mirror of
https://github.com/qodo-ai/pr-agent.git
synced 2025-07-02 11:50:37 +08:00
570 lines
30 KiB
Python
570 lines
30 KiB
Python
import asyncio
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import copy
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import textwrap
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from functools import partial
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from typing import Dict, List
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from jinja2 import Environment, StrictUndefined
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from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler
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from pr_agent.algo.ai_handlers.litellm_ai_handler import LiteLLMAIHandler
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from pr_agent.algo.pr_processing import get_pr_diff, get_pr_multi_diffs, retry_with_fallback_models
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from pr_agent.algo.token_handler import TokenHandler
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from pr_agent.algo.utils import load_yaml, replace_code_tags, ModelType
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from pr_agent.config_loader import get_settings
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from pr_agent.git_providers import get_git_provider
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from pr_agent.git_providers.git_provider import get_main_pr_language
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from pr_agent.log import get_logger
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from pr_agent.servers.help import HelpMessage
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from pr_agent.tools.pr_description import insert_br_after_x_chars
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import difflib
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class PRCodeSuggestions:
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def __init__(self, pr_url: str, cli_mode=False, args: list = None,
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ai_handler: partial[BaseAiHandler,] = LiteLLMAIHandler):
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self.git_provider = get_git_provider()(pr_url)
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self.main_language = get_main_pr_language(
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self.git_provider.get_languages(), self.git_provider.get_files()
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)
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# limit context specifically for the improve command, which has hard input to parse:
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if get_settings().pr_code_suggestions.max_context_tokens:
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MAX_CONTEXT_TOKENS_IMPROVE = get_settings().pr_code_suggestions.max_context_tokens
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if get_settings().config.max_model_tokens > MAX_CONTEXT_TOKENS_IMPROVE:
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get_logger().info(f"Setting max_model_tokens to {MAX_CONTEXT_TOKENS_IMPROVE} for PR improve")
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get_settings().config.max_model_tokens = MAX_CONTEXT_TOKENS_IMPROVE
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# extended mode
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try:
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self.is_extended = self._get_is_extended(args or [])
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except:
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self.is_extended = False
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if self.is_extended:
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num_code_suggestions = get_settings().pr_code_suggestions.num_code_suggestions_per_chunk
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else:
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num_code_suggestions = get_settings().pr_code_suggestions.num_code_suggestions
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self.ai_handler = ai_handler()
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self.ai_handler.main_pr_language = self.main_language
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self.patches_diff = None
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self.prediction = None
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self.cli_mode = cli_mode
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self.vars = {
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"title": self.git_provider.pr.title,
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"branch": self.git_provider.get_pr_branch(),
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"description": self.git_provider.get_pr_description(),
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"language": self.main_language,
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"diff": "", # empty diff for initial calculation
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"num_code_suggestions": num_code_suggestions,
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"extra_instructions": get_settings().pr_code_suggestions.extra_instructions,
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"commit_messages_str": self.git_provider.get_commit_messages(),
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}
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self.token_handler = TokenHandler(self.git_provider.pr,
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self.vars,
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get_settings().pr_code_suggestions_prompt.system,
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get_settings().pr_code_suggestions_prompt.user)
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self.progress = f"## Generating PR code suggestions\n\n"
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self.progress += f"""\nWork in progress ...<br>\n<img src="https://codium.ai/images/pr_agent/dual_ball_loading-crop.gif" width=48>"""
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self.progress_response = None
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async def run(self):
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try:
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get_logger().info('Generating code suggestions for PR...')
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relevant_configs = {'pr_code_suggestions': dict(get_settings().pr_code_suggestions),
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'config': dict(get_settings().config)}
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get_logger().debug("Relevant configs", artifacts=relevant_configs)
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if get_settings().config.publish_output and get_settings().config.publish_output_progress:
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if self.git_provider.is_supported("gfm_markdown"):
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self.progress_response = self.git_provider.publish_comment(self.progress)
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else:
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self.git_provider.publish_comment("Preparing suggestions...", is_temporary=True)
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if not self.is_extended:
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data = await retry_with_fallback_models(self._prepare_prediction, ModelType.TURBO)
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else:
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data = await retry_with_fallback_models(self._prepare_prediction_extended, ModelType.TURBO)
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if not data:
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data = {"code_suggestions": []}
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if data is None or 'code_suggestions' not in data or not data['code_suggestions']:
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get_logger().error('No code suggestions found for PR.')
