# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

import asyncio
import logging
import os
import time
from dataclasses import dataclass
from typing import AsyncIterator

import torch
from transformers import AutoImageProcessor
from vllm.engine.arg_utils import AsyncEngineArgs

import dynamo.nixl_connect as connect
from dynamo.common.multimodal import LocalEmbeddingSender, NixlPersistentEmbeddingSender
from dynamo.runtime import DistributedRuntime

from ..multimodal_utils import (
    ImageLoader,
    encode_image_embeddings,
    get_encoder_components,
    load_vision_model,
    vLLMMultimodalRequest,
)
from ..multimodal_utils.embedding_cache import EmbeddingCache
from ..multimodal_utils.model import is_qwen_vl_model

logger = logging.getLogger(__name__)

CACHE_SIZE_MAXIMUM = 8

# Both embedding transmitter suffers from increasing latency as
# number of concurrent requests increases, NixlPersistentEmbedding transmitters
# scale worse than local. Need to investigate why.
TRANSFER_LOCAL = int(os.getenv("TRANSFER_LOCAL", 1))
# [gluo NOTE] default off to benchmark standalone encoder
ENABLE_ENCODER_CACHE = int(os.getenv("ENABLE_ENCODER_CACHE", 1))


@dataclass
class EmbeddingItem:
    key: str
    image_grid_thw: list
    embeddings: torch.Tensor


class EncodeWorkerHandler:
    def __init__(
        self,
        engine_args: AsyncEngineArgs,
    ) -> None:
        self.engine_args = engine_args
        self.model = self.engine_args.model

        self.image_loader = ImageLoader(cache_size=CACHE_SIZE_MAXIMUM)
        self.image_processor = AutoImageProcessor.from_pretrained(
            self.model, trust_remote_code=True
        )
        self.vision_model = load_vision_model(self.model)
        logger.debug(f"embedding hidden dim: {self.vision_model.out_hidden_size}")
        self.min_workers = 1

        # Get encoder components for the model
        self.vision_encoder, self.projector = get_encoder_components(
            self.model, self.vision_model
        )
        self._connector: connect.Connector | None = None
        self._accumulated_time = 0.0
        self._processed_requests = 0
        self.readables = []
        self.embedding_cache = EmbeddingCache() if ENABLE_ENCODER_CACHE else None
        self.embedding_sender = (
            LocalEmbeddingSender()
            if TRANSFER_LOCAL
            else NixlPersistentEmbeddingSender()
        )
        self.send_complete_queue = asyncio.Queue()
        self.send_complete_checker_task = asyncio.create_task(
            self.check_complete(self.send_complete_queue)
        )

    async def check_complete(self, queue):
        while True:
            transfer_future, embedding = await queue.get()
            if transfer_future is None:  # Sentinel value to stop the checker
                queue.task_done()
                break
            await transfer_future
            queue.task_done()

    def cleanup(self):
        self.send_complete_queue.put_nowait(
            (None, None)
        )  # Send sentinel value to stop the checker

    async def async_init(self, runtime: DistributedRuntime):
        """Initialize the connector for RDMA transfers"""
        logger.info("Encode worker startup started.")
        # Create and initialize a dynamo connector for this worker.
        # We'll needs this to move data between this worker and remote workers efficiently.
        self._connector = connect.Connector()
        logger.info("Encode worker startup completed.")

    async def generate(
        self, request: vLLMMultimodalRequest, context
    ) -> AsyncIterator[str]:
        logger.debug(f"Got raw request: {request}")
        if not isinstance(request, vLLMMultimodalRequest):
            if isinstance(request, str):
                request = vLLMMultimodalRequest.model_validate_json(request)
            else:
                request = vLLMMultimodalRequest.model_validate(request)
        logger.debug(f"Received encode request: {{ id: {request.request_id} }}.")

        request_id = request.request_id

        # The following steps encode the requested image and provided useful embeddings.
        # 1. Open the image from the provided URL.
        # 2. Process the image using the image processor.
        # 3. Run the image through the vision model's vision tower.
        # 4. Run the results of the vision tower through the multi-modal projector.
        # 5. Create a descriptor for the embeddings.
        # 6. Create a write operation using the serialized request and the descriptor.
        # 7. Await for the write operation to complete.
        # 8. Yield the encode response.

        try:
            time_start = time.perf_counter()
            # Before batch process images, check cache first
            need_encode_indexes = []
            embedding_lists = [None] * len(request.multimodal_inputs)
            for idx in range(len(request.multimodal_inputs)):
                if not request.multimodal_inputs[idx].multimodal_input.image_url:
                    raise ValueError("image_url is required for the encode worker.")

                image_url = request.multimodal_inputs[idx].multimodal_input.image_url
                # see if we have local cache
                embedding_key = EmbeddingCache.generate_hash_key(image_url)
                if self.embedding_cache is not None and self.embedding_cache.has_key(
                    embedding_key
                ):
                    (image_grid_thw, embeddings) = self.embedding_cache.get(
                        embedding_key
                    )
                    embedding_lists[idx] = EmbeddingItem(
                        embedding_key, image_grid_thw, embeddings
                    )
                # compute
                else:
                    # keep track of key to avoid recompute of it
                    need_encode_indexes.append((idx, embedding_key))

