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# 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,
# 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 math
from dataclasses import dataclass

import numpy as np
import torch
from torch import nn

from ... import initialization as init
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...generation import GenerationMixin
from ...image_processing_utils import select_best_resolution
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, torch_compilable_check
from ...utils.generic import can_return_tuple, merge_with_config_defaults
from ..auto import AutoModel
from .configuration_llava_onevision import LlavaOnevisionConfig


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for Llava outputs, with hidden states and attentions.
    """
)
class LlavaOnevisionModelOutputWithPast(BaseModelOutputWithPast):
    r"""
    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

        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.
    image_hidden_states (`torch.FloatTensor`, *optional*):
        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
    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.
    """

    image_hidden_states: torch.FloatTensor | None = None

    video_hidden_states: torch.FloatTensor | None = None


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for LlavaOnevision causal language model (or autoregressive) outputs.
    """
)
class LlavaOnevisionCausalLMOutputWithPast(ModelOutput):
    r"""
    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 (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

        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.
    image_hidden_states (`torch.FloatTensor`, *optional*):
        A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
    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.
    """

    loss: torch.FloatTensor | None = None
    logits: torch.FloatTensor | None = None
    past_key_values: Cache | None = None
    hidden_states: tuple[torch.FloatTensor] | None = None
    attentions: tuple[torch.FloatTensor] | None = None
    image_hidden_states: torch.FloatTensor | None = None

    video_hidden_states: torch.FloatTensor | None = None


@auto_docstring
class LlavaOnevisionPreTrainedModel(PreTrainedModel):
    config: LlavaOnevisionConfig
    base_model_prefix = "model"
    input_modalities = ("image", "video", "text")
    supports_gradient_checkpointing = True
    _no_split_modules = ["LlamaDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"

    _supports_flash_attn = True
    _supports_sdpa = True

    _can_compile_fullgraph = True
    _supports_flex_attn = True
    _supports_attention_backend = True

    @torch.no_grad()
    def _init_weights(self, module):
        std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)

        if isinstance(module, nn.Linear):
            init.normal_(module.weight, mean=0.0, std=std)
            if module.bias is not None:
                init.zeros_(module.bias)
        elif isinstance(module, LlavaOnevisionModel):
            embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
            init.normal_(module.image_newline, mean=0.0, std=embed_std)


class LlavaOnevisionMultiModalProjector(nn.Module):
    def __init__(self, config: LlavaOnevisionConfig):
        super().__init__()
        # We have hidden_size * the number of vision feature layers
        num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
        self.linear_1 = nn.Linear(
            config.vision_config.hidden_size * num_feature_layers,
            config.text_config.hidden_size,
            bias=config.multimodal_projector_bias,
        )
        self.act = ACT2FN[config.projector_hidden_act]
        self.linear_2 = nn.Linear(
            config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
        )

    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


def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
    """
    Calculate the shape of the image patch grid after the preprocessing for images of any resolution.

    Args:
        image_size (`tuple`):
            The size of the input image in the format (width, height).
        grid_pinpoints (`List`):
            A list containing possible resolutions. Each item in the list should be a tuple or list
            of the form `(height, width)`.
        patch_size (`int`):
            The size of each image patch.

    Returns:
        tuple: The shape of the image patch grid in the format (width, height).
    """
    if not isinstance(grid_pinpoints, list):
        raise TypeError("grid_pinpoints should be a list of tuples or lists")

    # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
    if not isinstance(image_size, (list, tuple)):
        if not isinstance(image_size, (torch.Tensor, np.ndarray)):
            raise TypeError(
                f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor"
            )
        image_size = image_size.tolist()

    height, width = select_best_resolution(image_size, grid_pinpoints)
    return height // patch_size, width // patch_size


def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int):
    """
    Calculate the number of patches after the preprocessing for images of any resolution.

    Args:
        image_size (`torch.LongTensor` or `np.ndarray` or `tuple[int, int]`):
            The size of the input image in the format (height, width). ?
        grid_pinpoints (`List`):
            A list containing possible resolutions. Each item in the list should be a tuple or list
            of the form `(height, width)`.
        patch_size (`int`):
            The size of each image patch.

    Returns:
        int: the number of patches
    """
    if not isinstance(grid_pinpoints, list):
        raise TypeError("grid_pinpoints should be a list of tuples or lists")

    # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
    if not isinstance(image_size, (list, tuple)):
        if not isinstance(image_size, (torch.Tensor, np.ndarray)):
            raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}")
        image_size = image_size.tolist()

    best_resolution = select_best_resolution(image_size, grid_pinpoints)
    height, width = best_resolution
    num_patches = 0
    # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1
    for i in range(0, height, patch_size):
        for j in range(0, width, patch_size):
            num_patches += 1
    # add the base patch
    num_patches += 1
    return num_patches


def unpad_image(tensor, original_size):
    """
    Unpads a PyTorch tensor of a padded and resized image.