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pr_body = "## PR Code Suggestions ✨\n\nNo code suggestions found for PR."
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get_logger().debug(f"PR output", artifact=pr_body)
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if self.progress_response:
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self.git_provider.edit_comment(self.progress_response, body=pr_body)
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else:
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self.git_provider.publish_comment(pr_body)
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return
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if (not self.is_extended and get_settings().pr_code_suggestions.rank_suggestions) or \
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(self.is_extended and get_settings().pr_code_suggestions.rank_extended_suggestions):
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get_logger().info('Ranking Suggestions...')
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data['code_suggestions'] = await self.rank_suggestions(data['code_suggestions'])
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if get_settings().config.publish_output:
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self.git_provider.remove_initial_comment()
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if ((not get_settings().pr_code_suggestions.commitable_code_suggestions) and
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self.git_provider.is_supported("gfm_markdown")):
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# generate summarized suggestions
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pr_body = self.generate_summarized_suggestions(data)
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get_logger().debug(f"PR output", artifact=pr_body)
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# add usage guide
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if get_settings().pr_code_suggestions.enable_help_text:
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pr_body += "<hr>\n\n<details> <summary><strong>💡 Tool usage guide:</strong></summary><hr> \n\n"
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pr_body += HelpMessage.get_improve_usage_guide()
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pr_body += "\n</details>\n"
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if get_settings().pr_code_suggestions.persistent_comment:
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final_update_message = False
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self.git_provider.publish_persistent_comment(pr_body,
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initial_header="## PR Code Suggestions ✨",
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update_header=True,
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name="suggestions",
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final_update_message=final_update_message, )
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if self.progress_response:
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self.progress_response.delete()
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else:
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if self.progress_response:
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self.git_provider.edit_comment(self.progress_response, body=pr_body)
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else:
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self.git_provider.publish_comment(pr_body)
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else:
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self.push_inline_code_suggestions(data)
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if self.progress_response:
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self.progress_response.delete()
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except Exception as e:
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get_logger().error(f"Failed to generate code suggestions for PR, error: {e}")
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if self.progress_response:
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self.progress_response.delete()
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else:
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try:
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self.git_provider.remove_initial_comment()
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self.git_provider.publish_comment(f"Failed to generate code suggestions for PR")
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except Exception as e:
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pass
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async def _prepare_prediction(self, model: str) -> dict:
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self.patches_diff = get_pr_diff(self.git_provider,
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self.token_handler,
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model,
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add_line_numbers_to_hunks=True,
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disable_extra_lines=True)
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if self.patches_diff:
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get_logger().debug(f"PR diff", artifact=self.patches_diff)
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self.prediction = await self._get_prediction(model, self.patches_diff)
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else:
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get_logger().error(f"Error getting PR diff")
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self.prediction = None
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data = self.prediction
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return data
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async def _get_prediction(self, model: str, patches_diff: str) -> dict:
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variables = copy.deepcopy(self.vars)
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variables["diff"] = patches_diff # update diff
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environment = Environment(undefined=StrictUndefined)
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system_prompt = environment.from_string(get_settings().pr_code_suggestions_prompt.system).render(variables)
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user_prompt = environment.from_string(get_settings().pr_code_suggestions_prompt.user).render(variables)
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response, finish_reason = await self.ai_handler.chat_completion(model=model, temperature=0.2,
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system=system_prompt, user=user_prompt)
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# load suggestions from the AI response
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data = self._prepare_pr_code_suggestions(response)
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# self-reflect on suggestions
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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response_reflect = await self.self_reflect_on_suggestions(data["code_suggestions"], patches_diff)
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if response_reflect:
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response_reflect_yaml = load_yaml(response_reflect)
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code_suggestions_feedback = response_reflect_yaml["code_suggestions"]