            # Load and generate image tensors
            image_tasks = []
            image_to_load = []
            for idx, _ in need_encode_indexes:
                url = request.multimodal_inputs[idx].multimodal_input.image_url
                image_tasks.append(
                    asyncio.create_task(self.image_loader.load_image(url))
                )
                image_to_load.append(url)
            results = await asyncio.gather(*image_tasks, return_exceptions=True)
            loaded_images = []
            collective_exceptions = ""
            for i, result in enumerate(results):
                if isinstance(result, Exception):
                    url = image_to_load[i]
                    logger.error(f"Failed to load image from {url[:80]}...: {result}")
                    collective_exceptions += (
                        f"Failed to load image from {url[:80]}...: {result}\n"
                    )
                    continue
                loaded_images.append(result)
            if collective_exceptions:
                raise ValueError(
                    f"Errors occurred during image loading:\n{collective_exceptions}"
                )

            if loaded_images:
                image_embeds = await asyncio.to_thread(
                    self.image_processor, images=loaded_images, return_tensors="pt"
                )

                # Encode the image embeddings using model-specific encoder
                embeddings = await asyncio.to_thread(
                    encode_image_embeddings,
                    model_name=self.model,
                    image_embeds=image_embeds,
                    vision_encoder=self.vision_encoder,
                    projector=self.projector,
                )

                # [gluo FIXME] This is specific to qwen vision processing..
                # Split concatenated embeddings for each image item.
                if is_qwen_vl_model(self.model):
                    merge_size = self.vision_encoder.spatial_merge_size
                    sizes = (
                        image_embeds["image_grid_thw"].prod(-1)
                        // merge_size
                        // merge_size
                    ).tolist()
                    splitted_embeddings = embeddings.squeeze(0).split(sizes)
                    logger.debug(
                        f"Splitted embeddings lengths: {[e.shape for e in splitted_embeddings]}"
                    )
                else:
                    # Validated on llava (NOTE need to double check on other models) that the
                    # embeddings already has batch dimension for images, so we can directly
                    # split by batch dimension
                    logger.debug(f"image embedding shape: {embeddings.shape}")
                    splitted_embeddings = embeddings

                image_grid_thw = (
                    image_embeds["image_grid_thw"].tolist()
                    if "image_grid_thw" in image_embeds
                    else None
                )

            # fill in the embedding_lists with new computed embeddings and cache them
            for split_idx, (list_idx, key) in enumerate(need_encode_indexes):
                embedding_lists[list_idx] = EmbeddingItem(
                    key,
                    [image_grid_thw[split_idx]] if image_grid_thw else None,
                    splitted_embeddings[split_idx].unsqueeze(0),
                )
                # Cache the computed value for future use
                if self.embedding_cache is not None:
                    self.embedding_cache.set(
                        embedding_lists[list_idx].key,
                        (
                            embedding_lists[list_idx].image_grid_thw,
                            embedding_lists[list_idx].embeddings,
                        ),
                    )

            before_transfer_time = time.perf_counter()

            # Prepare transfer
            send_tasks = [
                asyncio.create_task(
                    self.embedding_sender.send_embeddings(
                        embedding_item.embeddings, stage_embeddings=True
                    )
                )
                for embedding_item in embedding_lists
            ]
            transfer_requests = await asyncio.gather(*send_tasks)

            after_transfer_time = time.perf_counter()

            for idx, item in enumerate(zip(embedding_lists, transfer_requests)):
                embedding_item, transfer_request = item
                logger.debug(
                    f"{embedding_item.embeddings.shape} prepared for transfer."
                )
                # Update request for transfer metadata
                request.multimodal_inputs[idx].multimodal_input.image_url = None
                request.multimodal_inputs[
                    idx
                ].image_grid_thw = embedding_item.image_grid_thw
                request.multimodal_inputs[idx].embeddings_shape = tuple(
                    embedding_item.embeddings.shape
                )
                request.multimodal_inputs[idx].serialized_request = transfer_request[0]

                # Keep a reference of the embedding and only drop reference when the transfer is done
                self.send_complete_queue.put_nowait(
                    (transfer_request[1], embedding_item.embeddings)
                )

            logger.debug(f"Request: {request.model_dump_json()}")

            time_end = time.perf_counter()
            self._accumulated_time += time_end - time_start
            self._processed_requests += 1
            logger.debug(
                f"received request {{ id: {request_id} }} at time {time_start:.4f}, processed in {time_end - time_start:.4f} seconds, break down: image loading and encoding time {(before_transfer_time - time_start):.4f} seconds, transfer preparation time {(after_transfer_time - before_transfer_time):.4f} seconds, after transfer time {(time_end - after_transfer_time):.4f} seconds."
            )
            logger.debug(
                f"Encoded image(s) for request {{ id: {request_id} }} in {time_end - time_start:.4f} seconds. "
                f"Average encoding time: {self._accumulated_time / self._processed_requests:.4f} seconds over {self._processed_requests} requests."
            )

            # Yield transformed request back
            yield request.model_dump_json()

        except Exception as e:
            logger.error(f"Error processing request {request_id}: {e}")
            raise
