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#           This file was automatically generated from src/transformers/models/qwen3_omni_moe/modular_qwen3_omni_moe.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_qwen3_omni_moe.py file directly. One of our CI enforces this.
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# coding=utf-8
# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
#
# 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 re
from typing import Optional, Union

import numpy as np

from ...audio_utils import AudioInput
from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessingKwargs, ProcessorMixin, VideosKwargs
from ...tokenization_utils_base import TextInput
from ...video_utils import VideoInput, make_batched_videos


# Redefine kwargs for videos because Qwen-Omni uses some kwargs for processing omni
# and does not use them in video processor class
class Qwen3OmniMoeVideosKwargs(VideosKwargs, total=False):
    min_pixels: int
    max_pixels: int
    patch_size: int
    temporal_patch_size: int
    merge_size: int
    min_frames: int
    max_frames: int
    use_audio_in_video: bool
    seconds_per_chunk: float
    position_id_per_seconds: Union[int, float]


class Qwen3OmniMoeProcessorKwargs(ProcessingKwargs, total=False):
    videos_kwargs: Qwen3OmniMoeVideosKwargs
    _defaults = {
        "text_kwargs": {
            "padding": False,
            "padding_side": "left",
        },
        "videos_kwargs": {
            "seconds_per_chunk": 2.0,
            "position_id_per_seconds": 13.0,
            "use_audio_in_video": False,
            "size": {
                "shortest_edge": 128 * 32 * 32,
                "longest_edge": 768 * 32 * 32,
            },
        },
        "audio_kwargs": {
            "sampling_rate": 16000,
            "padding": True,
            "truncation": False,
            "return_attention_mask": True,
        },
    }


def _get_feat_extract_output_lengths(input_lengths):
    """
    Computes the output length of the convolutional layers and the output length of the audio encoder
    """

    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 Qwen3OmniMoeProcessor(ProcessorMixin):
    r"""
    Constructs a Qwen2.5Omni processor.
    [`Qwen3OmniMoeProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`], [`WhisperFeatureExtractor`], and [`Qwen2TokenizerFast`]. See the
    [`~Qwen3OmniMoeProcessor.__call__`] and [`~Qwen3OmniMoeProcessor.decode`] for more information.

    Args:
        image_processor ([`Qwen2VLImageProcessor`], *optional*):
            The image processor.
        video_processor ([`Qwen2VLVideoProcessor`], *optional*):
            The video processor.
        feature_extractor ([`WhisperFeatureExtractor`], *optional*):
            The audio feature extractor.
        tokenizer ([`Qwen2TokenizerFast`], *optional*):
            The text tokenizer.
        chat_template (`Optional[str]`, *optional*):
            The Jinja template to use for formatting the conversation. If not provided, the default chat template is used.
    """

    attributes = ["image_processor", "video_processor", "feature_extractor", "tokenizer"]
    image_processor_class = "AutoImageProcessor"
    video_processor_class = "AutoVideoProcessor"
    feature_extractor_class = "WhisperFeatureExtractor"
    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")

    def __init__(
        self, image_processor=None, video_processor=None, feature_extractor=None, tokenizer=None, chat_template=None
    ):
        super().__init__(image_processor, video_processor, feature_extractor, tokenizer, chat_template=chat_template)
        self.image_token = self.tokenizer.image_token
        self.audio_token = self.tokenizer.audio_token
        self.video_token = self.tokenizer.video_token
        self.vision_bos_token = self.tokenizer.vision_bos_token
        self.vision_eos_token = self.tokenizer.vision_eos_token
        self.audio_bos_token = self.tokenizer.audio_bos_token
        self.audio_eos_token = self.tokenizer.audio_eos_token

    def __call__(
        self,
        text: TextInput = None,
        images: Optional[ImageInput] = None,
        videos: Optional[VideoInput] = None,
        audio: Optional[AudioInput] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the audio(s), this method forwards the `audio` and `kwargs` arguments to
        WhisperFeatureExtractor's [`~WhisperFeatureExtractor.__call__`] if `audio` is not `None`. To prepare the vision inputs,
        this method forwards the `vision_infos` and `kwargs` arguments to Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`]
        if `vision_infos` is not `None`. Please refer to the doctsring
        of the above two methods for more information.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. Both channels-first and channels-last formats are supported.
            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
            audio (`np.ndarray`, `List[np.ndarray]`):
                The audio or batch of audio to be prepared. Each audio can be a NumPy array.
        """

        if text is None:
            raise ValueError("You need to specify either a `text` input to process.")

