# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2026 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""Inference-only Qwen3-ASR model."""

from collections.abc import Iterable, Mapping, Sequence
from typing import Any, Literal, cast

import numpy as np
import torch
import torch.nn as nn
from transformers.feature_extraction_utils import BatchFeature
from transformers.models.whisper import WhisperFeatureExtractor

from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.inputs.data import PromptType
from vllm.logger import init_logger
from vllm.model_executor.models.interfaces import (
    MultiModalEmbeddings,
    SupportsMRoPE,
    SupportsMultiModal,
    SupportsPP,
    SupportsTranscription,
)
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.models.qwen3 import Qwen3ForCausalLM
from vllm.model_executor.models.qwen3_omni_moe_thinker import (
    Qwen2_5OmniAudioFeatureInputs,
    Qwen3OmniMoeAudioEncoder,
    Qwen3OmniMoeThinkerMultiModalProcessor,
)
from vllm.model_executor.models.utils import (
    AutoWeightsLoader,
    WeightsMapper,
    _merge_multimodal_embeddings,
    maybe_prefix,
)
from vllm.model_executor.models.whisper import ISO639_1_SUPPORTED_LANGS
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (
    AudioItem,
    ModalityData,
    MultiModalDataDict,
    MultiModalFeatureSpec,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
)
from vllm.multimodal.parse import (
    AudioProcessorItems,
    DictEmbeddingItems,
    ModalityDataItems,
    MultiModalDataItems,
    MultiModalDataParser,
)
from vllm.multimodal.processing import (
    BaseDummyInputsBuilder,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
)
from vllm.sequence import IntermediateTensors
from vllm.tokenizers import cached_tokenizer_from_config
from vllm.transformers_utils.configs.qwen3_asr import (
    Qwen3ASRConfig,
    Qwen3ASRThinkerConfig,
)
from vllm.transformers_utils.processor import cached_processor_from_config
from vllm.transformers_utils.processors.qwen3_asr import (
    Qwen3ASRProcessor,
)

logger = init_logger(__name__)


def _get_feat_extract_output_lengths(input_lengths: torch.Tensor):
    input_lengths_leave = input_lengths % 100
    feat_lengths = (input_lengths_leave - 1) // 2 + 1
    output_lengths = (
        ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
    )
    return output_lengths


class Qwen3ASRProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
        return self.ctx.get_hf_config(Qwen3ASRConfig).thinker_config

    def get_hf_processor(self, **kwargs: object) -> Qwen3ASRProcessor:
        processor = self.ctx.get_hf_processor(
            Qwen3ASRProcessor,
            use_fast=kwargs.pop("use_fast", True),
            **kwargs,
        )
        if not hasattr(processor, "audio_token"):
            processor.audio_token = "<|audio_pad|>"
        return processor

    def get_feature_extractor(self, **kwargs: object) -> WhisperFeatureExtractor:
        hf_processor = self.get_hf_processor(**kwargs)
        feature_extractor = hf_processor.feature_extractor
        assert isinstance(feature_extractor, WhisperFeatureExtractor)
        return feature_extractor

    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
        return {"audio": None}


class Qwen3ASRDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3ASRProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_audios = mm_counts.get("audio", 0)

        hf_processor = self.info.get_hf_processor()
        audio_token = hf_processor.audio_token

        return audio_token * num_audios

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions] | None = None,
    ) -> MultiModalDataDict:
        num_audios = mm_counts.get("audio", 0)

        feature_extractor = self.info.get_feature_extractor()

        target_audio_length = (
            min(
                feature_extractor.chunk_length,
                30,
            )
            * feature_extractor.sampling_rate
        )

        audio_overrides = mm_options.get("audio") if mm_options else None

        return {
            "audio": self._get_dummy_audios(
                length=target_audio_length,
                num_audios=num_audios,
                overrides=audio_overrides,
            ),
        }


def _qwen3asr_field_config(hf_inputs: Mapping[str, torch.Tensor]):
    audio_feature_lengths = hf_inputs.get("audio_feature_lengths", torch.empty((0,)))
    return dict(
        input_audio_features=MultiModalFieldConfig.flat_from_sizes(
            "audio", audio_feature_lengths, dim=1
        ),
        feature_attention_mask=MultiModalFieldConfig.batched("audio"),
        audio_feature_lengths=MultiModalFieldConfig.batched("audio"),
    )


class Qwen3ASRMultiModalDataParser(MultiModalDataParser):
    def _parse_audio_data(
        self,
        data: dict[str, torch.Tensor] | ModalityData[AudioItem],
    ) -> ModalityDataItems[Any, Any] | None:
        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="audio",
                required_fields={"input_audio_features", "audio_feature_lengths"},
                fields_factory=_qwen3asr_field_config,
            )

        return super()._parse_audio_data(data)


class Qwen3ASRMultiModalProcessor(
    Qwen3OmniMoeThinkerMultiModalProcessor,
):
    def _get_data_parser(self) -> MultiModalDataParser:
        feature_extractor = self.info.get_feature_extractor()
        return Qwen3ASRMultiModalDataParser(
            target_sr=feature_extractor.sampling_rate,
        )

