# Copyright (c) 2024-2026 Tencent Zhuque Lab. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Requirement: Any integration or derivative work must explicitly attribute
# Tencent Zhuque Lab (https://github.com/Tencent/AI-Infra-Guard) in its
# documentation or user interface, as detailed in the NOTICE file.

import json
import uuid
from typing import Optional

from core.agent_adapter.adapter import ProviderOptions
from tools.dispatcher import ToolDispatcher
from utils.aig_logger import scanLogger
from utils.llm import LLM, is_llm_error_response
from utils.logging import logger
from utils.parse import parse_tool_invocations, clean_content
from utils.prompt_manager import prompt_manager
from utils.tool_context import ToolContext


class BaseAgent:

    def __init__(
            self,
            name: str,
            instruction: str,
            llm: LLM,
            specialized_llms: dict = None,
            log_step_id: str = None,
            debug: bool = False,
            agent_provider: Optional[ProviderOptions] = None,
            language: str = "zh",
            format_on_finish: bool = True,
    ):
        self.llm = llm
        self.name = name
        self.specialized_llms = specialized_llms or {}
        self.instruction = instruction
        self.history = []
        self.max_iter = 40
        self.iter = 0
        self.is_finished = False
        self.step_id = log_step_id
        self.debug = debug
        self.repo_dir = ""
        self.agent_provider = agent_provider
        self.language = language
        self.format_on_finish = format_on_finish
        self.dispatcher = ToolDispatcher()
        self.tool_usage_stats = {}

    async def initialize(self):
        """Asynchronously initialize system prompt"""
        if not self.history:
            system_prompt = await self.generate_system_prompt()
            self.history.append({"role": "system", "content": system_prompt})

    def add_user_message(self, message: str):
        self.history.append({"role": "user", "content": message})

    def set_repo_dir(self, repo_dir: str):
        self.repo_dir = repo_dir

    async def compact_history(self) -> bool:
        if len(self.history) < 3:
            return False

        prompt = prompt_manager.load_template("compact")
        history = self.history[1:]
        history.append({"role": "user", "content": prompt})
        response = await self.llm.chat_async(history, language=self.language)
        if is_llm_error_response(response):
            logger.warning(f"History compaction skipped due to LLM error response: {response}")
            return False

        system_prompt = self.history[0]
        original_task = self.history[1]['content']
        user_messages = f"I want you to complete: {original_task}\n\nThe following context is provided for reference:\n{response}"
        self.history = [system_prompt, {"role": "user", "content": user_messages}]
        return True

    async def generate_system_prompt(self):

        tools_prompt = await self.dispatcher.get_all_tools_prompt()

        template_name = "system_prompt"
        format_kwargs = {
            "generate_tools": tools_prompt,
            "name": self.name,
            "instruction": self.instruction
        }

        return prompt_manager.format_prompt(template_name, **format_kwargs)

    def next_prompt(self):
        return prompt_manager.format_prompt("next_prompt", round=self.iter)

    def _latest_assistant_fallback(self) -> str:
        for message in reversed(self.history):
            if message.get("role") != "assistant":
                continue
            content = clean_content(message.get("content", ""))
            if content and not is_llm_error_response(content):
                return content
        return ""

    def _append_model_error_recovery_message(self, response: str):
        self.history.append({"role": "assistant", "content": response})
        self.history.append({
            "role": "user",
            "content": (
                "The previous request could not be completed due to a model-side error. "
                "Please continue with the next step in your plan."
            )
        })

    async def run(self):
        await self.initialize()
        return await self._run()

    async def _run(self):
        logger.info(f"Agent {self.name} started with max_iter={self.max_iter}")
        # Compact history when this fraction of max iterations has been consumed.
        compact_threshold = int(self.max_iter * 0.7)
        compacted = False
        result = ""
        consecutive_failures = 0
        max_consecutive_failures = 3
        
        while not self.is_finished and self.iter < self.max_iter:
            logger.debug(f"\n{'=' * 50}\nIteration {self.iter}\n{'=' * 50}")

            # Compact once at 70 % of max iterations so the agent can keep running
            # with a condensed context rather than hitting the hard limit cold.
            if not compacted and self.iter >= compact_threshold:
                logger.warning(f"Compacting history at iteration {self.iter} (threshold={compact_threshold})")
                compacted = await self.compact_history()