    Args:
        tensor (`torch.Tensor`):
            The image tensor, assumed to be of shape (num_channels, height, width).
        original_size (`tuple`):
            The original size of the image (height, width).

    Returns:
        `torch.Tensor`: The unpadded image tensor.
    """
    if not isinstance(original_size, (list, tuple)):
        if not isinstance(original_size, (torch.Tensor, np.ndarray)):
            raise TypeError(
                f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor"
            )
        original_size = original_size.tolist()
    original_height, original_width = original_size
    current_height, current_width = tensor.shape[1:]

    original_aspect_ratio = original_width / original_height
    current_aspect_ratio = current_width / current_height

    if original_aspect_ratio > current_aspect_ratio:
        scale_factor = current_width / original_width
        new_height = int(round(original_height * scale_factor, 7))
        padding = (current_height - new_height) // 2
        unpadded_tensor = tensor[:, padding : current_height - padding, :]
    else:
        scale_factor = current_height / original_height
        new_width = int(round(original_width * scale_factor, 7))
        padding = (current_width - new_width) // 2
        unpadded_tensor = tensor[:, :, padding : current_width - padding]

    return unpadded_tensor


@auto_docstring(
    custom_intro="""
    The Llava-Next model which consists of a vision backbone and a language model without language modeling head.
    """
)
class LlavaOnevisionModel(LlavaOnevisionPreTrainedModel):
    _checkpoint_conversion_mapping = {
        r"^language_model.model": "language_model",
    }
    base_model_prefix = "model"

    def __init__(self, config):
        super().__init__(config)
        self.vision_tower = AutoModel.from_config(config.vision_config)

        self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
        embed_std = 1 / math.sqrt(config.text_config.hidden_size)
        self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std)

        self.vocab_size = config.text_config.vocab_size
        self.language_model = AutoModel.from_config(config.text_config)
        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 pack_image_features(self, image_features, image_sizes, image_newline=None, vision_aspect_ratio="anyres_max_9"):
        """
        Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.

        Args:
            image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`)
                List of image feature tensor, each contains all the visual feature of all patches.
            image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
                Actual image size of each images (H, W).
            image_newline (`torch.Tensor` of shape `(embed_dim)`)
                New line embedding vector.
            vision_aspect_ratio (`str`, *optional*, "anyres_max_9"):
                Aspect ratio used when processing image features. The default value is "anyres_max_9".
        Returns:
            image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`)
            feature_lens (`list[int]`)
                token length of each image in image_features
        """
        new_image_features = []
        feature_lens = []
        for image_idx, image_feature in enumerate(image_features):
            if image_feature.shape[0] > 1:
                base_image_feature = image_feature[0]
                image_feature = image_feature[1:]
                height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
                if height * width != base_image_feature.shape[0]:
                    raise ValueError("The number of patches is not consistent with the image size.")
                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
                    image_sizes[image_idx],
                    self.config.image_grid_pinpoints,
                    self.config.vision_config.image_size,
                )
                image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
                image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
                image_feature = image_feature.flatten(1, 2).flatten(2, 3)
                image_feature = unpad_image(image_feature, image_sizes[image_idx])
                max_num_patches = int(vision_aspect_ratio.strip("anyres_max_"))
                channels, curr_height, curr_width = image_feature.shape
                ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2))
                if ratio > 1.1:
                    image_feature = image_feature[None]
                    image_feature = nn.functional.interpolate(
                        image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear"
                    )[0]
                if image_newline is not None:
                    image_feature = torch.cat(
                        (
                            image_feature,
                            image_newline[:, None, None]
                            .expand(*image_feature.shape[:-1], 1)
                            .to(image_feature.device, image_feature.dtype),
                        ),
                        dim=-1,
                    )
                image_feature = image_feature.flatten(1, 2).transpose(0, 1)
                image_feature = torch.cat((base_image_feature, image_feature), dim=0)
            else:
                image_feature = image_feature[0]
                if image_newline is not None:
                    image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
            new_image_features.append(image_feature)
            feature_lens.append(image_feature.size(0))
        feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device)
        return new_image_features, feature_lens

