# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo

import asyncio
import json
import os
import shutil
import tempfile
import time
from typing import Any, Dict, Optional

from fastapi import (
    APIRouter,
    File,
    Form,
    HTTPException,
    Path,
    Query,
    Request,
    UploadFile,
)
from fastapi.responses import FileResponse

from sglang.multimodal_gen.configs.sample.sampling_params import (
    SamplingParams,
    generate_request_id,
)
from sglang.multimodal_gen.runtime.entrypoints.openai.protocol import (
    VideoGenerationsRequest,
    VideoListResponse,
    VideoResponse,
)
from sglang.multimodal_gen.runtime.entrypoints.openai.storage import cloud_storage
from sglang.multimodal_gen.runtime.entrypoints.openai.stores import VIDEO_STORE
from sglang.multimodal_gen.runtime.entrypoints.openai.utils import (
    DEFAULT_FPS,
    DEFAULT_VIDEO_SECONDS,
    add_common_data_to_response,
    build_sampling_params,
    merge_image_input_list,
    process_generation_batch,
    save_image_to_path,
)
from sglang.multimodal_gen.runtime.entrypoints.utils import prepare_request
from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
from sglang.multimodal_gen.runtime.server_args import get_global_server_args
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

logger = init_logger(__name__)
router = APIRouter(prefix="/v1/videos", tags=["videos"])


def _build_video_sampling_params(request_id: str, request: VideoGenerationsRequest):
    """Resolve video-specific defaults (fps, seconds → num_frames) then
    delegate to the shared build_sampling_params."""
    seconds = request.seconds if request.seconds is not None else DEFAULT_VIDEO_SECONDS
    fps = request.fps if request.fps is not None else DEFAULT_FPS
    num_frames = request.num_frames if request.num_frames is not None else fps * seconds

    return build_sampling_params(
        request_id,
        prompt=request.prompt,
        size=request.size,
        num_frames=num_frames,
        fps=fps,
        image_path=request.input_reference,
        output_file_name=request_id,
        seed=request.seed,
        generator_device=request.generator_device,
        num_inference_steps=request.num_inference_steps,
        guidance_scale=request.guidance_scale,
        guidance_scale_2=request.guidance_scale_2,
        negative_prompt=request.negative_prompt,
        enable_teacache=request.enable_teacache,
        enable_frame_interpolation=request.enable_frame_interpolation,
        frame_interpolation_exp=request.frame_interpolation_exp,
        frame_interpolation_scale=request.frame_interpolation_scale,
        frame_interpolation_model_path=request.frame_interpolation_model_path,
        enable_upscaling=request.enable_upscaling,
        upscaling_model_path=request.upscaling_model_path,
        upscaling_scale=request.upscaling_scale,
        output_path=request.output_path,
        output_compression=request.output_compression,
        output_quality=request.output_quality,
        perf_dump_path=request.perf_dump_path,
    )


# extract metadata which http_server needs to know
def _video_job_from_sampling(
    request_id: str, req: VideoGenerationsRequest, sampling: SamplingParams
) -> Dict[str, Any]:
    size_str = f"{sampling.width}x{sampling.height}"
    seconds = int(round((sampling.num_frames or 0) / float(sampling.fps or 24)))
    return {
        "id": request_id,
        "object": "video",
        "model": req.model or "sora-2",
        "status": "queued",
        "progress": 0,
        "created_at": int(time.time()),
        "size": size_str,
        "seconds": str(seconds),
        "quality": "standard",
        "file_path": os.path.abspath(sampling.output_file_path()),
    }


async def _save_first_input_image(
    image_sources, request_id: str, uploads_dir: str
) -> str | None:
    """Save the first input image from a list of sources and return its path."""
    image_list = merge_image_input_list(image_sources)
    if not image_list:
        return None
    image = image_list[0]

    os.makedirs(uploads_dir, exist_ok=True)

    filename = image.filename if hasattr(image, "filename") else "url_image"
    target_path = os.path.join(uploads_dir, f"{request_id}_{filename}")
    return await save_image_to_path(image, target_path)


async def _dispatch_job_async(
    job_id: str,
    batch: Req,
    *,
    temp_dirs: list[str] | None = None,
    output_persistent: bool = True,
) -> None:
    from sglang.multimodal_gen.runtime.scheduler_client import async_scheduler_client

    try:
        save_file_path_list, result = await process_generation_batch(
            async_scheduler_client, batch
        )
        save_file_path = save_file_path_list[0]

        cloud_url = await cloud_storage.upload_and_cleanup(save_file_path)

        persistent_path = (
            save_file_path if not cloud_url and output_persistent else None
        )
        update_fields = {
            "status": "completed",
            "progress": 100,
            "completed_at": int(time.time()),
            "url": cloud_url,
            "file_path": persistent_path,
        }
        update_fields = add_common_data_to_response(
            update_fields, request_id=job_id, result=result
        )
        await VIDEO_STORE.update_fields(job_id, update_fields)
    except Exception as e:
        logger.error(f"{e}")
        await VIDEO_STORE.update_fields(
            job_id, {"status": "failed", "error": {"message": str(e)}}
        )
    finally:
        for td in temp_dirs or []:
            shutil.rmtree(td, ignore_errors=True)


