# coding=utf-8
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn

from transformers.models.llava_next.modeling_llava_next import (
    LlavaNextCausalLMOutputWithPast,
    LlavaNextForConditionalGeneration,
    image_size_to_num_patches,
)

from ...configuration_utils import PretrainedConfig
from ...utils import (
    logging,
)
from ..auto import CONFIG_MAPPING, AutoConfig


logger = logging.get_logger(__name__)


class LlavaNextVideoConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`LlavaNextVideoForConditionalGeneration`]. It is used to instantiate an
    Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the [llava-hf/LLaVA-NeXT-Video-7B-hf](https://huggingface.co/llava-hf/LLaVA-NeXT-Video-7B-hf)
    model.
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vision_config (`Union[AutoConfig, dict]`,  *optional*, defaults to `CLIPVisionConfig`):
            The config object or dictionary of the vision backbone.
        text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
            The config object or dictionary of the text backbone.
        ignore_index (`int`, *optional*, defaults to -100):
            The ignore index for the loss function.
        image_token_index (`int`, *optional*, defaults to 32001):
            The image token index to encode the image prompt.
        projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The activation function used by the multimodal projector.
        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
            The feature selection strategy used to select the vision feature from the vision backbone.
            Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
            If `"full"`, the full vision features are used.
        vision_feature_layer (`int`, *optional*, defaults to -2):
            The index of the layer to select the vision feature.
        image_grid_pinpoints (`List`, *optional*, defaults to `[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]`):
            A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
            of the form `(height, width)`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        video_token_index (`int`, *optional*, defaults to 32000):
            The video token index to encode the image prompt.
        spatial_pool_mode (`str`, *optional*, defaults to `"average"`):
            Pooling mode to use for videos. Can be "average", "max" or "conv".
        spatial_pool_stride (`int`, *optional*, defaults to 2):
            Stride used in the pooling layer for videos.
        image_seq_length (`int`, *optional*, defaults to 576):
            Sequence length of one image embedding.
        video_seq_length (`int`, *optional*, defaults to 288):
            Sequence length of one video embedding.

    Example:

    ```python
    >>> from transformers import LlavaNextVideoForConditionalGeneration, LlavaNextVideoConfig, CLIPVisionConfig, LlamaConfig

    >>> # Initializing a CLIP-vision config
    >>> vision_config = CLIPVisionConfig()

    >>> # Initializing a Llama config
    >>> text_config = LlamaConfig()

    >>> configuration = LlavaNextVideoConfig(vision_config, text_config)

    >>> model = LlavaNextVideoForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "llava_next_video"
    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}

    def __init__(
        self,
        vision_config=None,
        text_config=None,
        ignore_index=-100,
        image_token_index=32001,
        projector_hidden_act="gelu",
        vision_feature_select_strategy="default",
        vision_feature_layer=-2,
        image_grid_pinpoints=None,
        tie_word_embeddings=False,
        video_token_index=32000,
        spatial_pool_mode="average",
        spatial_pool_stride=2,
        image_seq_length=576,
        video_seq_length=288,
        **kwargs,
    ):
        self.video_token_index = video_token_index
        self.spatial_pool_mode = spatial_pool_mode
        self.spatial_pool_stride = spatial_pool_stride
        self.image_seq_length = image_seq_length
        self.video_seq_length = video_seq_length
        self.ignore_index = ignore_index
        self.image_token_index = image_token_index
        self.projector_hidden_act = projector_hidden_act

        if vision_feature_select_strategy not in ["default", "full"]:
            raise ValueError(
                "vision_feature_select_strategy should be one of 'default', 'full'."
                f"Got: {vision_feature_select_strategy}"
            )

        self.vision_feature_select_strategy = vision_feature_select_strategy
        self.vision_feature_layer = vision_feature_layer
        image_grid_pinpoints = (
            image_grid_pinpoints
            if image_grid_pinpoints is not None
            else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
        )
        self.image_grid_pinpoints = image_grid_pinpoints

        if isinstance(vision_config, dict):
            vision_config["model_type"] = (
                vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
            )
            vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
        elif vision_config is None:
            vision_config = CONFIG_MAPPING["clip_vision_model"](
                intermediate_size=4096,
                hidden_size=1024,
                patch_size=14,
                image_size=336,
                num_hidden_layers=24,
                num_attention_heads=16,
                vocab_size=32000,
                projection_dim=768,
            )

        self.vision_config = vision_config

        if isinstance(text_config, dict):
            text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
        elif text_config is None:
            text_config = CONFIG_MAPPING["llama"]()

        self.text_config = text_config

        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)


