"""Unified inference engine for continuous batching."""

import asyncio
import gc
import threading
from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Tuple, Union

import torch
import torch.nn as nn

from astrai.inference.core.cache import PagePool
from astrai.inference.core.scheduler import InferenceScheduler
from astrai.inference.core.task import STOP
from astrai.tokenize import AutoTokenizer


class GenerateResult:
    """Thread-safe token accumulator for streaming and non-streaming modes."""

    def __init__(self, count: int = 1):
        self._cond = threading.Condition()
        self._event = threading.Event()
        self.tokens: List[Tuple[int, str]] = []
        self.results: List[str] = [""] * count
        self._done: List[bool] = [False] * count
        self._completed = 0
        self._total = count

    def append(self, token: str, idx: int = 0):
        with self._cond:
            self.tokens.append((idx, token))
            if token is not STOP:
                self.results[idx] += token
            else:
                if not self._done[idx]:
                    self._done[idx] = True
                    self._completed += 1
                    self._cond.notify_all()
            self._event.set()

    def pop_all(self) -> List[Tuple[int, str]]:
        with self._cond:
            out = self.tokens.copy()
            self.tokens.clear()
            if not out:
                self._event.clear()
            return out

    def wait(self, timeout: Optional[float] = None) -> bool:
        return self._event.wait(timeout=timeout)

    def wait_completion(self, timeout: float = 300.0):
        with self._cond:
            if not self._cond.wait_for(
                lambda: self._completed >= self._total, timeout=timeout
            ):
                raise TimeoutError(
                    f"Generation timeout after {timeout}s "
                    f"({self._completed}/{self._total} completed)"
                )

    def get_results(self) -> List[str]:
        with self._cond:
            return self.results.copy()


class InferenceEngine:
    """Unified inference engine backed by continuous-batching scheduler."""

    def __init__(
        self,
        model: nn.Module,
        tokenizer: AutoTokenizer,
        max_batch_size: int = 1,
        max_seq_len: Optional[int] = None,
        cache: Optional[PagePool] = None,
    ):
        self.model = model
        self.tokenizer = tokenizer
        self.scheduler = InferenceScheduler(
            model=self.model,
            tokenizer=self.tokenizer,
            max_batch_size=max_batch_size,
            max_seq_len=max_seq_len,
            cache=cache,
        )

        self.scheduler.start()

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.shutdown()
        return False

    def generate(
        self,
        prompt: Union[str, List[str]],
        stream: bool = False,
        max_tokens: Optional[int] = None,
        temperature: float = 1.0,
        top_p: float = 1.0,
        top_k: int = 50,
        frequency_penalty: float = 0.0,
        rep_window: int = 64,
    ) -> Union[Generator, str, List[str]]:
        is_batch = isinstance(prompt, list)
        prompts = prompt if is_batch else [prompt]

        if max_tokens is not None and max_tokens <= 0:
            if stream:
                return iter(())
            results = [""] * len(prompts)
            return results if is_batch else results[0]

        return self._generate(
            prompts,
            is_batch,
            stream,
            max_tokens,
            temperature,
            top_p,
            top_k,
            frequency_penalty,
            rep_window,
        )

    def generate_async(
        self,
        prompt: str,
        max_tokens: Optional[int] = None,
        temperature: float = 1.0,
        top_p: float = 1.0,
        top_k: int = 50,
        frequency_penalty: float = 0.0,
        rep_window: int = 64,
    ) -> AsyncGenerator[str, None]:
        sync_gen = self._generate(
            [prompt],
            False,
            True,
            max_tokens,
            temperature,
            top_p,
            top_k,
            frequency_penalty,
            rep_window,
        )

        async def _agen():
            loop = asyncio.get_event_loop()
            while True:
                try:
                    token = await loop.run_in_executor(None, next, sync_gen)
                except StopIteration:
                    break
                yield token

        return _agen()

    def _generate(
        self,
        prompts: List[str],
        is_batch: bool,
        stream: bool,
        max_tokens: Optional[int],
        temperature: float,
        top_p: float,
        top_k: int,
        frequency_penalty: float,
        rep_window: int,
    ) -> Union[Generator, str, List[str]]:
        n = len(prompts)
        result = GenerateResult(count=n)
        task_ids = [
            self.scheduler.add_task(
                prompt=p,
                max_tokens=max_tokens,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                frequency_penalty=frequency_penalty,
                rep_window=rep_window,
                stream_callback=lambda token, idx=i: result.append(token, idx),
            )
            for i, p in enumerate(prompts)
        ]

        if not stream:
            try:
                result.wait_completion()
            except TimeoutError:
                for tid in task_ids:
                    self.scheduler.remove_task(tid)
                raise
            for tid in task_ids:
                self.scheduler.remove_task(tid)
            res = result.get_results()
            return res if is_batch else res[0]

        remaining = n
        finished = [False] * n

        def gen():
            nonlocal remaining
            while remaining > 0:
                items = result.pop_all()
                for idx, token in items:
                    if token is STOP:
                        if not finished[idx]:
                            finished[idx] = True
                            remaining -= 1
                    else:
                        yield (idx, token) if is_batch else token
                if remaining > 0:
                    result.wait(timeout=0.05)

        return gen()

    def get_stats(self) -> Dict[str, Any]:
        return self.scheduler.get_stats()

    def shutdown(self):
        self.scheduler.stop()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()