            # Use the non-blocking async wrapper so the event loop is free to
            # schedule other coroutines (e.g. parallel skill workers in
            # ScanPipeline.run_parallel_detection) while awaiting the response.
            try:
                response = await self.llm.chat_async(self.history, language=self.language)
                logger.debug(f"LLM Response: {response}")

                # Check if the response is an error (degraded string returned by llm.chat)
                # Use the constant LLM_ERROR_PREFIX for consistent error detection
                if is_llm_error_response(response):
                    consecutive_failures += 1
                    logger.warning(
                        f"LLM error response: {response}. "
                        f"Consecutive failures: {consecutive_failures}/{max_consecutive_failures}"
                    )

                    # Terminate agent if too many consecutive errors
                    if consecutive_failures >= max_consecutive_failures:
                        logger.error(
                            f"Max consecutive LLM failures ({max_consecutive_failures}) reached. "
                            f"Terminating agent."
                        )
                        self.is_finished = True
                        break

                    # Write error message as assistant + user pair to history,
                    # ensuring history always ends with a user message,
                    # while letting LLM know the previous step failed.
                    self._append_model_error_recovery_message(response)
                    self.iter += 1
                    continue

                # Successful response, reset error count
                consecutive_failures = 0

                self.history.append({"role": "assistant", "content": response})
                res = await self.handle_response(response)
                if res is not None:
                    result = res
                self.iter += 1

            except Exception as e:
                # Catch unexpected exceptions (llm.chat should degrade to string, this is a fallback)
                consecutive_failures += 1
                logger.error(
                    f"Unexpected error in agent iteration {self.iter}: {e}. "
                    f"Consecutive failures: {consecutive_failures}/{max_consecutive_failures}"
                )

                if consecutive_failures >= max_consecutive_failures:
                    logger.error(
                        f"Max consecutive exceptions ({max_consecutive_failures}) reached. "
                        f"Terminating agent."
                    )
                    self.is_finished = True
                    break

                # Write as valid assistant + user pair to avoid invalid role sequence
                self.history.append({
                    "role": "assistant",
                    "content": f"[System Error: {str(e)[:200]}]"
                })
                self.history.append({
                    "role": "user",
                    "content": "An unexpected error occurred. Please continue with the next step."
                })
                self.iter += 1
                continue

        if self.iter >= self.max_iter:
            logger.warning(f"Max iterations ({self.max_iter}) reached without finish signal")

        return result

    async def handle_response(self, response: str):
        # Parse tool invocation (only first invocation supported)
        tool_invocation = parse_tool_invocations(response)
        description = clean_content(response)
        if description == "":
            description = "I will continue to execute"

        scanLogger.status_update(self.step_id, description, "", "running")

        if tool_invocation:
            return await self.process_tool_call(tool_invocation, description)
        else:
            return await self.handle_no_tool(description)

    async def process_tool_call(self, tool_call: dict, description: str):
        tool_name = tool_call["toolName"]
        tool_args = tool_call["args"]
        tool_id = uuid.uuid4().__str__()

        params = json.dumps(tool_args, ensure_ascii=False) if tool_args else ""
        if isinstance(params, str):
            params = params.replace(self.repo_dir, "")

        scanLogger.tool_used(self.step_id, tool_id, tool_name, "done", tool_name, f"{params}")

        # Update stats
        if tool_name not in self.tool_usage_stats:
            self.tool_usage_stats[tool_name] = 0
        self.tool_usage_stats[tool_name] += 1

        if tool_name == "finish":
            self.is_finished = True
            if self.format_on_finish:
                # Full stages (recon, review): reformat the entire history via LLM
                # to produce a clean, structured final report.
                result = await self._format_final_output()
            else:
                # Skill workers: the last assistant turn already contains the
                # <vuln> XML blocks emitted just before the finish() call.
                # brief_content is only a plain-text summary (no XML) — always
                # use the full assistant message so _extract_vuln_blocks() can
                # find the <vuln> tags.
                result = self.history[-1].get("content", "")
            logger.info("Finish tool called, final result formatted.")
            scanLogger.status_update(self.step_id, description, "", "completed")
            scanLogger.action_log(tool_id, tool_name, self.step_id, result)
            return result