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if len(code_suggestions_feedback) == len(data["code_suggestions"]):
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for i, suggestion in enumerate(data["code_suggestions"]):
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try:
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suggestion["score"] = code_suggestions_feedback[i]["suggestion_score"]
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suggestion["score_why"] = code_suggestions_feedback[i]["why"]
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except Exception as e: #
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get_logger().error(f"Error processing suggestion score {i}",
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artifact={"suggestion": suggestion,
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"code_suggestions_feedback": code_suggestions_feedback[i]})
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suggestion["score"] = 7
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suggestion["score_why"] = ""
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else:
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get_logger().error(f"Could not self-reflect on suggestions. using default score 7")
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for i, suggestion in enumerate(data["code_suggestions"]):
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suggestion["score"] = 7
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suggestion["score_why"] = ""
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return data
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@staticmethod
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def _truncate_if_needed(suggestion):
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max_code_suggestion_length = get_settings().get("PR_CODE_SUGGESTIONS.MAX_CODE_SUGGESTION_LENGTH", 0)
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suggestion_truncation_message = get_settings().get("PR_CODE_SUGGESTIONS.SUGGESTION_TRUNCATION_MESSAGE", "")
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if max_code_suggestion_length > 0:
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if len(suggestion['improved_code']) > max_code_suggestion_length:
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suggestion['improved_code'] = suggestion['improved_code'][:max_code_suggestion_length]
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suggestion['improved_code'] += f"\n{suggestion_truncation_message}"
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get_logger().info(f"Truncated suggestion from {len(suggestion['improved_code'])} "
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f"characters to {max_code_suggestion_length} characters")
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return suggestion
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def _prepare_pr_code_suggestions(self, predictions: str) -> Dict:
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data = load_yaml(predictions.strip(),
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keys_fix_yaml=["relevant_file", "suggestion_content", "existing_code", "improved_code"])
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if isinstance(data, list):
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data = {'code_suggestions': data}
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# remove or edit invalid suggestions
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suggestion_list = []
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one_sentence_summary_list = []
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for i, suggestion in enumerate(data['code_suggestions']):
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try:
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if (not suggestion or 'one_sentence_summary' not in suggestion or
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'label' not in suggestion or 'relevant_file' not in suggestion):
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get_logger().debug(f"Skipping suggestion {i + 1}, because it is invalid: {suggestion}")
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continue
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if suggestion['one_sentence_summary'] in one_sentence_summary_list:
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get_logger().debug(f"Skipping suggestion {i + 1}, because it is a duplicate: {suggestion}")
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continue
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if 'const' in suggestion['suggestion_content'] and 'instead' in suggestion['suggestion_content'] and 'let' in suggestion['suggestion_content']:
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get_logger().debug(f"Skipping suggestion {i + 1}, because it uses 'const instead let': {suggestion}")
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continue
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if ('existing_code' in suggestion) and ('improved_code' in suggestion):
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if suggestion['existing_code'] == suggestion['improved_code']:
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get_logger().debug(
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f"edited improved suggestion {i + 1}, because equal to existing code: {suggestion['existing_code']}")
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if get_settings().pr_code_suggestions.commitable_code_suggestions:
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suggestion['improved_code'] = "" # we need 'existing_code' to locate the code in the PR
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else:
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suggestion['existing_code'] = ""
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suggestion = self._truncate_if_needed(suggestion)
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one_sentence_summary_list.append(suggestion['one_sentence_summary'])
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suggestion_list.append(suggestion)
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else:
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get_logger().info(
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f"Skipping suggestion {i + 1}, because it does not contain 'existing_code' or 'improved_code': {suggestion}")
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except Exception as e:
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get_logger().error(f"Error processing suggestion {i + 1}: {suggestion}, error: {e}")
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data['code_suggestions'] = suggestion_list
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return data
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def push_inline_code_suggestions(self, data):
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code_suggestions = []
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if not data['code_suggestions']:
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get_logger().info('No suggestions found to improve this PR.')
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if self.progress_response:
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return self.git_provider.edit_comment(self.progress_response, body='No suggestions found to improve this PR.')
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else:
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return self.git_provider.publish_comment('No suggestions found to improve this PR.')
