from dataclasses import dataclass, field
import logging

from agent.agent_exceptions import InferenceError, MaxTurnsExceeded
from agent.conversation_history import ConversationHistory
from agent.documents import build_documents_block
from agent.skill_manager import SkillsManager
from agent.prompts import SKILLS_SYSTEM_PROMPT
from helpers.timing import timed_block
from providers.exceptions import ProviderError
from providers.protocol import ChatProvider, Message, ReasoningEffort, ToolCall


logger = logging.getLogger(__name__)

DEFAULT_AGENT_MODEL = "openai/gpt-oss-20b"
DEFAULT_AGENT_MAX_TURNS = 10
DEFAULT_REASONING_EFFORT: ReasoningEffort = "medium"


@dataclass
class ToolCallRecord:
    id: str
    function_name: str
    arguments: dict
    result: str


@dataclass
class AgentResponse:
    content: str
    tool_calls: list[ToolCallRecord] = field(default_factory=list)
    n_tokens: int = 0

    @property
    def activated_skills(self) -> list[ToolCallRecord]:
        return [tc for tc in self.tool_calls if tc.function_name == "activate_skill"]

    @property
    def executed_functions(self) -> list[ToolCallRecord]:
        return [tc for tc in self.tool_calls if tc.function_name == "execute_function"]

    def to_judge_context(self) -> dict:
        return {
            "agent_response": self.content,
            "tool_calls": [
                {
                    "function_name": tc.function_name,
                    "arguments": tc.arguments,
                    "result": tc.result,
                }
                for tc in self.tool_calls
            ],
        }


class Agent:
    def __init__(
        self,
        skills: SkillsManager,
        provider: ChatProvider,
        model_id: str = DEFAULT_AGENT_MODEL,
        system_prompt: str = SKILLS_SYSTEM_PROMPT,
        reasoning_effort: ReasoningEffort | None = DEFAULT_REASONING_EFFORT,
        max_turns: int = DEFAULT_AGENT_MAX_TURNS,
    ) -> None:
        self.skills = skills
        self.provider = provider
        self.model_id = model_id
        self.system_prompt = system_prompt
        self.reasoning_effort = reasoning_effort
        self.max_turns = max_turns

    def chat(
        self,
        query: str,
        conversation: ConversationHistory,
        *,
        system: bool = False,
        documents: dict[str, str] | None = None,
    ) -> AgentResponse:
        if system:
            conversation.record_system(query)
        else:
            conversation.record_user(query)

        tool_call_records: list[ToolCallRecord] = []
        n_tokens = 0

        for _ in range(self.max_turns):
            with timed_block("agent.llm_call"):
                try:
                    completion = self.provider.chat(
                        messages=self._build_messages(conversation, documents),
                        model_id=self.model_id,
                        tools=self.skills.tools_for(conversation),
                        reasoning_effort=self.reasoning_effort,
                    )
                except ProviderError as e:
                    raise InferenceError(str(e)) from e

            n_tokens += completion.usage.total_tokens
            msg = completion.message
            reasoning = msg.reasoning

            if msg.tool_calls:
                for tc in msg.tool_calls:
                    try:
                        record, should_return = self._dispatch_tool_call(
                            tc, conversation, documents
                        )
                    except Exception as exc:
                        conversation.record_tool_exchange(
                            tool_call_id=tc.id,
                            function_name=tc.name,
                            arguments=tc.arguments,
                            result=f"[DISPATCH ERROR] {type(exc).__name__}: {exc}",
                            reasoning=reasoning,
                        )
                        raise
                    conversation.record_tool_exchange(
                        tool_call_id=record.id,
                        function_name=record.function_name,
                        arguments=record.arguments,
                        result=record.result,
                        reasoning=reasoning,
                    )
                    tool_call_records.append(record)
                    if should_return:
                        conversation.record_assistant(record.result, reasoning=None)
                        return AgentResponse(
                            content=record.result,
                            tool_calls=tool_call_records,
                            n_tokens=n_tokens,
                        )
                continue

            if msg.content is None:
                raise ValueError("Provider returned neither tool_calls nor content")

            conversation.record_assistant(msg.content, reasoning=reasoning)
            return AgentResponse(
                content=msg.content, tool_calls=tool_call_records, n_tokens=n_tokens
            )

        raise MaxTurnsExceeded(f"Exceeded {self.max_turns} turns without a final reply")

    def _build_messages(
        self,
        conversation: ConversationHistory,
        documents: dict[str, str] | None = None,
    ) -> list[Message]:
        system_content = self._build_system_prompt()
        docs_block = build_documents_block(documents)
        if docs_block:
            system_content = f"{system_content}\n\n{docs_block}"
        return [
            Message(role="system", content=system_content),
            *conversation.to_messages(),
        ]

    def _build_system_prompt(self) -> str:
        return self.system_prompt.format(
            skill_list=self.skills.to_system_prompt_format()
        )

    def _dispatch_tool_call(
        self,
        tc: ToolCall,
        conversation: ConversationHistory,
        documents: dict[str, str] | None = None,
    ) -> tuple[ToolCallRecord, bool]:
        should_return = False
        try:
            if tc.name == "activate_skill":
                instructions = self.skills.activate(**tc.arguments)
                result = f"Instructions: {instructions}"
            elif tc.name == "execute_function":
                result, should_return = self.skills.execute(
                    **tc.arguments, conversation=conversation, documents=documents
                )
            else:
                result = f"Error: Unknown function: {tc.name}"
        except TypeError as e:
            result = f"An unexpected keyword argument was passed to the function you were trying to call: {e}"
            logger.warning(
                "Model generated an unexpected keyword argument for a tool call."
            )

        return (
            ToolCallRecord(
                id=tc.id, function_name=tc.name, arguments=tc.arguments, result=result
            ),
            should_return,
        )