from __future__ import annotations

from collections.abc import Awaitable, Callable
from dataclasses import dataclass
from typing import Any, TypeAlias

from pydantic_ai._utils import await_maybe
from pydantic_ai.agent import Agent
from pydantic_ai.capabilities import NativeTool
from pydantic_ai.capabilities._native_resolution import resolve_native_tool
from pydantic_ai.exceptions import ModelRetry, UnexpectedModelBehavior, UserError
from pydantic_ai.messages import BinaryImage
from pydantic_ai.models import KnownModelName, Model, parse_model_id
from pydantic_ai.native_tools import ImageGenerationTool
from pydantic_ai.tools import AgentDepsT, RunContext, Tool

ImageGenerationFallbackModelFunc = Callable[
    [RunContext[Any]],
    Awaitable[Model | KnownModelName | str] | Model | KnownModelName | str,
]
"""Callable that resolves the subagent's model dynamically per-run.

May return a `Model` instance or a model name string (e.g. `'openai-responses:gpt-5.4'`);
strings are resolved to a model at call time.
"""

ImageGenerationFallbackModel = Model | KnownModelName | str | ImageGenerationFallbackModelFunc | None
"""Type of [`ImageGeneration.fallback_subagent_model`][pydantic_ai.capabilities.ImageGeneration.fallback_subagent_model]: a model, model name, factory callable, or None."""

ImageGenerationNativeTool: TypeAlias = (
    ImageGenerationTool | Callable[[RunContext[AgentDepsT]], Awaitable[ImageGenerationTool] | ImageGenerationTool]
)
"""Type for the native tool: an `ImageGenerationTool` instance, or a callable resolving one from the run context.

The callable resolves once per fallback subagent invocation, from that tool call's
[`RunContext`][pydantic_ai.tools.RunContext]. It belongs to the same run and carries the same
`deps` as the resolution on the native path, but it is not the same context: `tool_call_id` and
`tool_name` name the fallback tool call rather than being `None`, and `messages` holds the run so
far. Read `ctx.deps` for configuration that has to match across both.

Unlike the capability-level `native=` parameter, this callable may not return `None`: omitting the
tool is meaningless once the subagent has been invoked, so returning `None` anyway raises
[`UserError`][pydantic_ai.exceptions.UserError] rather than enabling a default `ImageGenerationTool`.
"""

__all__ = (
    'ImageGenerationFallbackModel',
    'ImageGenerationFallbackModelFunc',
    'ImageGenerationNativeTool',
    'ImageGenerationSubagentTool',
    'image_generation_tool',
)

# Known image-only model names that don't support the conversational Agent loop
# required by the subagent fallback, mapped to suggested LLM alternatives.
_IMAGE_ONLY_MODELS: dict[str, str] = {
    'gpt-image-2': 'openai-responses:gpt-5.5',
    'gpt-image-1.5': 'openai-responses:gpt-5.5',
    'gpt-image-1': 'openai-responses:gpt-5.4',
    'gpt-image-1-mini': 'openai-responses:gpt-5.4',
    'dall-e-3': 'openai-responses:gpt-5.4',
    'dall-e-2': 'openai-responses:gpt-5.4',
    'imagen-3.0-generate-002': 'google:gemini-3-pro-image',
    'imagen-3.0-fast-generate-001': 'google:gemini-3-pro-image',
    # xAI has no conversational model that supports the `ImageGenerationTool`, so the Grok Imagine
    # family's suggested alternative crosses providers; `fallback_image_model='xai:grok-imagine-image'`
    # is the way to stay on xAI, which the error's first sentence points at.
    'grok-imagine-image': 'openai-responses:gpt-5.5',
    'grok-imagine-image-2.0': 'openai-responses:gpt-5.5',
    'grok-imagine-image-2026-03-02': 'openai-responses:gpt-5.5',
    'grok-imagine-image-quality': 'openai-responses:gpt-5.5',
    'grok-imagine-image-quality-20260403': 'openai-responses:gpt-5.5',
    'grok-imagine-image-quality-latest': 'openai-responses:gpt-5.5',
    'grok-imagine-image-pro': 'openai-responses:gpt-5.5',
}


def _check_image_only_model(model: str) -> None:
    """Raise UserError if the model is a known image-only model."""
    _, model_name = parse_model_id(model)
    if suggestion := _IMAGE_ONLY_MODELS.get(model_name):
        raise UserError(
            f'{model_name!r} is a dedicated image generation model that cannot be used as '
            f'`fallback_subagent_model` directly. Pass it to `fallback_image_model` instead, or use '
            f'a conversational model with image generation support, e.g. {suggestion!r}.'
        )


@dataclass(kw_only=True)
class ImageGenerationSubagentTool:
    """Local image generation tool that delegates to a subagent.

    Uses a subagent with the specified model and native tool configuration
    to generate images when the outer agent's model doesn't support image
    generation natively.
    """

    model: Model | KnownModelName | str | ImageGenerationFallbackModelFunc
    """The model to use for image generation, or a callable that returns one."""

    native_tool: ImageGenerationNativeTool[Any]
    """The image generation configuration or outer-run factory to pass to the subagent."""

    instructions: str = 'Generate an image based on the user prompt. Do not ask clarifying questions.'
    """Instructions for the subagent that generates the image."""

    async def __call__(self, ctx: RunContext[Any], prompt: str) -> BinaryImage:
        """Generate an image using a subagent.

        Args:
            ctx: The run context from the outer agent.
            prompt: A description of the image to generate.
        """
        model = self.model
        if callable(model):
            model = await await_maybe(model(ctx))

        if isinstance(model, str) and callable(self.model):
            # Only check at call time for dynamically resolved models;
            # static strings are already validated at factory time
            _check_image_only_model(model)

        native_tool = await resolve_native_tool(ImageGenerationTool, self.native_tool, ctx)

        agent = Agent(
            model,
            output_type=BinaryImage,
            capabilities=[NativeTool(native_tool)],
            instructions=self.instructions,
        )
        try:
            result = await agent.run(prompt)
        except UnexpectedModelBehavior as e:
            # `ContentFilterError` is an `UnexpectedModelBehavior`, so a moderation block becomes a
            # retry prompt too. Nothing the subagent raises escapes the tool call, which is what
            # keeps a durable engine from retrying the tool activity against an error class its
            # non-retryable list doesn't name.
            raise ModelRetry(str(e)) from e
        return result.output


def image_generation_tool(
    model: Model | KnownModelName | str | ImageGenerationFallbackModelFunc,
    native_tool: ImageGenerationNativeTool[Any],
    *,
    instructions: str = 'Generate an image based on the user prompt. Do not ask clarifying questions.',
) -> Tool[Any]:
    """Creates an image generation tool backed by a subagent.

    Args:
        model: The model to use for image generation (e.g. `'openai-responses:gpt-5.4'`),
            or a callable taking `RunContext` that returns a model.
        native_tool: The image generation configuration, or a callable that resolves it from the outer run context.
        instructions: Instructions for the subagent that generates the image.
    """
    if isinstance(model, str):
        _check_image_only_model(model)
    return Tool[Any](
        ImageGenerationSubagentTool(model=model, native_tool=native_tool, instructions=instructions).__call__,
        name='generate_image',
        description='Generate an image based on the given prompt.',
    )