# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

from typing import AsyncGenerator, List, Optional

from llama_stack_client import LlamaStackClient

from llama_stack.apis.common.content_types import InterleavedContent
from llama_stack.apis.inference import (
    EmbeddingsResponse,
    Inference,
    LogProbConfig,
    Message,
    ResponseFormat,
    SamplingParams,
    ToolChoice,
    ToolConfig,
    ToolDefinition,
    ToolPromptFormat,
)
from llama_stack.apis.models import Model
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper

from .config import PassthroughImplConfig


class PassthroughInferenceAdapter(Inference):
    def __init__(self, config: PassthroughImplConfig) -> None:
        ModelRegistryHelper.__init__(self, [])
        self.config = config

    async def initialize(self) -> None:
        pass

    async def shutdown(self) -> None:
        pass

    async def unregister_model(self, model_id: str) -> None:
        pass

    async def register_model(self, model: Model) -> Model:
        return model

    def _get_client(self) -> LlamaStackClient:
        passthrough_url = None
        passthrough_api_key = None
        provider_data = None

        if self.config.url is not None:
            passthrough_url = self.config.url
        else:
            provider_data = self.get_request_provider_data()
            if provider_data is None or not provider_data.passthrough_url:
                raise ValueError(
                    'Pass url of the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_url": <your passthrough url>}'
                )
            passthrough_url = provider_data.passthrough_url

        if self.config.api_key is not None:
            passthrough_api_key = self.config.api_key.get_secret_value()
        else:
            provider_data = self.get_request_provider_data()
            if provider_data is None or not provider_data.passthrough_api_key:
                raise ValueError(
                    'Pass API Key for the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_api_key": <your api key>}'
                )
            passthrough_api_key = provider_data.passthrough_api_key

        return LlamaStackClient(
            base_url=passthrough_url,
            api_key=passthrough_api_key,
            provider_data=provider_data,
        )

    async def completion(
        self,
        model_id: str,
        content: InterleavedContent,
        sampling_params: Optional[SamplingParams] = SamplingParams(),
        response_format: Optional[ResponseFormat] = None,
        stream: Optional[bool] = False,
        logprobs: Optional[LogProbConfig] = None,
    ) -> AsyncGenerator:
        client = self._get_client()
        model = await self.model_store.get_model(model_id)

        params = {
            "model_id": model.provider_resource_id,
            "content": content,
            "sampling_params": sampling_params,
            "response_format": response_format,
            "stream": stream,
            "logprobs": logprobs,
        }

        params = {key: value for key, value in params.items() if value is not None}

        # only pass through the not None params
        return client.inference.completion(**params)

    async def chat_completion(
        self,
        model_id: str,
        messages: List[Message],
        sampling_params: Optional[SamplingParams] = SamplingParams(),
        tools: Optional[List[ToolDefinition]] = None,
        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
        tool_prompt_format: Optional[ToolPromptFormat] = None,
        response_format: Optional[ResponseFormat] = None,
        stream: Optional[bool] = False,
        logprobs: Optional[LogProbConfig] = None,
        tool_config: Optional[ToolConfig] = None,
    ) -> AsyncGenerator:
        client = self._get_client()
        model = await self.model_store.get_model(model_id)

        params = {
            "model_id": model.provider_resource_id,
            "messages": messages,
            "sampling_params": sampling_params,
            "tools": tools,
            "tool_choice": tool_choice,
            "tool_prompt_format": tool_prompt_format,
            "response_format": response_format,
            "stream": stream,
            "logprobs": logprobs,
        }

        params = {key: value for key, value in params.items() if value is not None}

        # only pass through the not None params
        return client.inference.chat_completion(**params)

    async def embeddings(
        self,
        model_id: str,
        contents: List[InterleavedContent],
    ) -> EmbeddingsResponse:
        client = self._get_client()
        model = await self.model_store.get_model(model_id)

        return client.inference.embeddings(
            model_id=model.provider_resource_id,
            contents=contents,
        )