@dataclass
class LlavaNextVideoCausalLMOutputWithPast(LlavaNextCausalLMOutputWithPast):
    """
    video_hidden_states (`torch.FloatTensor`, *optional*):
        A `torch.FloatTensor`  of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`.
        video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
    """

    video_hidden_states: Optional[torch.FloatTensor] = None


class LlavaNextVideoPooler(nn.Module):
    def __init__(self, config):
        super().__init__()

        mode = config.spatial_pool_mode
        stride = config.spatial_pool_stride
        out_channels = getattr(config, "spatial_pool_out_channels", config.vision_config.hidden_size)
        self.image_size = (config.vision_config.image_size // config.vision_config.patch_size) ** 2

        if mode == "average":
            self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
        elif mode == "max":
            self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
        elif mode == "conv":
            self.pool = nn.Conv2d(
                in_channels=config.vision_config.hidden_size,
                out_channels=out_channels,
                kernel_size=stride,
                stride=stride,
            )
        else:
            raise ValueError(f"Unknown pooling mode: {mode}. Has to be one of [`average`, `max`, `conv`]")

    def forward(self, image_features):
        ori_width = int(math.sqrt(image_features.shape[1] * self.image_size // self.image_size))
        ori_height = int(ori_width * self.image_size // self.image_size)

        batch_size, _, dim = image_features.shape
        image_features_spatial = image_features.view(batch_size, ori_height, ori_height, dim).permute(0, 3, 1, 2)
        image_features_spatial_pool = self.pool(image_features_spatial)

        return image_features_spatial_pool.flatten(2).transpose(1, 2).contiguous()


class LlavaNextVideoForConditionalGeneration(LlavaNextForConditionalGeneration):
    def __init__(self, config: LlavaNextVideoConfig, **super_kwargs):
        super().__init__(config, **super_kwargs)
        self.vision_resampler = LlavaNextVideoPooler(config)
        self.post_init()

    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_sizes: torch.Tensor,
        vision_feature_layer: int,
        vision_feature_select_strategy: str,
    ):
        """
        Obtains image last hidden states from the vision tower and apply multimodal projection.

        Args:
            pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
               The tensors corresponding to the input images.
            image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
                Actual image size of each images (H, W).
            vision_feature_layer (`int`):
                The index of the layer to select the vision feature.
            vision_feature_select_strategy (`str`):
                The feature selection strategy used to select the vision feature from the vision backbone.
                Can be one of `"default"` or `"full"`
        Returns:
            image_features (List[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches
            and are of shape `(num_patches, image_length, embed_dim)`).
        """
        # ! infer image_num_patches from image_sizes
        image_num_patches = [
            image_size_to_num_patches(
                image_size=imsize,
                grid_pinpoints=self.config.image_grid_pinpoints,
                patch_size=self.config.vision_config.image_size,
            )
            for imsize in image_sizes
        ]
        if pixel_values.dim() == 5:
            # stacked if input is (batch_size, num_patches, num_channels, height, width)
            _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
            pixel_values = torch.cat(_pixel_values_list, dim=0)
        elif pixel_values.dim() != 4:
            # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
            raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")

        image_features = self.vision_tower(pixel_values, output_hidden_states=True)
        selected_image_feature = image_features.hidden_states[vision_feature_layer]
        if vision_feature_select_strategy == "default":
            selected_image_feature = selected_image_feature[:, 1:]
        elif vision_feature_select_strategy == "full":
            selected_image_feature = selected_image_feature
        image_features = self.multi_modal_projector(selected_image_feature)
        image_features = torch.split(image_features, image_num_patches, dim=0)
        return image_features

    def get_video_features(
        self, pixel_values: torch.FloatTensor, vision_feature_layer: int, vision_feature_select_strategy: str
    ):
        """
        Obtains video last hidden states from the vision tower and apply multimodal projection.

        Args:
            pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
               The tensors corresponding to the input video.
            vision_feature_layer (`int`):
                The index of the layer to select the vision feature.
            vision_feature_select_strategy (`str`):
                The feature selection strategy used to select the vision feature from the vision backbone.
                Can be one of `"default"` or `"full"`
        Returns:
            video_features (List[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches
            and are of shape `(num_videos, video_length, embed_dim)`).
        """
        batch_size, frames, channels, height, width = pixel_values.shape
        pixel_values = pixel_values.reshape(batch_size * frames, channels, height, width)
        video_features = self.vision_tower(pixel_values, output_hidden_states=True)
        selected_video_features = video_features.hidden_states[vision_feature_layer]
        if vision_feature_select_strategy == "default":
            selected_video_features = selected_video_features[:, 1:]
        elif vision_feature_select_strategy == "full":
            selected_video_features = selected_video_features