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _qwen3asr_field_config(hf_inputs)

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()

        audio_token = processor.audio_token
        audio_token_id = vocab[audio_token]

        out_mm_data = out_mm_kwargs.get_data()
        audio_feature_lengths = out_mm_data.get("audio_feature_lengths")
        feature_attention_mask = out_mm_data.get("feature_attention_mask")
        if audio_feature_lengths is None and feature_attention_mask is None:
            audio_output_lengths = []
        elif audio_feature_lengths is not None:
            audio_output_lens = _get_feat_extract_output_lengths(audio_feature_lengths)
            audio_output_lengths = audio_output_lens.tolist()
        elif feature_attention_mask is not None:
            assert isinstance(feature_attention_mask, torch.Tensor)
            audio_output_lens = _get_feat_extract_output_lengths(
                feature_attention_mask.sum(-1)
            )
            audio_output_lengths = audio_output_lens.tolist()

        def get_replacement_qwen2_audio(item_idx: int):
            num_features = audio_output_lengths[item_idx]
            if num_features == 0:
                audios = mm_items.get_items("audio", AudioProcessorItems)
                audio = audios.get(item_idx)
                raise ValueError(
                    f"The audio {audio} (len={len(audio)}) is too short "
                    "to be represented inside the model"
                )

            return [audio_token_id] * num_features

        return [
            PromptReplacement(
                modality="audio",
                target=audio_token,
                replacement=get_replacement_qwen2_audio,
            ),
        ]


@MULTIMODAL_REGISTRY.register_processor(
    Qwen3ASRMultiModalProcessor,
    info=Qwen3ASRProcessingInfo,
    dummy_inputs=Qwen3ASRDummyInputsBuilder,
)
class Qwen3ASRForConditionalGeneration(
    nn.Module,
    SupportsMultiModal,
    SupportsPP,
    SupportsMRoPE,
    SupportsTranscription,
):
    supported_languages = ISO639_1_SUPPORTED_LANGS

    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "thinker.lm_head.": "language_model.lm_head.",
            "thinker.model.": "language_model.model.",
            "thinker.": "",
        }
    )

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("audio"):
            return "<|audio_start|><|audio_pad|><|audio_end|>"

        raise ValueError("Only audio modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        self.vllm_config = vllm_config  # needed for torch compile forward context
        thinker_config: Qwen3ASRThinkerConfig = (
            vllm_config.model_config.hf_config.thinker_config
        )
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
        self.config = thinker_config
        self.multimodal_config = multimodal_config

        self.audio_tower = Qwen3OmniMoeAudioEncoder(
            thinker_config.audio_config,
            prefix=maybe_prefix(prefix, "audio_tower"),
        )
        self.quant_config = quant_config

        self.language_model = Qwen3ForCausalLM(
            vllm_config=vllm_config.with_hf_config(
                thinker_config.text_config, architectures=["Qwen3ForCausalLM"]
            ),
            prefix=maybe_prefix(prefix, "language_model"),
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    def _parse_and_validate_audio_input(
        self, **kwargs: object
    ) -> Qwen2_5OmniAudioFeatureInputs | None:
        input_audio_features = kwargs.pop("input_audio_features", None)
        audio_feature_lengths = kwargs.pop("audio_feature_lengths", None)
        feature_attention_mask = kwargs.pop("feature_attention_mask", None)
        if input_audio_features is None:
            return None

        return Qwen2_5OmniAudioFeatureInputs(
            type="audio_features",
            input_features=input_audio_features,
            audio_feature_lengths=audio_feature_lengths,
            feature_attention_mask=feature_attention_mask,
        )

    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        mm_input_by_modality = {}

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
            if (
                input_key in ("input_audio_features")
                and "audio" not in mm_input_by_modality
            ):
                mm_input_by_modality["audio"] = self._parse_and_validate_audio_input(
                    **kwargs
                )
        return mm_input_by_modality

    def _process_audio_input(
        self,
        audio_input: Qwen2_5OmniAudioFeatureInputs,
        audio_hashes: list[str] | None = None,
        cached_audio_features: torch.Tensor | None = None,
    ) -> torch.Tensor:
        input_features = audio_input["input_features"]
        audio_feature_lengths = audio_input["audio_feature_lengths"]

        audio_output_lengths = _get_feat_extract_output_lengths(audio_feature_lengths)

        audio_features = self.audio_tower(
            input_features.to(self.audio_tower.dtype),
            feature_lens=audio_feature_lengths,
            aftercnn_lens=audio_output_lengths,
        )
        return audio_features.split(audio_output_lengths.tolist())

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not mm_input_by_modality:
            return []