    @can_return_tuple
    @merge_with_config_defaults
    @auto_docstring(
        custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection."
    )
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_sizes: torch.Tensor,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        vision_aspect_ratio: str | None = None,
        batch_num_images: torch.LongTensor | None = None,
        output_hidden_states: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        image_sizes (`torch.Tensor` of shape `(num_images, 2)`):
            Actual image size of each images (H, W).
        vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
            Aspect ratio used when processing image features. The default value is "anyres_max_9".
        batch_num_images (`torch.LongTensor`, *optional*):
            Number of images in each sample.
        """
        # ! infer image_num_patches from image_sizes
        if batch_num_images is None:
            # treat this as a single-image case for backward compatibility
            need_patching = [True] * len(image_sizes)
        else:
            need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
        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,
            )
            if should_patch
            else 1
            for imsize, should_patch in zip(image_sizes, need_patching)
        ]
        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_outputs = self.vision_tower(
            pixel_values,
            output_hidden_states=True,  # Ignore arg on purpose
            return_dict=True,
            **kwargs,
        )
        # If we have one vision feature layer, return the corresponding hidden states,
        # otherwise, select the hidden states of each feature layer and concatenate them
        if isinstance(vision_feature_layer, int):
            selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
        else:
            hs_pool = [image_outputs.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
            selected_image_feature = torch.cat(hs_pool, dim=-1)

        if vision_feature_select_strategy == "default":
            selected_image_feature = selected_image_feature[:, 1:]
        image_features = self.multi_modal_projector(selected_image_feature)
        image_features = torch.split(image_features, image_num_patches, dim=0)

        image_features, feature_lens = self.pack_image_features(
            image_features,
            image_sizes,
            image_newline=self.image_newline,
            vision_aspect_ratio=vision_aspect_ratio,
        )
        image_outputs.pooler_output = image_features

        return image_outputs

    def get_placeholder_mask(
        self,
        input_ids: torch.LongTensor,
        inputs_embeds: torch.FloatTensor,
        image_features: torch.FloatTensor | None = None,
        video_features: torch.FloatTensor | None = None,
    ):
        """
        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
        equal to the length of multimodal features. If the lengths are different, an error is raised.
        """
        if input_ids is None:
            special_image_mask = inputs_embeds == self.get_input_embeddings()(
                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
            )
            special_image_mask = special_image_mask.all(-1)
            special_video_mask = inputs_embeds == self.get_input_embeddings()(
                torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
            )
            special_video_mask = special_video_mask.all(-1)
        else:
            special_image_mask = input_ids == self.config.image_token_id
            special_video_mask = input_ids == self.config.video_token_id

        n_image_tokens = special_image_mask.sum()
        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
        if image_features is not None:
            torch_compilable_check(
                inputs_embeds[special_image_mask].numel() == image_features.numel(),
                f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
            )

        n_video_tokens = special_video_mask.sum()
        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
        if video_features is not None:
            torch_compilable_check(
                inputs_embeds[special_video_mask].numel() == video_features.numel(),
                f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
            )
        return special_image_mask, special_video_mask

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        image_sizes: torch.LongTensor | None = None,
        pixel_values_videos: torch.FloatTensor | None = None,
        image_sizes_videos: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        vision_aspect_ratio: str | None = None,
        batch_num_images: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple | LlavaOnevisionModelOutputWithPast:
        r"""
        image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*):
            The sizes of the videos in the batch, being (height, width) for each frame in the video.
        vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
            Aspect ratio used when processing image features. The default value is "anyres_max_9".
        batch_num_images (`torch.LongTensor`, *optional*):
            Number of images in each sample.
        """

        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

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

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

        # Images are processed with Anyres
        if pixel_values is not None:
            image_features = self.get_image_features(
                pixel_values,
                image_sizes,
                vision_feature_layer=vision_feature_layer,
                vision_feature_select_strategy=vision_feature_select_strategy,
                batch_num_images=batch_num_images,
                return_dict=True,
            ).pooler_output
            image_features = torch.cat(image_features, dim=0)
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            special_image_mask, _ = self.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, image_features=image_features
            )
            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)

        # Video are simply embedded and further pooled to decrease seq len
        if pixel_values_videos is not None:
            video_features = self.get_video_features(
                pixel_values_videos,
                vision_feature_layer=vision_feature_layer,
                vision_feature_select_strategy=vision_feature_select_strategy,
                return_dict=True,
            ).pooler_output
            image_newline = (
                self.image_newline[None, None, :].repeat(video_features.shape[0], 1, 1).to(video_features.device)
            )
            video_features = torch.cat((video_features, image_newline), dim=1)
            video_features = video_features.flatten(0, 1).to(inputs_embeds.device, inputs_embeds.dtype)
            _, special_video_mask = self.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, video_features=video_features
            )
            inputs_embeds = inputs_embeds.masked_scatter(special_video_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=True,
            cache_position=cache_position,
            **kwargs,
        )

        return LlavaOnevisionModelOutputWithPast(
            last_hidden_state=outputs.last_hidden_state,
            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,
        )