        # Same as image features except that video has pooling layer
        video_features = self.vision_resampler(selected_video_features)
        video_features = self.multi_modal_projector(video_features)
        video_features = torch.split(video_features, frames, dim=0)
        return video_features

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        pixel_values_videos: torch.FloatTensor = None,
        image_sizes: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        vision_feature_layer: Optional[int] = None,
        vision_feature_select_strategy: Optional[str] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        num_logits_to_keep: int = 0,
    ) -> Union[Tuple, LlavaNextVideoCausalLMOutputWithPast]:
        r"""
        Args:
            pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, image_size, image_size)):
                The tensors corresponding to the input videos. Pixel values can be obtained using
                [`AutoImageProcessor`]. See [`LlavaNextVideoVideoProcessor.__call__`] for details. [`LlavaProcessor`] uses
                [`LlavaNextVideoVideoProcessor`] for processing videos.
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
            num_logits_to_keep (`int`, *optional*):
                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.

        Returns:

        Example:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> import av
        >>> from transformers import AutoProcessor, LlavaNextVideoForConditionalGeneration

        >>> def read_video_pyav(container, indices):
        ...     '''
        ...     Decode the video with PyAV decoder.
        ...     Args:
        ...         container (`av.container.input.InputContainer`): PyAV container.
        ...         indices (`List[int]`): List of frame indices to decode.
        ...     Returns:
        ...         result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
        ...     '''
        ...     frames = []
        ...     container.seek(0)
        ...     start_index = indices[0]
        ...     end_index = indices[-1]
        ...     for i, frame in enumerate(container.decode(video=0)):
        ...         if i > end_index:
        ...             break
        ...         if i >= start_index and i in indices:
        ...             frames.append(frame)
        ...     return np.stack([x.to_ndarray(format="rgb24") for x in frames])

        >>> model = LlavaNextVideoForConditionalGeneration.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf", device_map="auto")
        >>> processor = AutoProcessor.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf")

        >>> prompt = "USER: <video>\nWhy is this video funny? ASSISTANT:"
        >>> video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
        >>> container = av.open(video_path)

        >>> # sample uniformly 8 frames from the video (model was trained with 32 frames per video, but this video is short)
        >>> total_frames = container.streams.video[0].frames
        >>> indices = np.arange(0, total_frames, total_frames / 8).astype(int)
        >>> clip = read_video_pyav(container, indices)
        >>> inputs_video = processor(text=prompt, videos=clip, return_tensors="pt").to(model.device)

        >>> # load an image to generate from an image
        >>> prompt = "USER:<image>\nWhat is shown in this image? ASSISTANT:"
        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> inputs_image = processor(text=prompt, images=image, return_tensors="pt").to(model.device)

        >>> # Generate from video
        >>> generate_ids = model.generate(**inputs_video, max_length=50)
        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "USER:\nWhy is this video funny? ASSISTANT: The humor in this video comes from the unexpected and endearing sight of a baby wearing glasses and (...)"

        >>> # Generate from image
        >>> generate_ids = model.generate(**inputs_image, max_length=30)
        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "USER: \nWhat's the content of the image? ASSISTANT: The image shows a red stop sign on a pole, with a traditional Chinese archway (...)"
        ```"""

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        self.vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
        )
        self.vision_feature_select_strategy = (
            vision_feature_select_strategy
            if vision_feature_select_strategy is not None
            else self.config.vision_feature_select_strategy
        )

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both `pixel_values`/`pixel_values_videos` and `inputs_embeds` at the same time, "
                "and must specify either one"
            )

        legacy_processing = False
        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)

            # if the number of image/video tokens is more than image embeddings seq length, then prob we expanded it in processing
            # not very reliable, but we don't expect one to actually pass 500+ images for one prompt
            img_token_not_enough = (input_ids == self.config.image_token_index).sum(
                1
            ).max() < self.config.image_seq_length
            video_token_not_enough = (input_ids == self.config.video_token_index).sum(
                1
            ).max() < self.config.video_seq_length
            inputs_not_expanded = (img_token_not_enough and pixel_values is not None) or (
                video_token_not_enough and pixel_values_videos is not None
            )
            pixels_present = input_ids.shape[-1] == 1 and (pixel_values is not None or pixel_values_videos is not None)
            legacy_processing = inputs_not_expanded or pixels_present

        image_features = feature_lens = None
        if pixel_values is not None and pixel_values.size(0) > 0:
            image_features = self.get_image_features(
                pixel_values,
                image_sizes,
                vision_feature_layer=self.vision_feature_layer,
                vision_feature_select_strategy=self.vision_feature_select_strategy,
            )
            image_features, feature_lens = self.pack_image_features(
                image_features,
                image_sizes,
                self.vision_feature_select_strategy,
                image_newline=self.image_newline,
            )

