import random
import logging
import sys

from fastapi import Request
from open_webui.models.users import UserModel
from open_webui.models.models import Models
from open_webui.utils.models import check_model_access
from open_webui.env import SRC_LOG_LEVELS, GLOBAL_LOG_LEVEL, BYPASS_MODEL_ACCESS_CONTROL

from open_webui.routers.openai import embeddings as openai_embeddings
from open_webui.routers.ollama import embeddings as ollama_embeddings
from open_webui.routers.ollama import GenerateEmbeddingsForm
from open_webui.routers.pipelines import process_pipeline_inlet_filter


from open_webui.utils.payload import convert_embedding_payload_openai_to_ollama
from open_webui.utils.response import convert_embedding_response_ollama_to_openai

logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["MAIN"])


async def generate_embeddings(
    request: Request,
    form_data: dict,
    user: UserModel,
    bypass_filter: bool = False,
):
    """
    Dispatch and handle embeddings generation based on the model type (OpenAI, Ollama, Arena, pipeline, etc).

    Args:
        request (Request): The FastAPI request context.
        form_data (dict): The input data sent to the endpoint.
        user (UserModel): The authenticated user.
        bypass_filter (bool): If True, disables access filtering (default False).

    Returns:
        dict: The embeddings response, following OpenAI API compatibility.
    """
    if BYPASS_MODEL_ACCESS_CONTROL:
        bypass_filter = True

    # Attach extra metadata from request.state if present
    if hasattr(request.state, "metadata"):
        if "metadata" not in form_data:
            form_data["metadata"] = request.state.metadata
        else:
            form_data["metadata"] = {
                **form_data["metadata"],
                **request.state.metadata,
            }

    # If "direct" flag present, use only that model
    if getattr(request.state, "direct", False) and hasattr(request.state, "model"):
        models = {
            request.state.model["id"]: request.state.model,
        }
    else:
        models = request.app.state.MODELS

    model_id = form_data.get("model")
    if model_id not in models:
        raise Exception("Model not found")
    model = models[model_id]

    # Access filtering
    if not getattr(request.state, "direct", False):
        if not bypass_filter and user.role == "user":
            check_model_access(user, model)

    # Arena "meta-model": select a submodel at random
    if model.get("owned_by") == "arena":
        model_ids = model.get("info", {}).get("meta", {}).get("model_ids")
        filter_mode = model.get("info", {}).get("meta", {}).get("filter_mode")
        if model_ids and filter_mode == "exclude":
            model_ids = [
                m["id"]
                for m in list(models.values())
                if m.get("owned_by") != "arena" and m["id"] not in model_ids
            ]
        if isinstance(model_ids, list) and model_ids:
            selected_model_id = random.choice(model_ids)
        else:
            model_ids = [
                m["id"]
                for m in list(models.values())
                if m.get("owned_by") != "arena"
            ]
            selected_model_id = random.choice(model_ids)
        inner_form = dict(form_data)
        inner_form["model"] = selected_model_id
        response = await generate_embeddings(
            request, inner_form, user, bypass_filter=True
        )
        # Tag which concreted model was chosen
        if isinstance(response, dict):
            response = {
                **response,
                "selected_model_id": selected_model_id,
            }
        return response

    # Pipeline/Function models
    if model.get("pipe"):
        # The pipeline handler should provide OpenAI-compatible schema
        return await process_pipeline_inlet_filter(request, form_data, user, models)

    # Ollama backend
    if model.get("owned_by") == "ollama":
        ollama_payload = convert_embedding_payload_openai_to_ollama(form_data)
        form_obj = GenerateEmbeddingsForm(**ollama_payload)
        response = await ollama_embeddings(
            request=request,
            form_data=form_obj,
            user=user,
        )
        return convert_embedding_response_ollama_to_openai(response)

    # Default: OpenAI or compatible backend
    return await openai_embeddings(
        request=request,
        form_data=form_data,
        user=user,
    )