"""
This is OpenAI compatible - no transformation is applied

"""

from typing import Final

import httpx

from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
from litellm.types.utils import EmbeddingResponse, Usage

from ..common_utils import SambaNovaError


class SambaNovaEmbeddingConfig(BaseEmbeddingConfig):
    def __init__(self) -> None:
        pass

    def get_complete_url(
        self,
        api_base: str | None,
        api_key: str | None,
        model: str,
        optional_params: dict,
        litellm_params: dict,
        stream: bool | None = None,
    ) -> str:
        if api_base is None:
            raise ValueError("api_base is required for SambaNova embeddings")
        # Remove trailing slashes and ensure clean base URL
        api_base = api_base.rstrip("/")
        if not api_base.endswith("/embeddings"):
            api_base = f"{api_base}/embeddings"
        return api_base

    def validate_environment(
        self,
        headers: dict,
        model: str,
        messages: list[AllMessageValues],
        optional_params: dict,
        litellm_params: dict,
        api_key: str | None = None,
        api_base: str | None = None,
    ) -> dict:
        if api_key is None:
            api_key = get_secret_str("SAMBANOVA_API_KEY")

        default_headers: Final = {
            "Authorization": f"Bearer {api_key}",
            "accept": "application/json",
            "Content-Type": "application/json",
        }

        # If 'Authorization' is provided in headers, it overrides the default.
        if "Authorization" in headers:
            default_headers["Authorization"] = headers["Authorization"]

        # Merge other headers, overriding any default ones except Authorization
        return {**default_headers, **headers}

    def get_supported_openai_params(self, model: str):
        """
        Non additional params supported, placeholder method for future supported params
        https://docs.sambanova.ai/cloud/api-reference/endpoints/embeddings-api
        """
        return []

    def map_openai_params(
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ):
        """
        No transformation is applied - SambaNova is openai compatible
        """
        supported_openai_params: Final = self.get_supported_openai_params(model)
        for param, value in non_default_params.items():
            if param in supported_openai_params:
                optional_params[param] = value
        return optional_params

    def transform_embedding_request(
        self,
        model: str,
        input: AllEmbeddingInputValues,
        optional_params: dict,
        headers: dict,
    ) -> dict:
        return {
            "input": input,
            "model": model,
            **optional_params,
        }

    def transform_embedding_response(
        self,
        model: str,
        raw_response: httpx.Response,
        model_response: EmbeddingResponse,
        logging_obj: LiteLLMLoggingObj,
        api_key: str | None,
        request_data: dict,
        optional_params: dict,
        litellm_params: dict,
    ) -> EmbeddingResponse:
        try:
            raw_response_json: Final = raw_response.json()
        except Exception:
            raise SambaNovaError(
                message=raw_response.text,
                status_code=raw_response.status_code,
                headers=raw_response.headers,
            )

        model_response.model = raw_response_json.get("model")
        model_response.data = raw_response_json.get("data")
        model_response.object = raw_response_json.get("object")

        usage: Final = Usage(
            prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0),
            total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
        )

        model_response.usage = usage
        return model_response

    def get_error_class(self, error_message: str, status_code: int, headers: dict | httpx.Headers) -> BaseLLMException:
        return SambaNovaError(message=error_message, status_code=status_code, headers=headers)
