from __future__ import annotations

import json, re
from typing import Any

from agent_framework.guardrails.base import Guardrail, RailDecision
from .tim_prompts.ausencia_oferta_proativa import build_aoferta_prompt
from .tim_prompts.out_of_scope import build_oos_prompt
from .tim_prompts.revprec import build_revprec_prompt
from .tim_prompts.fraseologia import build_fraseologia_prompt


def _llm(context: dict[str, Any]):
    return context.get('guardrail_llm') or context.get('llm') or context.get('model')


def _context_text(context: dict[str, Any]) -> str:
    try: return json.dumps(context or {}, ensure_ascii=False, default=str)[:16000]
    except Exception: return str(context or {})[:16000]


def _parse_json(raw: Any) -> dict[str, Any]:
    text = str(getattr(raw, 'content', raw) or '').strip()
    m = re.search(r'\{[\s\S]*\}', text)
    if m: text=m.group(0)
    try: return json.loads(text)
    except Exception: return {'allowed': False, 'reason': f'Resposta inválida do guardrail TIM: {text[:300]}'}


class _TimPromptRail(Guardrail):
    prompt_builder = None
    profile_name = 'guardrail'

    async def evaluate(self, text: str, context: dict[str, Any]) -> RailDecision:
        llm = _llm(context)
        if llm is None:
            return RailDecision(code=self.code, allowed=False, reason='LLM do framework indisponível para guardrail TIM', metadata={'external': True, 'fail_closed': True})
        prompt = self.prompt_builder(text or '', _context_text(context))
        raw = await llm.ainvoke(
            [{'role':'system','content':'Responda apenas JSON válido, sem markdown.'}, {'role':'user','content':prompt}],
            profile_name=self.profile_name,
            component_name=f'guardrail.external.{self.code.lower()}',
            generation_name=f'guardrail.external.{self.code.lower()}',
        )
        out = _parse_json(raw)
        return RailDecision(code=self.code, allowed=bool(out.get('allowed', False)), reason=str(out.get('reason') or out.get('label') or ''), sanitized_text=text, metadata={'external': True, 'domain':'TIM_CONTAS', 'data':out})


class TimOutOfScopeRail(_TimPromptRail):
    code='TIM_OOS'; stage='output'; prompt_builder=staticmethod(build_oos_prompt)

class TimProactiveOfferRail(_TimPromptRail):
    code='TIM_AOFERTA'; stage='output'; prompt_builder=staticmethod(build_aoferta_prompt)

    # Mantém a mesma semântica do AOFERTA nativo do framework: mensagens de
    # continuidade de uma transação já aberta não constituem nova oferta
    # proativa. O bypass é estritamente dirigido pelo estado transacional, sem
    # inferência textual e sem desabilitar outros guardrails de saída.
    _TRANSACTION_CONTINUATION_STATUSES = {
        'COLLECTING_PARAMETERS',
        'AWAITING_CONFIRMATION',
    }

    @classmethod
    def _transaction_continuation_status(cls, context: dict[str, Any]) -> str | None:
        ctx = context or {}
        status = str(ctx.get('transaction_status') or '').strip().upper()
        if status in cls._TRANSACTION_CONTINUATION_STATUSES:
            return status

        # Compatibilidade com o contexto de output do Contas e com callers que
        # expõem o estado apenas no resultado da tool. É o mesmo fallback usado
        # pelo ProactiveOfferRail nativo: mcp_results -> tool_result.
        results = ctx.get('mcp_results') or ctx.get('tool_result') or []
        if isinstance(results, dict):
            results = [results]
        for result in reversed(list(results)):
            if not isinstance(result, dict):
                continue
            result_status = str(result.get('transaction_status') or '').strip().upper()
            if result_status in cls._TRANSACTION_CONTINUATION_STATUSES:
                return result_status
        return None

    async def evaluate(self, text: str, context: dict[str, Any]) -> RailDecision:
        continuation_status = self._transaction_continuation_status(context)
        if continuation_status:
            return RailDecision(
                code=self.code,
                allowed=True,
                reason=f'continuidade_transacional:{continuation_status}',
                sanitized_text=text,
                metadata={
                    'external': True,
                    'domain': 'TIM_CONTAS',
                    'mechanism': 'deterministic_transaction_bypass',
                    'transaction_status': continuation_status,
                    'data': {
                        'allowed': True,
                        'reason': f'continuidade_transacional:{continuation_status}',
                    },
                },
            )
        return await super().evaluate(text, context)

class TimPrematureActionRail(_TimPromptRail):
    code='TIM_REVPREC'; stage='output'; profile_name='grl'; prompt_builder=staticmethod(build_revprec_prompt)

    async def evaluate(self, text: str, context: dict[str, Any]) -> RailDecision:
        llm = _llm(context)
        if llm is None:
            return RailDecision(code=self.code, allowed=False, reason='LLM do framework indisponível para guardrail TIM', metadata={'external': True, 'fail_closed': True})
        prompt = self.prompt_builder(text or '', _context_text(context))
        raw = await llm.ainvoke(
            [{'role':'system','content':'Responda apenas com o dígito 1 ou 0, sem texto adicional.'}, {'role':'user','content':prompt}],
            profile_name=self.profile_name, component_name='guardrail.external.tim_revprec', generation_name='guardrail.external.tim_revprec')
        output = str(getattr(raw, 'content', raw) or '').strip()
        digits = [ch for ch in output if ch in '01']
        # Prompt original: 1 = violação / verbalização prematura; 0 = permitido.
        allowed = not digits or digits[-1] != '1'
        return RailDecision(code=self.code, allowed=allowed, reason='' if allowed else 'verbalização prematura segundo política TIM Contas', sanitized_text=text, metadata={'external':True,'domain':'TIM_CONTAS','raw':output[:100], 'terminal_action':'retry'})

class TimPhraseologyRail(_TimPromptRail):
    code='TIM_FRASEOLOGIA'; stage='output'; profile_name='grl'; prompt_builder=staticmethod(build_fraseologia_prompt)

    async def evaluate(self, text: str, context: dict[str, Any]) -> RailDecision:
        result = await super().evaluate(text, context)
        result.metadata = {
            **dict(result.metadata or {}),
            'remediation': {
                'type': 'rewrite', 'max_attempts': 1, 'prompt_id': 'FALLBACK',
                'profile_name': 'grl', 'component_name': 'guardrail.wording.rewrite',
                'generation_name': 'guardrail.wording.rewrite',
            },
        }
        return result