# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
# Exported for HuggingFace trust_remote_code loading.
# This file is intentionally self-contained.

from __future__ import annotations

from contextlib import contextmanager
from threading import RLock
from typing import Protocol

import torch
from torch import nn

from .model_state import RuntimeCacheSnapshot
from .runtime_contracts import RuntimeHost


class DecoderRuntimeConfig(Protocol):
    max_seq_len: int
    max_batch_size: int
    loss_chunk_size: int
    gradient_checkpointing_exclude_first: int
    gradient_checkpointing_exclude_last: int


class DecoderRuntimeModel(Protocol):
    tok_embeddings: nn.Module
    output: nn.Module
    gradient_checkpointing: bool
    gradient_checkpointing_exclude_first: int
    gradient_checkpointing_exclude_last: int
    def modules(self): ...
    def parameters(self, recurse: bool = True): ...


class DecoderRuntimeBase:
    def _runtime_model(self) -> DecoderRuntimeModel:
        from typing import cast

        return cast(DecoderRuntimeModel, self.model)

    def _runtime_host(self) -> RuntimeHost:
        from typing import cast

        return cast(RuntimeHost, self.runtime_host)

    def _runtime_config(self) -> DecoderRuntimeConfig:
        from typing import cast

        return cast(DecoderRuntimeConfig, self.config)

    @property
    def runtime_model(self):
        return self._runtime_model()

    @property
    def runtime_host(self):
        return self.runtime

    @property
    def runtime_lock(self) -> RLock:
        return self._model_runtime_lock


class DecoderRuntimeMixin(DecoderRuntimeBase):
    def reset_runtime_cache(self) -> None:
        self._runtime_host().reset_runtime_cache()

    def refresh_state_buffers(self) -> None:
        self._runtime_host().refresh_state_buffers()

    def replay_with_cache(
        self,
        input_ids: torch.Tensor,
        start_pos: int = 0,
        return_all_logits: bool = True,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        return self._runtime_host().replay_with_cache(
            input_ids,
            start_pos=start_pos,
            return_all_logits=return_all_logits,
        )

    def forward_with_last_hidden(
        self,
        input_ids: torch.Tensor,
        start_pos: int = 0,
        return_all_logits: bool = True,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        return self._runtime_host().forward_with_last_hidden(
            input_ids,
            start_pos=start_pos,
            return_all_logits=return_all_logits,
        )

    def cache_dump(
        self,
        device: str = "cpu",
        *,
        cache_pos: int | None = None,
        batch_size: int | None = None,
    ) -> RuntimeCacheSnapshot:
        return self._runtime_host().cache_dump(
            device=device,
            cache_pos=cache_pos,
            batch_size=batch_size,
        )

    def cache_load(self, cache_snapshot: RuntimeCacheSnapshot) -> None:
        self._runtime_host().cache_load(cache_snapshot)

    def runtime_max_seq_len(self) -> int:
        return int(self._runtime_host().runtime_max_seq_len())

    def _sync_runtime_gradient_checkpointing(self, *, enable: bool) -> None:
        model = self._runtime_model()
        config = self._runtime_config()
        model.gradient_checkpointing = bool(enable)
        model.gradient_checkpointing_exclude_first = int(
            config.gradient_checkpointing_exclude_first
        )
        model.gradient_checkpointing_exclude_last = int(
            config.gradient_checkpointing_exclude_last
        )


class DecoderModelMixin(DecoderRuntimeBase):
    def _sync_tied_runtime_word_embeddings(self) -> None:
        config = getattr(self, "config", None)
        if not bool(getattr(config, "tie_word_embeddings", False)):
            return
        runtime_model = self._runtime_model()
        input_embeddings = runtime_model.tok_embeddings
        output_embeddings = runtime_model.output
        if not hasattr(input_embeddings, "weight") or not hasattr(
            output_embeddings, "weight"
        ):
            return
        input_weight = input_embeddings.weight
        output_weight = output_embeddings.weight
        if tuple(input_weight.shape) != tuple(output_weight.shape):
            raise ValueError(
                "tied word embeddings require matching embedding/output shapes; "
                f"got {tuple(input_weight.shape)!r} vs {tuple(output_weight.shape)!r}"
            )
        output_embeddings.weight = input_weight

    def _rebuild_runtime_buffers(self) -> None:
        self._runtime_host().rebuild_runtime_buffers()

