# SPDX-FileCopyrightText: Copyright (c) 2025-2026 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 argparse
import asyncio
import logging
import os
import signal
import sys
from typing import AsyncIterator, Tuple

import torch
import uvloop
from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.utils.argparse_utils import FlexibleArgumentParser

import dynamo.nixl_connect as connect
from dynamo.runtime import Client, DistributedRuntime, dynamo_worker
from dynamo.runtime.logging import configure_dynamo_logging

sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))
from utils.args import Config, base_parse_args, parse_endpoint
from utils.audio_loader import AudioLoader
from utils.protocol import MyRequestOutput, vLLMMultimodalRequest

configure_dynamo_logging()
logger = logging.getLogger(__name__)

try:
    import cupy as array_module

    if not array_module.cuda.is_available():
        raise ImportError("CUDA is not available.")
    DEVICE = "cuda"
    logger.info("Using cupy for array operations (GPU mode).")
except ImportError as e:
    logger.warning(f"Failed to import cupy, falling back to numpy: {e}.")
    import numpy as array_module

    DEVICE = "cpu"

CACHE_SIZE_MAXIMUM = 8


class VllmEncodeWorker:
    def __init__(
        self,
        args: argparse.Namespace,
        engine_args: AsyncEngineArgs,
        pd_worker_client: Client,
    ) -> None:
        self.pd_worker_client = pd_worker_client
        self.engine_args = engine_args
        self.model = self.engine_args.model

        self.audio_loader = AudioLoader(cache_size=CACHE_SIZE_MAXIMUM)
        self.audio_processor = AutoProcessor.from_pretrained(
            self.model, trust_remote_code=True
        )
        self.audio_model = Qwen2AudioForConditionalGeneration.from_pretrained(
            self.model, device_map="auto", dtype=torch.float16
        ).eval()

    def get_audio_embeddings(self, audio_features):
        input_features, feature_attention_mask = (
            audio_features.input_features,
            audio_features.feature_attention_mask,
        )
        with torch.no_grad():
            (
                audio_feat_lengths,
                audio_output_lengths,
            ) = self.audio_model.audio_tower._get_feat_extract_output_lengths(
                feature_attention_mask.sum(-1)
            )
            batch_size, _, max_mel_seq_len = input_features.shape
            max_seq_len = (max_mel_seq_len - 2) // 2 + 1
            # Create a sequence tensor of shape (batch_size, max_seq_len)
            seq_range = (
                torch.arange(
                    0,
                    max_seq_len,
                    dtype=audio_feat_lengths.dtype,
                    device=audio_feat_lengths.device,
                )
                .unsqueeze(0)
                .expand(batch_size, max_seq_len)
            )
            lengths_expand = audio_feat_lengths.unsqueeze(1).expand(
                batch_size, max_seq_len
            )
            # Create mask
            padding_mask = seq_range >= lengths_expand

            audio_attention_mask_ = padding_mask.view(
                batch_size, 1, 1, max_seq_len
            ).expand(batch_size, 1, max_seq_len, max_seq_len)
            audio_attention_mask = audio_attention_mask_.to(
                dtype=self.audio_model.audio_tower.conv1.weight.dtype,
                device=self.audio_model.audio_tower.conv1.weight.device,
            )
            audio_attention_mask[audio_attention_mask_] = float("-inf")

            audio_outputs = self.audio_model.audio_tower(
                input_features, attention_mask=audio_attention_mask
            )
            selected_audio_feature = audio_outputs.last_hidden_state
            audio_features = self.audio_model.multi_modal_projector(
                selected_audio_feature
            )

            num_audios, max_audio_tokens, embed_dim = audio_features.shape
            audio_features_mask = torch.arange(
                max_audio_tokens, device=audio_output_lengths.device
            )[None, :]
            audio_features_mask = audio_features_mask < audio_output_lengths[:, None]
            audio_features = audio_features[audio_features_mask]

            return audio_features

    def cleanup(self):
        pass

    async def generate(
        self, request: vLLMMultimodalRequest
    ) -> AsyncIterator[MyRequestOutput]:
        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 audio and provided useful embeddings.
        # 1. Open the audio from the provided URL.
        # 2. Process the audio using the audio processor.
        # 3. Run the audio through the audio model's audio tower.
        # 4. Run the results of the audio 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:
            audio, sr = await self.audio_loader.load_audio(
                request.multimodal_input.audio_url
            )