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for d in data['code_suggestions']:
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try:
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relevant_file = d['relevant_file'].strip()
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relevant_lines_start = int(d['relevant_lines_start']) # absolute position
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relevant_lines_end = int(d['relevant_lines_end'])
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content = d['suggestion_content'].rstrip()
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new_code_snippet = d['improved_code'].rstrip()
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label = d['label'].strip()
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if new_code_snippet:
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new_code_snippet = self.dedent_code(relevant_file, relevant_lines_start, new_code_snippet)
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if d.get('score'):
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body = f"**Suggestion:** {content} [{label}, importance: {d.get('score')}]\n```suggestion\n" + new_code_snippet + "\n```"
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else:
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body = f"**Suggestion:** {content} [{label}]\n```suggestion\n" + new_code_snippet + "\n```"
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code_suggestions.append({'body': body, 'relevant_file': relevant_file,
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'relevant_lines_start': relevant_lines_start,
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'relevant_lines_end': relevant_lines_end})
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except Exception:
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get_logger().info(f"Could not parse suggestion: {d}")
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is_successful = self.git_provider.publish_code_suggestions(code_suggestions)
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if not is_successful:
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get_logger().info("Failed to publish code suggestions, trying to publish each suggestion separately")
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for code_suggestion in code_suggestions:
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self.git_provider.publish_code_suggestions([code_suggestion])
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def dedent_code(self, relevant_file, relevant_lines_start, new_code_snippet):
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try: # dedent code snippet
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self.diff_files = self.git_provider.diff_files if self.git_provider.diff_files \
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else self.git_provider.get_diff_files()
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original_initial_line = None
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for file in self.diff_files:
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if file.filename.strip() == relevant_file:
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if file.head_file: # in bitbucket, head_file is empty. toDo: fix this
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original_initial_line = file.head_file.splitlines()[relevant_lines_start - 1]
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break
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if original_initial_line:
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suggested_initial_line = new_code_snippet.splitlines()[0]
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original_initial_spaces = len(original_initial_line) - len(original_initial_line.lstrip())
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suggested_initial_spaces = len(suggested_initial_line) - len(suggested_initial_line.lstrip())
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delta_spaces = original_initial_spaces - suggested_initial_spaces
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if delta_spaces > 0:
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new_code_snippet = textwrap.indent(new_code_snippet, delta_spaces * " ").rstrip('\n')
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except Exception as e:
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get_logger().error(f"Could not dedent code snippet for file {relevant_file}, error: {e}")
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return new_code_snippet
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def _get_is_extended(self, args: list[str]) -> bool:
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"""Check if extended mode should be enabled by the `--extended` flag or automatically according to the configuration"""
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if any(["extended" in arg for arg in args]):
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get_logger().info("Extended mode is enabled by the `--extended` flag")
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return True
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if get_settings().pr_code_suggestions.auto_extended_mode:
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get_logger().info("Extended mode is enabled automatically based on the configuration toggle")
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return True
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return False
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async def _prepare_prediction_extended(self, model: str) -> dict:
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self.patches_diff_list = get_pr_multi_diffs(self.git_provider, self.token_handler, model,
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max_calls=get_settings().pr_code_suggestions.max_number_of_calls)
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if self.patches_diff_list:
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get_logger().info(f"Number of PR chunk calls: {len(self.patches_diff_list)}")
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get_logger().debug(f"PR diff:", artifact=self.patches_diff_list)
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# parallelize calls to AI:
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if get_settings().pr_code_suggestions.parallel_calls:
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prediction_list = await asyncio.gather(
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*[self._get_prediction(model, patches_diff) for patches_diff in self.patches_diff_list])
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self.prediction_list = prediction_list
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else:
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prediction_list = []
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for i, patches_diff in enumerate(self.patches_diff_list):
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prediction = await self._get_prediction(model, patches_diff)
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prediction_list.append(prediction)
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data = {"code_suggestions": []}
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for j, predictions in enumerate(prediction_list): # each call adds an element to the list
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if "code_suggestions" in predictions:
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score_threshold = max(1, get_settings().pr_code_suggestions.suggestions_score_threshold)
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for i, prediction in enumerate(predictions["code_suggestions"]):
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try:
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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score = int(prediction["score"])
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if score >= score_threshold:
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data["code_suggestions"].append(prediction)
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else:
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get_logger().info(
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f"Removing suggestions {i} from call {j}, because score is {score}, and score_threshold is {score_threshold}",
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artifact=prediction)
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else:
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data["code_suggestions"].append(prediction)
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except Exception as e:
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get_logger().error(f"Error getting PR diff for suggestion {i} in call {j}, error: {e}")
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self.data = data
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else:
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get_logger().error(f"Error getting PR diff")
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self.data = data = None
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return data
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async def rank_suggestions(self, data: List) -> List:
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"""
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Call a model to rank (sort) code suggestions based on their importance order.
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Args:
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data (List): A list of code suggestions to be ranked.
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Returns:
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List: The ranked list of code suggestions.
