"""Middleware for runtime model selection via LangGraph runtime context.

Allows switching the model per invocation by passing a `CLIContext` via
`context=` on `agent.astream()` / `agent.invoke()` without recompiling
the graph.
"""

from __future__ import annotations

import asyncio
import logging
from typing import TYPE_CHECKING, Any

from deepagents._models import model_matches_spec  # noqa: PLC2701
from langchain.agents.middleware.types import (
    AgentMiddleware,
    ModelRequest,
    ModelResponse,
)

from deepagents_code._cli_context import CLIContextSchema

if TYPE_CHECKING:
    from collections.abc import Awaitable, Callable

    from deepagents_code.config import ModelResult


logger = logging.getLogger(__name__)


def _is_anthropic_model(model: object) -> bool:
    """Check whether a resolved model reports `'anthropic'` as its provider.

    Uses `_get_ls_params` from `BaseChatModel` to read the provider name.

    Args:
        model: A model instance to inspect.

            Typed as `object` (rather than `BaseChatModel`) so the caller can
            pass any model without an import-time dependency on a specific
            provider package.

    Returns:
        `True` if the model's `ls_provider` is `'anthropic'`.
    """
    try:
        ls_params = model._get_ls_params()  # ty: ignore[unresolved-attribute]
    except (AttributeError, TypeError, RuntimeError):
        logger.debug(
            "_get_ls_params raised for %s; assuming non-Anthropic",
            type(model).__name__,
        )
        return False
    return isinstance(ls_params, dict) and ls_params.get("ls_provider") == "anthropic"


_ANTHROPIC_ONLY_SETTINGS: set[str] = {"cache_control"}
"""Keys injected by Anthropic-specific middleware (e.g.
`AnthropicPromptCachingMiddleware`) that are not accepted by other providers and
must be stripped on cross-provider swap."""


def _get_context(request: ModelRequest) -> CLIContextSchema | None:
    """Return runtime context when it matches the CLI context shape."""
    runtime = request.runtime
    if runtime is None:
        return None

    ctx = runtime.context
    if isinstance(ctx, CLIContextSchema):
        return ctx
    if isinstance(ctx, dict):
        raw_key = ctx.get("approval_mode_key")
        return CLIContextSchema(
            model=ctx.get("model"),
            model_params=ctx.get("model_params") or {},
            effective_model=ctx.get("effective_model"),
            auto_approve=bool(ctx.get("auto_approve", False)),
            approval_mode_key=raw_key if isinstance(raw_key, str) else None,
        )
    return None


def _build_overrides(
    request: ModelRequest, ctx: CLIContextSchema, model_result: ModelResult | None
) -> ModelRequest:
    """Build the overridden request from a (possibly resolved) model result.

    Holds the post-construction logic shared by the sync and async override
    paths: applying the model swap, merging `model_params`, stripping
    Anthropic-only settings on a cross-provider swap, and patching the
    `### Model Identity` system-prompt section. The only thing that differs
    between the two callers is how `model_result` is produced (a direct
    `create_model` call vs. an `asyncio.to_thread` offload).

    Args:
        request: The incoming model request from the middleware chain.
        ctx: Runtime CLI context carrying the requested overrides.
        model_result: The resolved model result from `create_model`, or `None`
            when no model swap was requested.

    Returns:
        The original request when no overrides apply, otherwise a new request
            with overrides applied via `request.override()`.
    """
    overrides: dict[str, Any] = {}

    new_model = model_result.model if model_result is not None else None
    if new_model is not None:
        overrides["model"] = new_model

    # Param merge
    model_params = ctx.model_params
    if model_params:
        overrides["model_settings"] = {**request.model_settings, **model_params}

    if not overrides:
        return request

    # When switching away from Anthropic, strip provider-specific settings
    # that would cause errors on other providers (e.g. cache_control passed
    # to the OpenAI SDK raises TypeError).
    if new_model is not None and not _is_anthropic_model(new_model):
        settings = overrides.get("model_settings", request.model_settings)
        dropped = settings.keys() & _ANTHROPIC_ONLY_SETTINGS
        if dropped:
            logger.debug(
                "Stripped Anthropic-only settings %s for non-Anthropic model",
                dropped,
            )
            overrides["model_settings"] = {
                k: v for k, v in settings.items() if k not in dropped
            }