        # Build context
        context = ToolContext(
            llm=self.llm,
            history=self.history,
            agent_name=self.name,
            iteration=self.iter,
            specialized_llms=self.specialized_llms,
            folder=self.repo_dir,
            agent_provider=self.agent_provider,
            language=self.language,
        )

        # Call tool via Dispatcher
        tool_result = await self.dispatcher.call_tool(tool_name, tool_args, context)

        # Format tool result and add to history
        result_message = f"{tool_result}"

        # Add next prompt
        next_p = self.next_prompt()
        full_message = f"{next_p}\n---\n{result_message}"

        self.history.append({"role": "user", "content": full_message})
        logger.debug(f"Agent Response: {result_message}")

        scanLogger.status_update(self.step_id, description, "", "completed")

        if tool_name != "read_file":
            scanLogger.action_log(tool_id, tool_name, self.step_id, f"```\n{result_message}\n```")

        return None


    async def handle_no_tool(self, description: str):
        next_p = self.next_prompt()
        result_message = "You didn't call any tool. Please call a tool to continue."
        full_message = f"{next_p}\n\n{result_message}"
        logger.debug(f"Agent Response: {result_message}")
        self.history.append({"role": "user", "content": full_message})
        scanLogger.status_update(self.step_id, description, "", "completed")
        return None

    async def _format_final_output(self) -> str:
        """Use LLM to generate final output based on history and preset format"""
        # Use recent conversation history as reference
        recent_history = self.history[1:]
        formatting_prompt = prompt_manager.format_prompt(
            "format_report",
            output_format=self.instruction
        )
        recent_history.append({"role": "user", "content": formatting_prompt})
        final_output = await self.llm.chat_async(recent_history, language=self.language)
        if is_llm_error_response(final_output):
            logger.warning(f"Final output formatting skipped due to LLM error response: {final_output}")
            fallback = self._latest_assistant_fallback()
            if fallback:
                return fallback
        logger.info(f"Final Output: {final_output}")
        return final_output


async def run_agent(description: str, instruction: str, llm: LLM, prompt: str, stage_id: str,
                    specialized_llms: dict | None = None, agent_provider: ProviderOptions | None = None,
                    language: str = "zh", repo_dir: str | None = None, context_data: dict | None = None,
                    format_on_finish: bool = True):
    logger.info(f"=== Stage {stage_id}: {description} ===")
    scanLogger.new_plan_step(stepId=stage_id, stepName=description)

    if language == "en":
        instruction = (
            "You are generating security scanning outputs.\n"
            "All of your outputs (including intermediate notes and final reports) MUST be in English.\n"
            "Some of the following instructions and example templates are written in Chinese; "
            "they are ONLY examples of structure and content types. "
            "Follow their structure, but ALWAYS write your own output in English.\n\n"
        ) + instruction
    else:
        instruction = (
            "你正在生成安全扫描相关的输出。\n"
            "所有输出（包括中间说明和最终报告）必须使用中文撰写。\n"
            "下面的指令和示例模板如果包含英文内容，仅作为结构示例，你的实际输出依然需要使用中文。\n\n"
        ) + instruction

    # Initialize stage agent
    agent = BaseAgent(
        name=f"{description}",
        instruction=instruction,
        llm=llm,
        specialized_llms=specialized_llms,
        log_step_id=stage_id,
        debug=True,
        agent_provider=agent_provider,
        language=language,
        format_on_finish=format_on_finish,
    )
    await agent.initialize()
    if repo_dir:
        agent.set_repo_dir(repo_dir)
        user_msg = f"Please perform {description}. The repository folder is {repo_dir}\n"
    else:
        user_msg = ""
    if prompt:
        user_msg += f"{prompt}\n"
    if context_data:
        user_msg += "\n\nThe following background information is provided:\n"
        for key, value in context_data.items():
            user_msg += f"{key}:{value}\n\n"

    agent.add_user_message(user_msg)

    # Run and return results
    result = await agent.run()
    return result, agent.tool_usage_stats