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"""
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suggestion_list = []
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if not data:
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return suggestion_list
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for suggestion in data:
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suggestion_list.append(suggestion)
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data_sorted = [[]] * len(suggestion_list)
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if len(suggestion_list) == 1:
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return suggestion_list
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try:
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suggestion_str = ""
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for i, suggestion in enumerate(suggestion_list):
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suggestion_str += f"suggestion {i + 1}: " + str(suggestion) + '\n\n'
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variables = {'suggestion_list': suggestion_list, 'suggestion_str': suggestion_str}
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model = get_settings().config.model
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environment = Environment(undefined=StrictUndefined)
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system_prompt = environment.from_string(get_settings().pr_sort_code_suggestions_prompt.system).render(
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variables)
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user_prompt = environment.from_string(get_settings().pr_sort_code_suggestions_prompt.user).render(variables)
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response, finish_reason = await self.ai_handler.chat_completion(model=model, system=system_prompt,
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user=user_prompt)
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sort_order = load_yaml(response)
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for s in sort_order['Sort Order']:
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suggestion_number = s['suggestion number']
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importance_order = s['importance order']
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data_sorted[importance_order - 1] = suggestion_list[suggestion_number - 1]
|
|
|
|
if get_settings().pr_code_suggestions.final_clip_factor != 1:
|
|
max_len = max(
|
|
len(data_sorted),
|
|
get_settings().pr_code_suggestions.num_code_suggestions,
|
|
get_settings().pr_code_suggestions.num_code_suggestions_per_chunk,
|
|
)
|
|
new_len = int(0.5 + max_len * get_settings().pr_code_suggestions.final_clip_factor)
|
|
if new_len < len(data_sorted):
|
|
data_sorted = data_sorted[:new_len]
|
|
except Exception as e:
|
|
if get_settings().config.verbosity_level >= 1:
|
|
get_logger().info(f"Could not sort suggestions, error: {e}")
|
|
data_sorted = suggestion_list
|
|
|
|
return data_sorted
|
|
|
|
def generate_summarized_suggestions(self, data: Dict) -> str:
|
|
try:
|
|
pr_body = "## PR Code Suggestions ✨\n\n"
|
|
|
|
if len(data.get('code_suggestions', [])) == 0:
|
|
pr_body += "No suggestions found to improve this PR."
|
|
return pr_body
|
|
|
|
language_extension_map_org = get_settings().language_extension_map_org
|
|
extension_to_language = {}
|
|
for language, extensions in language_extension_map_org.items():
|
|
for ext in extensions:
|
|
extension_to_language[ext] = language
|
|
|
|
pr_body = "## PR Code Suggestions ✨\n\n"
|
|
|
|
pr_body += "<table>"
|
|
header = f"Suggestion"
|
|
delta = 66
|
|
header += " " * delta
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
pr_body += f"""<thead><tr><td>Category</td><td align=left>{header}</td><td align=center>Score</td></tr>"""
|
|
else:
|
|
pr_body += f"""<thead><tr><td>Category</td><td align=left>{header}</td></tr>"""
|
|
pr_body += """<tbody>"""
|
|
suggestions_labels = dict()
|
|
# add all suggestions related to each label
|
|
for suggestion in data['code_suggestions']:
|
|
label = suggestion['label'].strip().strip("'").strip('"')
|
|
if label not in suggestions_labels:
|
|
suggestions_labels[label] = []
|
|
suggestions_labels[label].append(suggestion)
|
|
|
|