            audio_features = self.audio_processor(
                text="test<|AUDIO|>", audio=audio, return_tensors="pt", padding=False
            )
            with torch.no_grad():
                audio_embeddings = self.get_audio_embeddings(audio_features)
            descriptor = connect.Descriptor(audio_embeddings)
            with await self._connector.create_readable(descriptor) as readable:
                request.serialized_request = readable.metadata()
                # Clear the audio URL as hint that the audio is passed as embeddings.
                request.multimodal_input.audio_url = None
                request.embeddings_shape = tuple(audio_embeddings.shape)
                logger.debug(f"Request: {request.model_dump_json()}")

                response_generator = await self.pd_worker_client.round_robin(
                    request.model_dump_json()
                )

                await readable.wait_for_completion()

                async for response in response_generator:
                    output = MyRequestOutput.model_validate_json(response.data())
                    yield MyRequestOutput(
                        request_id=output.request_id,
                        prompt=output.prompt,
                        prompt_token_ids=output.prompt_token_ids,
                        prompt_logprobs=output.prompt_logprobs,
                        outputs=output.outputs,
                        finished=output.finished,
                    ).model_dump_json()

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

    async def async_init(self, runtime: DistributedRuntime):
        logger.info("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("Startup completed.")

    @classmethod
    def parse_args(cls) -> Tuple[argparse.Namespace, Config]:
        DYN_NAMESPACE = os.environ.get("DYN_NAMESPACE", "dynamo")
        DEFAULT_ENDPOINT = f"dyn://{DYN_NAMESPACE}.encoder.generate"
        DEFAULT_DOWNSTREAM_ENDPOINT = f"dyn://{DYN_NAMESPACE}.llm.generate"

        parser = FlexibleArgumentParser(
            description="vLLM based encoder for Dynamo LLM."
        )
        parser.add_argument(
            "--endpoint",
            type=str,
            default=DEFAULT_ENDPOINT,
            help=f"Dynamo endpoint string in 'dyn://namespace.component.endpoint' format. Default: '{DEFAULT_ENDPOINT}'",
        )
        parser.add_argument(
            "--downstream-endpoint",
            type=str,
            default=DEFAULT_DOWNSTREAM_ENDPOINT,
            help=f"The endpoint string of the downstream LLM in 'dyn://namespace.component.endpoint' format. Default: '{DEFAULT_DOWNSTREAM_ENDPOINT}'",
        )

        args, config = base_parse_args(parser)

        return args, config


async def graceful_shutdown(runtime):
    """
    By calling `runtime.shutdown()`, the endpoints will immediately be unavailable.
    However, in-flight requests will still be processed until they are finished.
    After all in-flight requests are finished, the `serve_endpoint` functions will return
    and the engine will be shutdown by Python's garbage collector.
    """
    logging.info("Received shutdown signal, shutting down DistributedRuntime")
    runtime.shutdown()
    logging.info("DistributedRuntime shutdown complete")


@dynamo_worker()
async def worker(runtime: DistributedRuntime):
    # Runtime setup
    # Set up signal handler for graceful shutdown
    loop = asyncio.get_running_loop()

    def signal_handler():
        asyncio.create_task(graceful_shutdown(runtime))

    for sig in (signal.SIGTERM, signal.SIGINT):
        loop.add_signal_handler(sig, signal_handler)

    logging.info("Signal handlers set up for graceful shutdown")

    # worker setup
    args, config = VllmEncodeWorker.parse_args()
    await init(runtime, args, config)


async def init(runtime: DistributedRuntime, args: argparse.Namespace, config: Config):
    """
    Instantiate and serve
    """

    generate_endpoint = runtime.endpoint(
        f"{config.namespace}.{config.component}.{config.endpoint}"
    )

    parsed_namespace, parsed_component_name, parsed_endpoint_name = parse_endpoint(
        args.downstream_endpoint
    )
    pd_worker_client = await runtime.endpoint(
        f"{parsed_namespace}.{parsed_component_name}.{parsed_endpoint_name}"
    ).client()

    handler = VllmEncodeWorker(args, config.engine_args, pd_worker_client)
    await handler.async_init(runtime)

    logger.info("Waiting for PD Worker Instances ...")
    await pd_worker_client.wait_for_instances()

    logger.info(f"Starting to serve the {args.endpoint} endpoint...")

    try:
        await asyncio.gather(
            generate_endpoint.serve_endpoint(
                handler.generate, metrics_labels=[("model", config.model)]
            ),
        )
    except Exception as e:
        logger.error(f"Failed to serve endpoints: {e}")
        raise
    finally:
        handler.cleanup()


if __name__ == "__main__":
    uvloop.install()
    asyncio.run(worker())
