from dora import DoraStatus import pylcs import os import pyarrow as pa from transformers import AutoModelForCausalLM, AutoTokenizer import torch import re import time CHATGPT = False MODEL_NAME_OR_PATH = "TheBloke/deepseek-coder-6.7B-instruct-GPTQ" CODE_MODIFIER_TEMPLATE = """ ### Instruction Respond with one block of modified code only in ```python block. No explaination. ```python {code} ``` {user_message} ### Response: """ model = AutoModelForCausalLM.from_pretrained( MODEL_NAME_OR_PATH, device_map="auto", trust_remote_code=True, revision="main", max_length=1024, ).to("cuda:0") tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True) def extract_python_code_blocks(text): """ Extracts Python code blocks from the given text that are enclosed in triple backticks with a python language identifier. Parameters: - text: A string that may contain one or more Python code blocks. Returns: - A list of strings, where each string is a block of Python code extracted from the text. """ pattern = r"```python\n(.*?)\n```" matches = re.findall(pattern, text, re.DOTALL) if len(matches) == 0: pattern = r"```python\n(.*?)(?:\n```|$)" matches = re.findall(pattern, text, re.DOTALL) if len(matches) == 0: return [text] else: matches = [remove_last_line(matches[0])] return matches def remove_last_line(python_code): """ Removes the last line from a given string of Python code. Parameters: - python_code: A string representing Python source code. Returns: - A string with the last line removed. """ lines = python_code.split("\n") # Split the string into lines if lines: # Check if there are any lines to remove lines.pop() # Remove the last line return "\n".join(lines) # Join the remaining lines back into a string def calculate_similarity(source, target): """ Calculate a similarity score between the source and target strings. This uses the edit distance relative to the length of the strings. """ edit_distance = pylcs.edit_distance(source, target) max_length = max(len(source), len(target)) # Normalize the score by the maximum possible edit distance (the length of the longer string) similarity = 1 - (edit_distance / max_length) return similarity def find_best_match_location(source_code, target_block): """ Find the best match for the target_block within the source_code by searching line by line, considering blocks of varying lengths. """ source_lines = source_code.split("\n") target_lines = target_block.split("\n") best_similarity = 0 best_start_index = 0 best_end_index = -1 # Iterate over the source lines to find the best matching range for all lines in target_block for start_index in range(len(source_lines) - len(target_lines) + 1): for end_index in range(start_index + len(target_lines), len(source_lines) + 1): current_window = "\n".join(source_lines[start_index:end_index]) current_similarity = calculate_similarity(current_window, target_block) if current_similarity > best_similarity: best_similarity = current_similarity best_start_index = start_index best_end_index = end_index # Convert line indices back to character indices for replacement char_start_index = len("\n".join(source_lines[:best_start_index])) + ( 1 if best_start_index > 0 else 0 ) char_end_index = len("\n".join(source_lines[:best_end_index])) return char_start_index, char_end_index def replace_code_in_source(source_code, replacement_block: str): """ Replace the best matching block in the source_code with the replacement_block, considering variable block lengths. """ replacement_block = extract_python_code_blocks(replacement_block)[0] start_index, end_index = find_best_match_location(source_code, replacement_block) if start_index != -1 and end_index != -1: # Replace the best matching part with the replacement block new_source = ( source_code[:start_index] + replacement_block + source_code[end_index:] ) return new_source else: return source_code class Operator: def on_event( self, dora_event, send_output, ) -> DoraStatus: global model, tokenizer if dora_event["type"] == "INPUT" and dora_event["id"] == "text": input = dora_event["value"][0].as_py() # Path to the current file current_file_path = __file__ # Directory of the current file current_directory = os.path.dirname(current_file_path) path = current_directory + "/policy.py" with open(path, "r", encoding="utf8") as f: code = f.read() user_message = input start_llm = time.time() output = self.ask_llm( CODE_MODIFIER_TEMPLATE.format(code=code, user_message=user_message) ) source_code = replace_code_in_source(code, output) print("response time:", time.time() - start_llm, flush=True) print("response: ", output, flush=True) with open(path, "w") as file: file.write(source_code) del model del tokenizer # model will still be on cache until its place is taken by other objects so also execute the below lines import gc # garbage collect library gc.collect() torch.cuda.empty_cache() time.sleep(9) send_output("init", pa.array([])) ## Stopping to liberate GPU space return DoraStatus.STOP return DoraStatus.CONTINUE def ask_llm(self, prompt): # Generate output # prompt = PROMPT_TEMPLATE.format(system_message=system_message, prompt=prompt)) input = tokenizer(prompt, return_tensors="pt") input_ids = input.input_ids.cuda() # add attention mask here attention_mask = input.attention_mask.cuda() output = model.generate( inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512, attention_mask=attention_mask, eos_token_id=tokenizer.eos_token_id, ) # Get the tokens from the output, decode them, print them # Get text between im_start and im_end return tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt) :] if __name__ == "__main__": op = Operator() # Path to the current file current_file_path = __file__ # Directory of the current file current_directory = os.path.dirname(current_file_path) path = current_directory + "/policy.py" with open(path, "r", encoding="utf8") as f: raw = f.read() op.on_event( { "type": "INPUT", "id": "text", "value": pa.array( [ { "path": path, "user_message": "go to the living room, ask the model if there is people, if there is, say i'm going to go get coffee for you, then go to the kitchen, when you reach the kitchen, check with the model if there is a person and say can i have a coffee please, then wait 10 sec and go back to the living room", }, ] ), "metadata": [], }, print, )