# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
from io import BytesIO
from typing import AsyncIterator

import requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, LlavaForConditionalGeneration
from utils.protocol import EncodeRequest, EncodeResponse
from utils.vllm import parse_vllm_args

from dynamo.sdk import dynamo_endpoint, service

logger = logging.getLogger(__name__)


@service(
    dynamo={
        "enabled": True,
        "namespace": "dynamo",
    },
    resources={"gpu": 1, "cpu": "10", "memory": "20Gi"},
    workers=1,
)
class EncodeWorker:
    def __init__(self) -> None:
        class_name = self.__class__.__name__
        self.engine_args = parse_vllm_args(class_name, "")
        self.MODEL_ID = self.engine_args.model

        self.image_processor = AutoImageProcessor.from_pretrained(
            self.MODEL_ID, trust_remote_code=True
        )

        self.vision_model = LlavaForConditionalGeneration.from_pretrained(
            self.MODEL_ID, device_map="auto", torch_dtype=torch.float16
        ).eval()

    @dynamo_endpoint()
    async def encode(self, request: EncodeRequest) -> AsyncIterator[EncodeResponse]:
        image = self.open_image(request.image_url)
        image_embeds = self.image_processor(images=image, return_tensors="pt")

        with torch.no_grad():
            logger.debug(f"Vision model device: {self.vision_model.device}")
            vision_outputs = self.vision_model.vision_tower(
                image_embeds["pixel_values"].to(self.vision_model.device)
            )

            image_features = vision_outputs.last_hidden_state
            image_features = self.vision_model.multi_modal_projector(image_features)
            yield EncodeResponse(
                image_features=image_features.tolist()
            ).model_dump_json()

    def open_image(self, image: str) -> Image.Image:
        # TODO: Have a seperate field for url and non url - and avoid auto detection
        try:
            if image.startswith("http") or image.startswith("https"):
                response = requests.get(image)
                image_data = Image.open(BytesIO(response.content)).convert("RGB")
            else:
                image_data = Image.open(image).convert("RGB")
        except Exception as e:
            logger.error(f"Error opening image: {e}")
            raise e
        return image_data
