# coding=utf-8
# Copyright 2024 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,
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"""PyTorch VideoLlava model."""

from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

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

from ... import PreTrainedModel
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...modeling_outputs import BaseModelOutputWithPooling, ModelOutput
from ...utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ..auto import AutoModel, AutoModelForCausalLM
from .configuration_video_llava import VideoLlavaConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "VideoLlavaConfig"


@dataclass
# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->VideoLlava
class VideoLlavaCausalLMOutputWithPast(ModelOutput):
    """
    Base class for VideoLlava causal language model (or autoregressive) outputs.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)

            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
            sequence_length, hidden_size)`.

            image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
    """

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[List[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None


# Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->VideoLlava
class VideoLlavaMultiModalProjector(nn.Module):
    def __init__(self, config: VideoLlavaConfig):
        super().__init__()

        self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
        self.act = ACT2FN[config.projector_hidden_act]
        self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)

    def forward(self, image_features):
        hidden_states = self.linear_1(image_features)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


VIDEO_LLAVA_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`VideoLlavaConfig`] or [`VideoLlavaVisionConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    VIDEO_LLAVA_START_DOCSTRING,
)
class VideoLlavaPreTrainedModel(PreTrainedModel):
    config_class = VideoLlavaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["VideoLlavaVisionAttention"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True

    def _init_weights(self, module):
        std = (
            self.config.initializer_range
            if hasattr(self.config, "initializer_range")
            else self.config.text_config.initializer_range
        )

        if hasattr(module, "class_embedding"):
            module.class_embedding.data.normal_(mean=0.0, std=std)

        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    @property
    def _supports_sdpa(self):
        """
        Retrieve language_model's attribute to check whether the model supports
        SDPA or not.
        """
        return self.language_model._supports_sdpa


VIDEO_LLAVA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        pixel_values_images (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
            The tensors corresponding to the input images. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`VideoLlavaImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
            [`VideoLlavaImageProcessor`] for processing images).
        pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, image_size, image_size)):
            The tensors corresponding to the input video. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`VideoLlavaImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
            [`VideoLlavaImageProcessor`] for processing videos).
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        vision_feature_layer (`int`, *optional*, defaults to -2):
            The index of the layer to select the vision feature.
        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"`
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    """The VideoLlava model which consists of a vision backbone and a language model.""",
    VIDEO_LLAVA_START_DOCSTRING,
)
class VideoLlavaForConditionalGeneration(VideoLlavaPreTrainedModel):
    def __init__(self, config: VideoLlavaConfig):
        super().__init__(config)
        self.video_tower = AutoModel.from_config(config.vision_config)
        self.image_tower = AutoModel.from_config(config.vision_config)

        self.multi_modal_projector = VideoLlavaMultiModalProjector(config)
        self.vocab_size = config.text_config.vocab_size
        self.language_model = AutoModelForCausalLM.from_config(
            config.text_config, attn_implementation=config._attn_implementation
        )
        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
        self.post_init()

    def get_input_embeddings(self):
        return self.language_model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.language_model.set_input_embeddings(value)

    def get_output_embeddings(self):
        return self.language_model.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        self.language_model.set_output_embeddings(new_embeddings)

    def set_decoder(self, decoder):
        self.language_model.set_decoder(decoder)

    def get_decoder(self):
        return self.language_model.get_decoder()

    def tie_weights(self):
        return self.language_model.tie_weights()

    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
        # update vocab size
        self.config.text_config.vocab_size = model_embeds.num_embeddings
        self.config.vocab_size = model_embeds.num_embeddings
        self.vocab_size = model_embeds.num_embeddings
        return model_embeds

    def _merge_input_ids_with_visual_features(
        self, visual_features, inputs_embeds, input_ids, attention_mask, labels, num_frames=1
    ):
        num_images, num_image_patches, embed_dim = visual_features.shape
        batch_size, sequence_length = input_ids.shape
        left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
        special_vision_token = self.config.video_token_index if num_frames > 1 else self.config.image_token_index

        # 1. Create a mask to know where special image tokens are
        special_image_token_mask = input_ids == special_vision_token
        num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
        # Compute the maximum embed dimension
        max_seq_len = (num_special_image_tokens.max() * (num_image_patches * num_frames - 1)) + sequence_length
        batch_indices, non_image_indices = torch.where(input_ids != special_vision_token)