# TODO: support image to video generation
@router.post("", response_model=VideoResponse)
async def create_video(
    request: Request,
    # multipart/form-data fields (optional; used only when content-type is multipart)
    prompt: Optional[str] = Form(None),
    input_reference: Optional[UploadFile] = File(None),
    reference_url: Optional[str] = Form(None),
    model: Optional[str] = Form(None),
    seconds: Optional[int] = Form(None),
    size: Optional[str] = Form(None),
    fps: Optional[int] = Form(None),
    num_frames: Optional[int] = Form(None),
    seed: Optional[int] = Form(1024),
    generator_device: Optional[str] = Form("cuda"),
    negative_prompt: Optional[str] = Form(None),
    guidance_scale: Optional[float] = Form(None),
    num_inference_steps: Optional[int] = Form(None),
    enable_teacache: Optional[bool] = Form(False),
    enable_frame_interpolation: Optional[bool] = Form(False),
    frame_interpolation_exp: Optional[int] = Form(1),
    frame_interpolation_scale: Optional[float] = Form(1.0),
    frame_interpolation_model_path: Optional[str] = Form(None),
    enable_upscaling: Optional[bool] = Form(False),
    upscaling_model_path: Optional[str] = Form(None),
    upscaling_scale: Optional[int] = Form(4),
    output_quality: Optional[str] = Form("default"),
    output_compression: Optional[int] = Form(None),
    extra_body: Optional[str] = Form(None),
):
    content_type = request.headers.get("content-type", "").lower()
    request_id = generate_request_id()

    server_args = get_global_server_args()
    task_type = server_args.pipeline_config.task_type

    # Resolve input upload directory (may be a temp dir when saving is disabled)
    temp_dirs: list[str] = []
    if server_args.input_save_path is not None:
        uploads_dir = server_args.input_save_path
        os.makedirs(uploads_dir, exist_ok=True)
    else:
        uploads_dir = tempfile.mkdtemp(prefix="sglang_input_")
        temp_dirs.append(uploads_dir)

    # Resolve output directory
    effective_output_path = server_args.output_path
    output_persistent = True
    if "multipart/form-data" not in content_type:
        # JSON body may carry a per-request output_path; checked after parsing below
        pass

    if "multipart/form-data" in content_type:
        if not prompt:
            raise HTTPException(status_code=400, detail="prompt is required")
        # Validate image input based on model task type
        image_sources = merge_image_input_list(input_reference, reference_url)
        if task_type.requires_image_input() and not image_sources:
            raise HTTPException(
                status_code=400,
                detail="input_reference or reference_url is required for image-to-video generation",
            )
        try:
            input_path = await _save_first_input_image(
                image_sources, request_id, uploads_dir
            )
        except Exception as e:
            raise HTTPException(
                status_code=400, detail=f"Failed to process image source: {str(e)}"
            )

        # Parse extra_body JSON (if provided in multipart form) to get fps/num_frames overrides
        extra_from_form: Dict[str, Any] = {}
        if extra_body:
            try:
                extra_from_form = json.loads(extra_body)
            except Exception:
                extra_from_form = {}

        fps_val = fps if fps is not None else extra_from_form.get("fps")
        num_frames_val = (
            num_frames if num_frames is not None else extra_from_form.get("num_frames")
        )