        output_kwargs = self._merge_kwargs(
            Qwen3OmniMoeProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        seconds_per_chunk = output_kwargs["videos_kwargs"].pop("seconds_per_chunk")
        position_id_per_seconds = output_kwargs["videos_kwargs"].pop("position_id_per_seconds")
        use_audio_in_video = output_kwargs["videos_kwargs"].pop("use_audio_in_video")
        fps = output_kwargs["videos_kwargs"].get("fps", 1.0)

        if audio is not None:
            audio_inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
            audio_inputs["feature_attention_mask"] = audio_inputs.pop(
                "attention_mask"
            )  # rename feature_attention_mask to prevent conflicts later on
            audio_inputs["input_features"] = audio_inputs.pop(
                "input_features"
            )  # rename input_features to prevent conflicts later on
            audio_lengths = iter(_get_feat_extract_output_lengths(audio_inputs["feature_attention_mask"].sum(-1)))
        else:
            audio_inputs = {}
            audio_lengths = iter([])

        if images is not None:
            images_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
            image_grid_thw = iter(images_inputs["image_grid_thw"])
        else:
            images_inputs = {}
            image_grid_thw = iter([])

        if videos is not None:
            videos = make_batched_videos(videos)
            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
            fps = [fps] * len(videos)
            videos_inputs["video_second_per_grid"] = [
                self.video_processor.temporal_patch_size / fps[i] for i in range(len(fps))
            ]
            video_grid_thw = iter(videos_inputs["video_grid_thw"])
            video_second_per_grid = iter(videos_inputs["video_second_per_grid"])
        else:
            videos_inputs = {}
            video_grid_thw = iter([])
            video_second_per_grid = iter([])

        if not isinstance(text, list):
            text = [text]

        text = self.replace_multimodal_special_tokens(
            text,
            audio_lengths,
            image_grid_thw,
            video_grid_thw,
            video_second_per_grid=video_second_per_grid,
            use_audio_in_video=use_audio_in_video,
            position_id_per_seconds=position_id_per_seconds,
            seconds_per_chunk=seconds_per_chunk,
        )

        texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])

        return BatchFeature(
            data={**texts_inputs, **images_inputs, **videos_inputs, **audio_inputs},
            tensor_type=kwargs.get("return_tensors"),
        )

    def replace_multimodal_special_tokens(
        self,
        text,
        audio_lengths,
        image_grid_thw,
        video_grid_thw,
        video_second_per_grid,
        use_audio_in_video,
        position_id_per_seconds,
        seconds_per_chunk,
    ):
        # Extend mm token length
        merge_length_image = self.image_processor.merge_size**2
        merge_length_video = self.video_processor.merge_size**2

        processed_text = []
        for sample in text:
            positions = []
            special_tokens = [re.escape(tok) for tok in [self.audio_token, self.image_token, self.video_token]]
            pattern = "|".join(special_tokens)
            positions = sorted([(match.start(), match.group()) for match in re.finditer(pattern, sample)])
            positions.sort(key=lambda x: x[0])

