import asyncio
from typing import Any, Coroutine, Optional, Union

import httpx

from litellm import LlmProviders
from litellm.integrations.gcs_bucket.gcs_bucket_base import (
    GCSBucketBase,
    GCSLoggingConfig,
)
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
from litellm.types.llms.openai import CreateFileRequest, OpenAIFileObject
from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES

from .transformation import VertexAIJsonlFilesTransformation

vertex_ai_files_transformation = VertexAIJsonlFilesTransformation()


class VertexAIFilesHandler(GCSBucketBase):
    """
    Handles Calling VertexAI in OpenAI Files API format v1/files/*

    This implementation uploads files on GCS Buckets
    """

    def __init__(self):
        super().__init__()
        self.async_httpx_client = get_async_httpx_client(
            llm_provider=LlmProviders.VERTEX_AI,
        )

    async def async_create_file(
        self,
        create_file_data: CreateFileRequest,
        api_base: Optional[str],
        vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
        vertex_project: Optional[str],
        vertex_location: Optional[str],
        timeout: Union[float, httpx.Timeout],
        max_retries: Optional[int],
    ) -> OpenAIFileObject:
        gcs_logging_config: GCSLoggingConfig = await self.get_gcs_logging_config(
            kwargs={}
        )
        headers = await self.construct_request_headers(
            vertex_instance=gcs_logging_config["vertex_instance"],
            service_account_json=gcs_logging_config["path_service_account"],
        )
        bucket_name = gcs_logging_config["bucket_name"]
        (
            logging_payload,
            object_name,
        ) = vertex_ai_files_transformation.transform_openai_file_content_to_vertex_ai_file_content(
            openai_file_content=create_file_data.get("file")
        )
        gcs_upload_response = await self._log_json_data_on_gcs(
            headers=headers,
            bucket_name=bucket_name,
            object_name=object_name,
            logging_payload=logging_payload,
        )

        return vertex_ai_files_transformation.transform_gcs_bucket_response_to_openai_file_object(
            create_file_data=create_file_data,
            gcs_upload_response=gcs_upload_response,
        )

    def create_file(
        self,
        _is_async: bool,
        create_file_data: CreateFileRequest,
        api_base: Optional[str],
        vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
        vertex_project: Optional[str],
        vertex_location: Optional[str],
        timeout: Union[float, httpx.Timeout],
        max_retries: Optional[int],
    ) -> Union[OpenAIFileObject, Coroutine[Any, Any, OpenAIFileObject]]:
        """
        Creates a file on VertexAI GCS Bucket

        Only supported for Async litellm.acreate_file
        """

        if _is_async:
            return self.async_create_file(
                create_file_data=create_file_data,
                api_base=api_base,
                vertex_credentials=vertex_credentials,
                vertex_project=vertex_project,
                vertex_location=vertex_location,
                timeout=timeout,
                max_retries=max_retries,
            )
        else:
            return asyncio.run(
                self.async_create_file(
                    create_file_data=create_file_data,
                    api_base=api_base,
                    vertex_credentials=vertex_credentials,
                    vertex_project=vertex_project,
                    vertex_location=vertex_location,
                    timeout=timeout,
                    max_retries=max_retries,
                )
            )