        # 2. Compute the positions where text should be written
        # Calculate new positions for text tokens in merged image-text sequence.
        # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
        # `torch.cumsum` computes how each image token shifts subsequent text token positions.
        # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
        new_token_positions = (
            torch.cumsum((special_image_token_mask * (num_image_patches * num_frames - 1) + 1), dim=-1) - 1
        )
        nb_image_pad = max_seq_len - 1 - new_token_positions[:, -1]
        if left_padding:
            new_token_positions += nb_image_pad[:, None]  # offset for left padding
        text_to_overwrite = new_token_positions[batch_indices, non_image_indices]

        # 3. Create the full embedding, already padded to the maximum position
        # expand input ids so that the second "merge" with videos does not fail
        final_embedding = torch.zeros(
            batch_size, max_seq_len, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
        )
        final_attention_mask = torch.zeros(
            batch_size, max_seq_len, dtype=attention_mask.dtype, device=inputs_embeds.device
        )
        final_input_ids = torch.full(
            (batch_size, max_seq_len), self.pad_token_id, dtype=input_ids.dtype, device=inputs_embeds.device
        )
        # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
        # set the corresponding tensors into their correct target device.
        target_device = inputs_embeds.device
        batch_indices, non_image_indices, text_to_overwrite = (
            batch_indices.to(target_device),
            non_image_indices.to(target_device),
            text_to_overwrite.to(target_device),
        )
        attention_mask = attention_mask.to(target_device)

        # 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
        # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
        final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
        final_input_ids[batch_indices, text_to_overwrite] = input_ids[batch_indices, non_image_indices]
        if labels is not None:
            final_labels = torch.full(
                (batch_size, max_seq_len), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
            )
            final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
        else:
            final_labels = None

        # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
        image_to_overwrite = torch.full((batch_size, max_seq_len), True, dtype=torch.bool, device=inputs_embeds.device)
        image_to_overwrite[batch_indices, text_to_overwrite] = False
        image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)

        if image_to_overwrite.sum() != visual_features.shape[:-1].numel():
            visual_type = "videos" if num_frames == 8 else "images"
            num_images //= num_frames
            raise ValueError(
                f"The input provided to the model are wrong. The number of {visual_type} tokens is {torch.sum(special_image_token_mask)} while"
                f" the number of {visual_type} given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
            )

        final_embedding[image_to_overwrite] = visual_features.contiguous().reshape(-1, embed_dim).to(target_device)
        final_attention_mask |= image_to_overwrite
        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)

        return final_embedding, final_attention_mask, final_labels, position_ids, final_input_ids

    def _get_vision_features(
        self,
        pixel_values_images: Optional[torch.FloatTensor] = None,
        pixel_values_videos: Optional[torch.FloatTensor] = None,
        vision_feature_layer: Optional[int] = None,
        vision_feature_select_strategy: Optional[str] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        if pixel_values_images is None and pixel_values_videos is None:
            raise ValueError("You have to specify `pixel_values_images` or `pixel_values_videos`")

        # videos do not need to select features and it's always "full" (as it is done in the orig implementation)
        if pixel_values_videos is not None:
            batch_size_vid, num_frames, channels, height, width = pixel_values_videos.shape

            pixel_values = pixel_values_videos.reshape(batch_size_vid * num_frames, channels, height, width)
            video_outputs = self.video_tower(pixel_values, output_hidden_states=True)
            video_outputs = video_outputs.hidden_states[vision_feature_layer].squeeze(1)
        else:
            video_outputs = None
            num_frames = 0

        if pixel_values_images is not None:
            image_outputs = self.image_tower(pixel_values_images, output_hidden_states=True)
            image_outputs = image_outputs.hidden_states[vision_feature_layer].squeeze(1)

            if vision_feature_select_strategy == "default":
                image_outputs = image_outputs[:, 1:]
            elif vision_feature_select_strategy == "full":
                image_outputs = image_outputs
            else:
                raise ValueError(f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}")
        else:
            image_outputs = None

        return image_outputs, video_outputs, num_frames

    @add_start_docstrings_to_model_forward(VIDEO_LLAVA_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=VideoLlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values_images: torch.FloatTensor = None,
        pixel_values_videos: torch.FloatTensor = 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,
    ) -> Union[Tuple, VideoLlavaCausalLMOutputWithPast]:
        r"""
        Args:
            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]`.