        video_features = video_feature_lens = None
        if pixel_values_videos is not None and pixel_values_videos.size(0) > 0:
            video_features = self.get_video_features(
                pixel_values_videos,
                vision_feature_layer=self.vision_feature_layer,
                vision_feature_select_strategy=self.vision_feature_select_strategy,
            )
            video_features = [feature.flatten(0, 1) for feature in video_features]
            video_feature_lens = [feature.size(0) for feature in video_features]
            video_features = torch.cat(video_features, dim=0)
            video_feature_lens = torch.tensor(video_feature_lens, dtype=torch.long, device=video_features.device)

        if legacy_processing:
            logger.warning_once(
                "Expanding inputs for image.video tokens in LLaVa-NeXT-Video should be done in processing. "
                "Please add `patch_size` and `vision_feature_select_strategy` to the model's processing config or set directly "
                "with `processor.patch_size = {{patch_size}}` and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. "
                "Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
            )
            if input_ids.shape[1] != 1:
                iterator = (
                    (image_features, feature_lens, self.config.image_token_index),
                    (video_features, video_feature_lens, self.config.video_token_index),
                )
                for features, lens, special_token in iterator:
                    if features is not None:
                        (
                            inputs_embeds,
                            attention_mask,
                            position_ids,
                            labels,
                            input_ids,
                        ) = self._merge_input_ids_with_image_features(
                            features,
                            lens,
                            inputs_embeds,
                            input_ids,
                            attention_mask,
                            position_ids,
                            labels=labels,
                            image_token_index=special_token,
                        )
                cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)
            else:
                # Retrieve the first layer to inspect the logits and mask out the hidden states that are set to 0
                first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
                # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
                batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
                # Get the target length
                target_length = input_ids.shape[1]
                past_length = first_layer_past_key_value.shape[-1]
                extended_attention_mask = torch.ones(
                    (attention_mask.shape[0], past_length),
                    dtype=attention_mask.dtype,
                    device=attention_mask.device,
                )
                # Filter out only the tokens that can be un-attended, this can happen
                # if one uses Llava + Fused modules where the cache on the
                # first iteration is already big enough, or if one passes custom cache
                valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
                new_batch_index = batch_index[valid_indices]
                new_non_attended_tokens = non_attended_tokens[valid_indices]
                # Zero-out the places where we don't need to attend
                extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
                attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
                cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)[-target_length:]

        # TODO: @raushan retain only the new behavior after v4.47
        else:
            if image_features is not None:
                n_image_tokens = (input_ids == self.config.image_token_index).sum().item()
                n_image_features = image_features.shape[0]

                if n_image_tokens != n_image_features:
                    raise ValueError(
                        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
                    )
                special_image_mask = (
                    (input_ids == self.config.image_token_index)
                    .unsqueeze(-1)
                    .expand_as(inputs_embeds)
                    .to(inputs_embeds.device)
                )
                image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
                inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
            if video_features is not None:
                n_video_tokens = (input_ids == self.config.video_token_index).sum().item()
                n_video_features = video_features.shape[0]
                if n_video_tokens != n_video_features:
                    raise ValueError(
                        f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
                    )
                special_image_mask = (
                    (input_ids == self.config.video_token_index)
                    .unsqueeze(-1)
                    .expand_as(inputs_embeds)
                    .to(inputs_embeds.device)
                )
                video_features = video_features.to(inputs_embeds.device, inputs_embeds.dtype)
                inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, video_features)

        outputs = self.language_model(
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
            num_logits_to_keep=num_logits_to_keep,
        )

        logits = outputs[0]

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            if attention_mask is not None:
                # we use the input attention mask to shift the logits and labels, because it is 2D.
                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
                shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device)
                shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
                shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
            else:
                shift_logits = logits[..., :-1, :].contiguous()
                shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(
                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
            )

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return LlavaNextVideoCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=image_features if pixel_values is not None else None,
            video_hidden_states=video_features if pixel_values_videos is not None else None,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        pixel_values=None,
        pixel_values_videos=None,
        image_sizes=None,
        attention_mask=None,
        cache_position=None,
        num_logits_to_keep=None,
        **kwargs,
    ):
        # Overwritten -- extra custom processing

        model_inputs = self.language_model.prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            num_logits_to_keep=num_logits_to_keep,
            **kwargs,
        )

        # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
        # Otherwise we need pixel values to be passed to model
        if cache_position[0] == 0:
            model_inputs["pixel_values"] = pixel_values
            model_inputs["pixel_values_videos"] = pixel_values_videos
            model_inputs["image_sizes"] = image_sizes

        return model_inputs