    # Patch the Model Identity section in the system prompt so the new model
    # sees its own name/provider/context-limit, not the original's.
    # We read metadata from model_result (not the app's settings singleton)
    # because the middleware runs in the server subprocess where settings
    # are never updated by /model.
    if model_result is not None and request.system_prompt:
        from deepagents_code.agent import (
            MODEL_IDENTITY_RE,
            build_model_identity_section,
        )

        prompt = request.system_prompt
        new_identity = build_model_identity_section(
            model_result.model_name,
            provider=model_result.provider,
            context_limit=model_result.context_limit,
            unsupported_modalities=model_result.unsupported_modalities,
        )
        patched = MODEL_IDENTITY_RE.sub(new_identity, prompt, count=1)
        if patched != prompt:
            overrides["system_prompt"] = patched
        elif "### Model Identity" in prompt:
            logger.warning(
                "System prompt contains '### Model Identity' but regex "
                "did not match; identity section was NOT updated for "
                "model '%s'. The regex may be out of sync with the "
                "prompt template.",
                model_result.model_name,
            )

    return request.override(**overrides)


def _apply_overrides(request: ModelRequest) -> ModelRequest:
    """Apply model/param overrides from `CLIContext` on the runtime.

    Reads `'model'` and `'model_params'` from `runtime.context` and, when
    present, swaps the model and/or merges extra settings into the request.
    On a cross-provider swap away from Anthropic, Anthropic-only settings
    (e.g. `cache_control`) are stripped. The `### Model Identity` section
    in the system prompt is also patched to reflect the new model.

    Args:
        request: The incoming model request from the middleware chain.

    Returns:
        The original request unchanged when no `CLIContext` is present or it
            contains no overrides, otherwise a new request with overrides
            applied via `request.override()`.
    """
    ctx = _get_context(request)
    if ctx is None:
        return request

    model_result = None
    model = ctx.model
    if model and not model_matches_spec(request.model, model):
        from deepagents_code.config import create_model
        from deepagents_code.model_config import ModelConfigError

        logger.debug("Overriding model to %s", model)
        try:
            model_result = create_model(model)
        except ModelConfigError:
            logger.exception(
                "Failed to resolve runtime model override '%s'; "
                "continuing with current model",
                model,
            )
            return request

    return _build_overrides(request, ctx, model_result)


async def _apply_overrides_async(request: ModelRequest) -> ModelRequest:
    """Async variant of `_apply_overrides` that offloads model construction.

    Returns:
        The original request when no async override applies, otherwise a request
            with the runtime model or settings override applied.
    """
    ctx = _get_context(request)
    if ctx is None:
        return request

    model_result = None
    model = ctx.model
    if model and not model_matches_spec(request.model, model):
        from deepagents_code.config import create_model
        from deepagents_code.model_config import ModelConfigError

        logger.debug("Overriding model to %s", model)
        try:
            model_result = await asyncio.to_thread(create_model, model)
        except ModelConfigError:
            logger.exception(
                "Failed to resolve runtime model override '%s'; "
                "continuing with current model",
                model,
            )
            return request

    return _build_overrides(request, ctx, model_result)


class ConfigurableModelMiddleware(AgentMiddleware):
    """Swap the model or per-call settings from `runtime.context`.

    Reads two optional keys from the runtime context dict:

    - `'model'` — a `provider:model` spec (e.g. `"openai:gpt-5"`).
        When present and different from the current model, the request is
        re-routed to the new model.
    - `'model_params'` — a dict of extra model settings (e.g.
        `{"temperature": 0}`) that are shallow-merged into the
        request's `model_settings`.

    This middleware is typically the outermost layer so it intercepts every
    model call before provider-specific middleware (like
    `AnthropicPromptCachingMiddleware`) runs.
    """

    def wrap_model_call(  # noqa: PLR6301
        self,
        request: ModelRequest,
        handler: Callable[[ModelRequest], ModelResponse],
    ) -> ModelResponse:
        """Apply runtime overrides and delegate to the next handler.

        Returns:
            The `ModelResponse` produced by the downstream handler.
        """
        return handler(_apply_overrides(request))

    async def awrap_model_call(  # noqa: PLR6301
        self,
        request: ModelRequest,
        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
    ) -> ModelResponse:
        """Apply runtime overrides and delegate to the next async handler.

        Returns:
            The `ModelResponse` produced by the downstream handler.
        """
        return await handler(await _apply_overrides_async(request))