    @staticmethod
    @contextmanager
    def get_input_embeddings(self) -> nn.Module:
        return self._runtime_model().tok_embeddings

    def set_input_embeddings(self, value: nn.Module) -> None:
        with self._model_runtime_lock:
            self.reset_runtime_cache()
            self._runtime_model().tok_embeddings = value
            self._sync_tied_runtime_word_embeddings()

    def get_output_embeddings(self) -> nn.Module:
        return self._runtime_model().output

    def set_output_embeddings(self, value: nn.Module) -> None:
        with self._model_runtime_lock:
            self.reset_runtime_cache()
            self._runtime_model().output = value
            self._sync_tied_runtime_word_embeddings()

    def to(self, *args, **kwargs):
        with self._model_runtime_lock:
            runtime_model = self._runtime_model()
            complex_buffers: list[tuple[nn.Module, str, torch.Tensor]] = []
            for module in runtime_model.modules():
                for name, buf in list(getattr(module, "_buffers", {}).items()):
                    if torch.is_tensor(buf) and torch.is_complex(buf):
                        complex_buffers.append((module, name, module._buffers.pop(name)))
            try:
                module = super().to(*args, **kwargs)
            finally:
                root_param = next(runtime_model.parameters(), None)
                root_device = (
                    torch.device("cpu") if root_param is None else root_param.device
                )
                for owner, name, buf in complex_buffers:
                    owner_param = next(owner.parameters(), None)
                    target_device = (
                        root_device if owner_param is None else owner_param.device
                    )
                    owner.register_buffer(
                        name,
                        buf.to(device=target_device),
                        persistent=False,
                    )
                self._rebuild_runtime_buffers()
            return module

    def get_submodule(self, target: str) -> nn.Module:
        try:
            return super().get_submodule(target)
        except AttributeError:
            return self.model.get_submodule(target)

    def train(self, mode: bool = True):
        with self._model_runtime_lock:
            if bool(mode):
                self.reset_runtime_cache()
            return super().train(mode)

    def load_state_dict(
        self,
        state_dict: dict[str, torch.Tensor],
        strict: bool = True,
        assign: bool = False,
    ):
        with self._model_runtime_lock:
            self.reset_runtime_cache()
            result = super().load_state_dict(
                state_dict,
                strict=strict,
                assign=assign,
            )
            self._sync_tied_runtime_word_embeddings()
            self._rebuild_runtime_buffers()
            return result


class DecoderRecipeMixin(DecoderRuntimeBase):
    def ensure_runtime_max_seq_len(self, max_seq_len: int) -> None:
        required = int(max_seq_len)
        config = self._runtime_config()
        if required <= 0:
            raise ValueError(f"max_seq_len must be > 0, got {max_seq_len}")
        if required > int(config.max_seq_len):
            raise ValueError(
                "runtime max_seq_len cannot exceed config.max_seq_len: "
                f"{required} > {int(config.max_seq_len)}"
            )
        with self._model_runtime_lock:
            self.reset_runtime_cache()
            self._runtime_host().ensure_runtime_max_seq_len(required)

    def supports_loss_chunk_size(self) -> bool:
        return bool(self._runtime_host().supports_loss_chunk_size())

    def supports_checkpoint_excludes(self) -> bool:
        return bool(self._runtime_host().supports_checkpoint_excludes())

    def runtime_recipe_knobs(self) -> tuple[int, int, int]:
        return self._runtime_host().runtime_recipe_knobs()

    def apply_runtime_recipe_knobs(
        self,
        *,
        loss_chunk_size: int,
        gradient_checkpointing_exclude_first: int,
        gradient_checkpointing_exclude_last: int,
    ) -> tuple[int, int, int]:
        with self._model_runtime_lock:
            chunk_size = int(loss_chunk_size)
            exclude_first = int(gradient_checkpointing_exclude_first)
            exclude_last = int(gradient_checkpointing_exclude_last)
            if chunk_size != 0 and not self.supports_loss_chunk_size():
                raise ValueError("decoder runtime does not support loss_chunk_size")
            if (exclude_first != 0 or exclude_last != 0) and not (
                self.supports_checkpoint_excludes()
            ):
                raise ValueError(
                    "decoder runtime does not support gradient checkpoint exclusions"
                )
            config = self._runtime_config()
            config.loss_chunk_size = int(chunk_size)
            config.gradient_checkpointing_exclude_first = int(exclude_first)
            config.gradient_checkpointing_exclude_last = int(exclude_last)
            return self._runtime_host().apply_runtime_recipe_knobs(
                loss_chunk_size=int(chunk_size),
                gradient_checkpointing_exclude_first=int(exclude_first),
                gradient_checkpointing_exclude_last=int(exclude_last),
            )

    def sync_runtime_batch_capacity(self, max_batch_size: int) -> int:
        with self._model_runtime_lock:
            batch_size = self._runtime_host().sync_runtime_batch_capacity(
                int(max_batch_size)
            )
            self._runtime_config().max_batch_size = int(batch_size)
            return int(batch_size)


__all__ = [
    "DecoderRuntimeConfig",
    "DecoderRuntimeBase",
    "DecoderRuntimeModel",
    "DecoderModelMixin",
    "DecoderRecipeMixin",
    "DecoderRuntimeMixin",
]