            for _, special_token in positions:
                if special_token == self.audio_token:
                    sample = sample.replace(self.audio_token, "<|audio_placeholder|>" * next(audio_lengths), 1)
                elif special_token == self.image_token:
                    image_seq_length = next(image_grid_thw).prod() // merge_length_image
                    sample = sample.replace(self.image_token, "<|image_placeholder|>" * image_seq_length, 1)
                elif special_token == self.video_token:
                    if not use_audio_in_video:
                        video_seq_length = next(video_grid_thw).prod() // merge_length_video
                        sample = sample.replace(self.video_token, "<|video_placeholder|>" * video_seq_length, 1)
                    else:
                        audio_token_indices = np.arange(next(audio_lengths))
                        curr_video_grid_thw = next(video_grid_thw)
                        height = curr_video_grid_thw[1] // self.video_processor.merge_size
                        width = curr_video_grid_thw[2] // self.video_processor.merge_size
                        video_token_indices = np.arange(curr_video_grid_thw[0]).reshape(-1, 1, 1)
                        video_token_indices = np.broadcast_to(
                            video_token_indices, (video_token_indices.shape[0], height, width)
                        ).reshape(-1)
                        video_token_indices = (
                            video_token_indices * next(video_second_per_grid) * position_id_per_seconds
                        )

                        video_data_index, audio_data_index = 0, 0
                        placeholder_string = self.vision_bos_token + self.audio_bos_token
                        while video_data_index < len(video_token_indices) and audio_data_index < len(
                            audio_token_indices
                        ):
                            if video_token_indices[video_data_index] <= audio_token_indices[audio_data_index]:
                                placeholder_string += "<|video_placeholder|>"
                                video_data_index += 1
                            else:
                                placeholder_string += "<|audio_placeholder|>"
                                audio_data_index += 1
                        if video_data_index < len(video_token_indices):
                            placeholder_string += "<|video_placeholder|>" * (
                                len(video_token_indices) - video_data_index
                            )
                        if audio_data_index < len(audio_token_indices):
                            placeholder_string += "<|audio_placeholder|>" * (
                                len(audio_token_indices) - audio_data_index
                            )
                        placeholder_string += self.audio_eos_token + self.vision_eos_token
                        sample = sample.replace(
                            self.vision_bos_token + self.video_token + self.vision_eos_token,
                            placeholder_string,
                            1,
                        )

            sample = sample.replace("<|audio_placeholder|>", self.audio_token)
            sample = sample.replace("<|image_placeholder|>", self.image_token)
            sample = sample.replace("<|video_placeholder|>", self.video_token)
            processed_text.append(sample)
        return processed_text

    def get_chunked_index(self, token_indices: np.ndarray, tokens_per_chunk: int) -> list[tuple[int, int]]:
        """
        Splits token index list into chunks based on token value ranges.

        Given a list of token indices, returns a list of (start, end) index tuples representing
        slices of the list where the token values fall within successive ranges of `t_ntoken_per_chunk`.

        For example, if `t_ntoken_per_chunk` is 1000, the function will create chunks such that:
        - the first chunk contains token values < 1000,
        - the second chunk contains values >= 1000 and < 2000, and so on.

        Parameters:
            token_indices (`np.ndarray`): A monotonically increasing list of token index values.
            t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).

        Returns:
            `list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive)
                                and end (exclusive) indices of a chunk in `token_indices`.
        """

        def _iter():
            i, start_idx = 0, 0  # skip bos token
            current_chunk = 1
            while i < len(token_indices):  # skip eos token
                if token_indices[i] >= current_chunk * tokens_per_chunk:
                    yield (start_idx, i)
                    start_idx = i
                    current_chunk += 1
                i += 1
            yield (start_idx, len(token_indices))

        return list(_iter())

    def apply_chat_template(self, conversations, chat_template=None, **kwargs):
        return super().apply_chat_template(conversations, chat_template, **kwargs)

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        feature_extractor_input_names = self.feature_extractor.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        video_processor_input_names = self.video_processor.model_input_names
        return list(
            dict.fromkeys(
                tokenizer_input_names
                + feature_extractor_input_names
                + image_processor_input_names
                + video_processor_input_names
                + ["feature_attention_mask"]
                + ["video_second_per_grid"]
            )
        )


__all__ = ["Qwen3OmniMoeProcessor"]
