            # apply top_k
            #if top_k is not None:
            #    probs_sort1, _ = torch.topk(probs_sort, top_k)
            #    min_top_probs_sort = torch.min(probs_sort1, dim=-1, keepdim=True).values
            #    probs_sort = torch.where(probs_sort < min_top_probs_sort, torch.full_like(probs_sort, float(0.0)), probs_sort)
    
            probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
            next_token = torch.multinomial(probs_sort, num_samples=1)
            next_token = torch.gather(probs_idx, -1, next_token)
    
            input_ids = torch.cat((input_ids, next_token), dim=-1)
    
            generated_tokens.append(next_token[0].item())
            text = tokenizer.decode(generated_tokens)
    
            yield text
            if any([x in text for x in stop_words]):
                del past_key_values
                del logits
                del probs
                del probs_sort
                del probs_idx
                del probs_sum
                gc.collect()
                return 

def generate_prompt_with_history(text,history,tokenizer,max_length=2048):
    prompt = "The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n[|Human|]Hello!\n[|AI|]Hi!"   
    history = ["\n[|Human|]{}\n[|AI|]{}".format(x[0],x[1]) for x in history]
    history.append("\n[|Human|]{}\n[|AI|]".format(text))
    history_text = ""
    flag = False
    for x in history[::-1]:
        if tokenizer(prompt+history_text+x, return_tensors="pt")['input_ids'].size(-1) <= max_length:
            history_text = x + history_text
            flag = True
        else:
            break
    if flag:
        return  prompt+history_text,tokenizer(prompt+history_text, return_tensors="pt")
    else:
        return None


def is_stop_word_or_prefix(s: str, stop_words: list) -> bool:
    for stop_word in stop_words:
        if s.endswith(stop_word):
            return True
        for i in range(1, len(stop_word)):
            if s.endswith(stop_word[:i]):
                return True
    return False



def load_tokenizer_and_model(base_model,adapter_model=None,load_8bit=False):
    if torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"

    try:
        if torch.backends.mps.is_available():
            device = "mps"
    except:  # noqa: E722
        pass
    tokenizer = LlamaTokenizer.from_pretrained(base_model)
    if device == "cuda":
        model = LlamaForCausalLM.from_pretrained(
            base_model,
            load_in_8bit=load_8bit,
            torch_dtype=torch.float16,
            device_map="auto",
        )
        if adapter_model is not None:
            model = PeftModel.from_pretrained(
                model,
                adapter_model,
                torch_dtype=torch.float16,
            )
    elif device == "mps":
        model = LlamaForCausalLM.from_pretrained(
            base_model,
            device_map={"": device},
            torch_dtype=torch.float16,
        )
        if adapter_model is not None:
            model = PeftModel.from_pretrained(
                model,
                adapter_model,
                device_map={"": device},
                torch_dtype=torch.float16,
            )
    else:
        model = LlamaForCausalLM.from_pretrained(
            base_model, device_map={"": device}, low_cpu_mem_usage=True
        )
        if adapter_model is not None:
            model = PeftModel.from_pretrained(
                model,
                adapter_model,
                device_map={"": device},
            )

    if not load_8bit:
        model.half()  # seems to fix bugs for some users.

    model.eval()
    return tokenizer,model,device