        Returns:

        Example:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> import numpy as np
        >>> import av
        >>> from huggingface_hub import hf_hub_download
        >>> from transformers import VideoLlavaProcessor, VideoLlavaForConditionalGeneration


        >>> 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 = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")
        >>> processor = VideoLlavaProcessor.from_pretrained("LanguageBind/Video-LLaVA-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
        >>> 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 = processor(text=prompt, videos=clip, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(**inputs, max_length=80)
        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "USER:  Why is this video funny? ASSISTANT: The video is funny because the baby is playing with a Wii remote while sitting on the floor, and the baby is wearing glasses.Ъ. The baby's actions are amusing because it is a young child trying to interact with a video game, which is not a typical activity for a"

        >>> # to generate from image and video mix
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> prompt = [
        ...     "USER: <image>\nHow many cats do you see? ASSISTANT:",
        ...     "USER: <video>\nWhy is this video funny? ASSISTANT:"
        ... ]
        >>> inputs = processor(text=prompt, images=image, videos=clip, padding=True, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(**inputs, max_length=50)
        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
        ['USER:   How many cats do you see? ASSISTANT: There are two cats visible in the image. (or three, if you count the one in the background).', 'USER:  Why is this video funny? ASSISTANT: The video is funny because it shows a baby sitting on a bed and playing with a Wii remote.Ъ. The baby is holding the remote']
        ```
        """

        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
        vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
        )
        vision_feature_select_strategy = (
            vision_feature_select_strategy
            if vision_feature_select_strategy is not None
            else self.config.vision_feature_select_strategy
        )

        if inputs_embeds is None:
            # 1. Extra the input embeddings
            inputs_embeds = self.get_input_embeddings()(input_ids)

            # 2. Merge text and images
            if (pixel_values_images is not None or pixel_values_videos is not None) and input_ids.shape[1] != 1:
                image_outputs, video_outputs, num_frames = self._get_vision_features(
                    pixel_values_images=pixel_values_images,
                    pixel_values_videos=pixel_values_videos,
                    vision_feature_layer=vision_feature_layer,
                    vision_feature_select_strategy=vision_feature_select_strategy,
                )

                # first add image embeds where possible, then expand again and add video embeds
                if image_outputs is not None:
                    visual_features = self.multi_modal_projector(image_outputs)
                    (
                        inputs_embeds,
                        attention_mask,
                        labels,
                        position_ids,
                        input_ids,
                    ) = self._merge_input_ids_with_visual_features(
                        visual_features, inputs_embeds, input_ids, attention_mask, labels
                    )
                if video_outputs is not None:
                    visual_features = self.multi_modal_projector(video_outputs)
                    (
                        inputs_embeds,
                        attention_mask,
                        labels,
                        position_ids,
                        _,
                    ) = self._merge_input_ids_with_visual_features(
                        visual_features,
                        inputs_embeds,
                        input_ids,
                        attention_mask,
                        labels,
                        num_frames=num_frames,
                    )
            else:
                # In case input_ids.shape[1] == 1 & past_key_values != None, we are in the case of
                # generation with cache
                if past_key_values is not None and input_ids.shape[1] == 1:
                    # 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)

                    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

        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,
        )

        logits = outputs[0]

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            if attention_mask is not None:
                shift_attention_mask = attention_mask[..., 1:]
                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 VideoLlavaCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        pixel_values_images=None,
        pixel_values_videos=None,
        attention_mask=None,
        **kwargs,
    ):
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
            else:
                input_ids = input_ids[:, input_ids.shape[1] - 1 :]
            # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
            # older attention values, as their corresponding values are not part of the input.
            if cache_length < past_length and attention_mask is not None:
                attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]

            pixel_values_videos = None
            pixel_values_images = None

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
                "pixel_values_videos": pixel_values_videos,
                "pixel_values_images": pixel_values_images,
            }
        )
        return model_inputs

    def _reorder_cache(self, *args, **kwargs):
        return self.language_model._reorder_cache(*args, **kwargs)