# sort suggestions_labels by the suggestion with the highest score
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
suggestions_labels = dict(sorted(suggestions_labels.items(), key=lambda x: max([s['score'] for s in x[1]]), reverse=True))
|
|
# sort the suggestions inside each label group by score
|
|
for label, suggestions in suggestions_labels.items():
|
|
suggestions_labels[label] = sorted(suggestions, key=lambda x: x['score'], reverse=True)
|
|
|
|
|
|
for label, suggestions in suggestions_labels.items():
|
|
num_suggestions=len(suggestions)
|
|
pr_body += f"""<tr><td rowspan={num_suggestions}><strong>{label.capitalize()}</strong></td>\n"""
|
|
for i, suggestion in enumerate(suggestions):
|
|
|
|
relevant_file = suggestion['relevant_file'].strip()
|
|
relevant_lines_start = int(suggestion['relevant_lines_start'])
|
|
relevant_lines_end = int(suggestion['relevant_lines_end'])
|
|
range_str = ""
|
|
if relevant_lines_start == relevant_lines_end:
|
|
range_str = f"[{relevant_lines_start}]"
|
|
else:
|
|
range_str = f"[{relevant_lines_start}-{relevant_lines_end}]"
|
|
|
|
try:
|
|
code_snippet_link = self.git_provider.get_line_link(relevant_file, relevant_lines_start,
|
|
relevant_lines_end)
|
|
except:
|
|
code_snippet_link = ""
|
|
# add html table for each suggestion
|
|
|
|
suggestion_content = suggestion['suggestion_content'].rstrip().rstrip()
|
|
|
|
suggestion_content = insert_br_after_x_chars(suggestion_content, 90)
|
|
# pr_body += f"<tr><td><details><summary>{suggestion_content}</summary>"
|
|
existing_code = suggestion['existing_code'].rstrip()+"\n"
|
|
improved_code = suggestion['improved_code'].rstrip()+"\n"
|
|
|
|
diff = difflib.unified_diff(existing_code.split('\n'),
|
|
improved_code.split('\n'), n=999)
|
|
patch_orig = "\n".join(diff)
|
|
patch = "\n".join(patch_orig.splitlines()[5:]).strip('\n')
|
|
|
|
example_code = ""
|
|
example_code += f"```diff\n{patch}\n```\n"
|
|
if i==0:
|
|
pr_body += f"""<td>\n\n"""
|
|
else:
|
|
pr_body += f"""<tr><td>\n\n"""
|
|
suggestion_summary = suggestion['one_sentence_summary'].strip().rstrip('.')
|
|
if '`' in suggestion_summary:
|
|
suggestion_summary = replace_code_tags(suggestion_summary)
|
|
|
|
pr_body += f"""\n\n<details><summary>{suggestion_summary}</summary>\n\n___\n\n"""
|
|
pr_body += f"""
|
|
**{suggestion_content}**
|
|
|
|
[{relevant_file} {range_str}]({code_snippet_link})
|
|
|
|
{example_code}
|
|
"""
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
pr_body +=f"\n\n<details><summary><b>Suggestion importance[1-10]: {suggestion['score']}</b></summary>\n\n"
|
|
pr_body += f"Why: {suggestion['score_why']}\n\n"
|
|
pr_body += f"</details>"
|
|
|
|
pr_body += f"</details>"
|
|
|
|
# # add another column for 'score'
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
pr_body += f"</td><td align=center>{suggestion['score']}\n\n"
|
|
|
|
pr_body += f"</td></tr>"
|
|
|
|
|
|
# pr_body += "</details>"
|
|
# pr_body += """</td></tr>"""
|
|
pr_body += """</tr></tbody></table>"""
|
|
return pr_body
|
|
except Exception as e:
|
|
get_logger().info(f"Failed to publish summarized code suggestions, error: {e}")
|
|
return ""
|
|
|
|
async def self_reflect_on_suggestions(self, suggestion_list: List, patches_diff: str) -> str:
|
|
if not suggestion_list:
|
|
return ""
|
|
|
|
try:
|
|
suggestion_str = ""
|
|
for i, suggestion in enumerate(suggestion_list):
|
|
suggestion_str += f"suggestion {i + 1}: " + str(suggestion) + '\n\n'
|
|
|
|
variables = {'suggestion_list': suggestion_list,
|
|
'suggestion_str': suggestion_str,
|
|
"diff": patches_diff,
|
|
'num_code_suggestions': len(suggestion_list)}
|
|
model = get_settings().config.model
|
|
environment = Environment(undefined=StrictUndefined)
|
|
system_prompt_reflect = environment.from_string(get_settings().pr_code_suggestions_reflect_prompt.system).render(
|
|
variables)
|
|
user_prompt_reflect = environment.from_string(get_settings().pr_code_suggestions_reflect_prompt.user).render(variables)
|
|
with get_logger().contextualize(command="self_reflect_on_suggestions"):
|
|
response_reflect, finish_reason_reflect = await self.ai_handler.chat_completion(model=model,
|
|
system=system_prompt_reflect,
|
|
user=user_prompt_reflect)
|
|
except Exception as e:
|
|
get_logger().info(f"Could not reflect on suggestions, error: {e}")
|
|
return ""
|
|
return response_reflect |