        req = VideoGenerationsRequest(
            prompt=prompt,
            input_reference=input_path,
            model=model,
            seconds=seconds if seconds is not None else 4,
            size=size,
            fps=fps_val,
            num_frames=num_frames_val,
            seed=seed,
            generator_device=generator_device,
            negative_prompt=negative_prompt,
            num_inference_steps=num_inference_steps,
            enable_teacache=enable_teacache,
            enable_frame_interpolation=enable_frame_interpolation,
            frame_interpolation_exp=frame_interpolation_exp,
            frame_interpolation_scale=frame_interpolation_scale,
            frame_interpolation_model_path=frame_interpolation_model_path,
            enable_upscaling=enable_upscaling,
            upscaling_model_path=upscaling_model_path,
            upscaling_scale=upscaling_scale,
            output_compression=output_compression,
            output_quality=output_quality,
            **(
                {"guidance_scale": guidance_scale} if guidance_scale is not None else {}
            ),
        )
    else:
        try:
            body = await request.json()
        except Exception:
            body = {}
        try:
            # If client uses extra_body, merge it into the top-level payload
            payload: Dict[str, Any] = dict(body or {})
            extra = payload.pop("extra_body", None)
            if isinstance(extra, dict):
                # Shallow-merge: only keys like fps/num_frames are expected
                payload.update(extra)
            # openai may turn extra_body to extra_json
            extra_json = payload.pop("extra_json", None)
            if isinstance(extra_json, dict):
                payload.update(extra_json)
            # Validate image input based on model task type
            has_image_input = payload.get("reference_url") or payload.get(
                "input_reference"
            )
            if task_type.requires_image_input() and not has_image_input:
                raise HTTPException(
                    status_code=400,
                    detail="input_reference or reference_url is required for image-to-video generation",
                )
            # for non-multipart/form-data type
            if payload.get("reference_url"):
                try:
                    input_path = await _save_first_input_image(
                        payload.get("reference_url"), request_id, uploads_dir
                    )
                except Exception as e:
                    raise HTTPException(
                        status_code=400,
                        detail=f"Failed to process image source: {str(e)}",
                    )
                payload["input_reference"] = input_path
            req = VideoGenerationsRequest(**payload)
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"Invalid request body: {e}")

    # Resolve per-request output_path override
    effective_output_path = req.output_path or server_args.output_path
    if effective_output_path is None:
        output_tmp = tempfile.mkdtemp(prefix="sglang_output_")
        temp_dirs.append(output_tmp)
        effective_output_path = output_tmp
        output_persistent = False

    # Inject resolved output_path so _build_video_sampling_params picks it up
    req.output_path = effective_output_path

    logger.debug(f"Server received from create_video endpoint: req={req}")

    try:
        sampling_params = _build_video_sampling_params(request_id, req)
    except (ValueError, TypeError) as e:
        raise HTTPException(status_code=400, detail=str(e))

    job = _video_job_from_sampling(request_id, req, sampling_params)
    await VIDEO_STORE.upsert(request_id, job)

    # Build Req for scheduler
    batch = prepare_request(
        server_args=server_args,
        sampling_params=sampling_params,
    )
    # Add diffusers_kwargs if provided
    if req.diffusers_kwargs:
        batch.extra["diffusers_kwargs"] = req.diffusers_kwargs
    # Enqueue the job asynchronously and return immediately
    asyncio.create_task(
        _dispatch_job_async(
            request_id,
            batch,
            temp_dirs=temp_dirs or None,
            output_persistent=output_persistent,
        )
    )
    return VideoResponse(**job)


@router.get("", response_model=VideoListResponse)
async def list_videos(
    after: Optional[str] = Query(None),
    limit: Optional[int] = Query(None, ge=1, le=100),
    order: Optional[str] = Query("desc"),
):
    # Normalize order
    order = (order or "desc").lower()
    if order not in ("asc", "desc"):
        order = "desc"
    jobs = await VIDEO_STORE.list_values()

    reverse = order != "asc"
    jobs.sort(key=lambda j: j.get("created_at", 0), reverse=reverse)

    if after is not None:
        try:
            idx = next(i for i, j in enumerate(jobs) if j["id"] == after)
            jobs = jobs[idx + 1 :]
        except StopIteration:
            jobs = []

    if limit is not None:
        jobs = jobs[:limit]
    items = [VideoResponse(**j) for j in jobs]
    return VideoListResponse(data=items)


@router.get("/{video_id}", response_model=VideoResponse)
async def retrieve_video(video_id: str = Path(...)):
    job = await VIDEO_STORE.get(video_id)
    if not job:
        raise HTTPException(status_code=404, detail="Video not found")
    return VideoResponse(**job)


# TODO: support aborting a job.
@router.delete("/{video_id}", response_model=VideoResponse)
async def delete_video(video_id: str = Path(...)):
    job = await VIDEO_STORE.pop(video_id)
    if not job:
        raise HTTPException(status_code=404, detail="Video not found")
    # Mark as deleted in response semantics
    job["status"] = "deleted"
    return VideoResponse(**job)


@router.get("/{video_id}/content")
async def download_video_content(
    video_id: str = Path(...), variant: Optional[str] = Query(None)
):
    job = await VIDEO_STORE.get(video_id)
    if not job:
        raise HTTPException(status_code=404, detail="Video not found")

    if job.get("url"):
        raise HTTPException(
            status_code=400,
            detail=f"Video has been uploaded to cloud storage. Please use the cloud URL: {job.get('url')}",
        )

    file_path = job.get("file_path")
    if not file_path or not os.path.exists(file_path):
        raise HTTPException(status_code=404, detail="Generation is still in-progress")

    media_type = "video/mp4"  # default variant
    return FileResponse(
        path=file_path, media_type=media_type, filename=os.path.basename(file_path)
    )