        # The result multimodal_embeddings is tuple of tensors, with each
        # tensor correspoending to a multimodal data item (image or video).
        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in mm_input_by_modality:
            multimodal_input = mm_input_by_modality[modality]
            if modality == "audio":
                audio_embeddings = self._process_audio_input(multimodal_input)
                multimodal_embeddings += tuple(audio_embeddings)
        return multimodal_embeddings

    def embed_input_ids(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: MultiModalEmbeddings | None = None,
        *,
        is_multimodal: torch.Tensor | None = None,
        handle_oov_mm_token: bool = False,
    ) -> torch.Tensor:
        inputs_embeds = self._embed_text_input_ids(
            input_ids,
            self.language_model.embed_input_ids,
            is_multimodal=is_multimodal,
            handle_oov_mm_token=handle_oov_mm_token,
        )

        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
            return inputs_embeds

        inputs_embeds = _merge_multimodal_embeddings(
            inputs_embeds=inputs_embeds,
            multimodal_embeddings=multimodal_embeddings,
            is_multimodal=is_multimodal,
        )

        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> torch.Tensor | IntermediateTensors:
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.language_model.model(
            input_ids,
            positions,
            intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )

        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        return self.language_model.compute_logits(hidden_states)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=["talker.", "code2wav."],
        )
        loaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

        return loaded_weights

    def get_mrope_input_positions(
        self,
        input_tokens: list[int],
        mm_features: list[MultiModalFeatureSpec],
    ) -> tuple[torch.Tensor, int]:
        seq_len = len(input_tokens)

        if not mm_features:
            # No audio features, just return linear positions
            llm_positions = (
                torch.arange(seq_len, dtype=torch.long).view(1, -1).expand(3, -1)
            )
            return llm_positions.clone(), 0

        llm_pos_ids_list: list[torch.Tensor] = []
        st = 0

        for mm_feature in sorted(mm_features, key=lambda f: f.mm_position.offset):
            offset = mm_feature.mm_position.offset

            # Get audio feature length from mm_feature data
            audio_feature_length = mm_feature.data["audio_feature_lengths"].data
            if isinstance(audio_feature_length, torch.Tensor):
                audio_feature_length = audio_feature_length.item()
            audio_len = _get_feat_extract_output_lengths(
                torch.tensor(audio_feature_length)
            ).item()

            # Text segment before audio (includes audio_start token)
            text_len = offset - st
            st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
            text_positions = (
                torch.arange(text_len, dtype=torch.long).view(1, -1).expand(3, -1)
                + st_idx
            )
            llm_pos_ids_list.append(text_positions)
            st_idx = st_idx + text_len

            # Audio token segment
            audio_positions = (
                torch.arange(audio_len, dtype=torch.long).view(1, -1).expand(3, -1)
                + st_idx
            )
            llm_pos_ids_list.append(audio_positions)

            st = offset + audio_len

        # Handle remaining text (includes audio_end and any trailing text)
        if st < seq_len:
            st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
            text_len = seq_len - st
            final_text_positions = (
                torch.arange(text_len, dtype=torch.long).view(1, -1).expand(3, -1)
                + st_idx
            )
            llm_pos_ids_list.append(final_text_positions)

        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
        if llm_positions.shape[1] != seq_len:
            raise RuntimeError("Position ids length mismatch with input ids length")

        mrope_position_delta = (llm_positions.max() + 1 - seq_len).item()
        return llm_positions, mrope_position_delta

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            tower_model=["audio_tower."],
        )

    @classmethod
    def get_speech_to_text_config(
        cls, model_config: ModelConfig, task_type: str
    ) -> SpeechToTextConfig:
        processor = cached_processor_from_config(model_config)
        feature_extractor: WhisperFeatureExtractor = processor.feature_extractor
        return SpeechToTextConfig(
            max_audio_clip_s=feature_extractor.chunk_length,
            sample_rate=feature_extractor.sampling_rate,
        )

    @classmethod
    def get_generation_prompt(
        cls,
        audio: np.ndarray,
        model_config: ModelConfig,
        stt_config: SpeechToTextConfig,
        language: str | None,
        task_type: Literal["transcribe", "translate"],
        request_prompt: str,
        to_language: str | None,
    ) -> PromptType:
        """Get the generation prompt to be used for transcription requests."""
        tokenizer = cached_tokenizer_from_config(model_config)
        audio_placeholder = cls.get_placeholder_str("audio", 0)

        if task_type not in ("transcribe", "translate"):
            raise ValueError(
                f"Unsupported task_type '{task_type}'. "
                "Supported task types are 'transcribe' and 'translate'."
            )
        full_lang_name_to = cls.supported_languages.get(to_language, to_language)
        if to_language is None:
            prompt = (
                f"<|im_start|>user\n{audio_placeholder}<|im_end|>\n"
                f"<|im_start|>assistant\n"
            )
        else:
            prompt = (
                f"<|im_start|>user\n{audio_placeholder}<|im_end|>\n"
                f"<|im_start|>assistant\nlanguage {full_lang_name_to}<asr_text>"
            )

        prompt_token_ids = tokenizer.encode(prompt)
        prompt_dict = {
            "prompt_token_ids": prompt_token_ids,
            "multi_modal_data": {"audio": audio},
        }
        return cast(PromptType, prompt_dict)