    @can_return_tuple
    @merge_with_config_defaults
    @auto_docstring(
        custom_intro="Obtains video last hidden states from the vision tower, apply multimodal projection and pooling."
    )
    def get_video_features(
        self,
        pixel_values: torch.FloatTensor,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        output_hidden_states: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
            The tensors corresponding to the input video.
        vision_feature_layer (`Union[int, list[int]], *optional*, defaults to -2`):
            The index of the layer to select the vision feature. If multiple indices are provided,
            the vision feature of the corresponding indices will be concatenated to form the
            vision features.
        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"`
        """
        batch_size, frames, channels, height, width = pixel_values.shape
        pixel_values = pixel_values.view(batch_size * frames, channels, height, width)
        vision_outputs = self.vision_tower(
            pixel_values,
            output_hidden_states=True,  # Ignore arg on purpose
            return_dict=True,
            **kwargs,
        )

        # If we have one vision feature layer, return the corresponding hidden states,
        # otherwise, select the hidden states of each feature layer and concatenate them
        if isinstance(vision_feature_layer, int):
            selected_video_feature = vision_outputs.hidden_states[vision_feature_layer]
        else:
            hs_pool = [vision_outputs.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
            selected_video_feature = torch.cat(hs_pool, dim=-1)

        if vision_feature_select_strategy == "default":
            selected_video_feature = selected_video_feature[:, 1:]
        video_features = self.multi_modal_projector(selected_video_feature)

        video_features = self.apply_pooling(video_features)
        video_features = video_features.reshape(batch_size, frames * video_features.shape[1], -1)
        vision_outputs.pooler_output = video_features

        return vision_outputs

    def apply_pooling(self, image_features):
        height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
        batch_frames, seq_len, dim = image_features.shape
        image_features = image_features.view(batch_frames, height, width, -1)
        image_features = image_features.permute(0, 3, 1, 2).contiguous()

        height, width = image_features.shape[2:]
        scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)]
        image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear")

        image_features = image_features.permute(0, 2, 3, 1)
        image_features = image_features.view(batch_frames, -1, dim)
        return image_features


@auto_docstring(
    custom_intro="""
    The LLAVA-NeXT model which consists of a vision backbone and a language model.
    """
)
class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel, GenerationMixin):
    _checkpoint_conversion_mapping = {
        r"^language_model.model": "model.language_model",
        r"^vision_tower": "model.vision_tower",
        r"^multi_modal_projector": "model.multi_modal_projector",
        r"^image_newline": "model.image_newline",
        r"^language_model.lm_head": "lm_head",
    }
    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}

    def __init__(self, config: LlavaOnevisionConfig):
        super().__init__(config)
        self.model = LlavaOnevisionModel(config)
        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
        self.post_init()

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

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

    def get_output_embeddings(self) -> nn.Module:
        return self.lm_head

    def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
        return self.model.pack_image_features(
            image_features=image_features,
            image_sizes=image_sizes,
            vision_feature_select_strategy=vision_feature_select_strategy,
            image_newline=image_newline,
        )

    @auto_docstring
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_sizes: torch.Tensor,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        vision_aspect_ratio: str | None = None,
        batch_num_images: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        image_sizes (`torch.Tensor` of shape `(num_images, 2)`):
            Actual image size of each images (H, W).
        vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
            Aspect ratio used when processing image features. The default value is "anyres_max_9".
        batch_num_images (`torch.LongTensor`, *optional*):
            Number of images in each sample.
        """
        return self.model.get_image_features(
            pixel_values=pixel_values,
            image_sizes=image_sizes,
            vision_feature_layer=vision_feature_layer,
            vision_feature_select_strategy=vision_feature_select_strategy,
            vision_aspect_ratio=vision_aspect_ratio,
            batch_num_images=batch_num_images,
            **kwargs,
        )

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        image_sizes: torch.LongTensor | None = None,
        pixel_values_videos: torch.FloatTensor | None = None,
        image_sizes_videos: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        vision_aspect_ratio: str | None = None,
        batch_num_images: torch.LongTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | LlavaOnevisionCausalLMOutputWithPast:
        r"""
        image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*):
            The sizes of the videos in the batch, being (height, width) for each frame in the video.
        vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
            Aspect ratio used when processing image features. The default value is "anyres_max_9".
        batch_num_images (`torch.LongTensor`, *optional*):
            Number of images in each sample.
        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]`.

        Example:

        ```python
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO
        >>> import torch
        >>> from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration

        >>> model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", dtype="float16", device_map="cuda:0")
        >>> processor = LlavaOnevisionProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")

        >>> conversation = [
        ...     {
        ...       "role": "user",
        ...       "content": [
        ...           {"type": "text", "text": "What is shown in this image?"},
        ...           {"type": "image"},
        ...         ],
        ...     },
        ... ]
        >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))
        >>> inputs = processor(text=prompt, images=image, return_tensors='pt').to(0, torch.float16)

        >>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        >>> processor.batch_decode(output, skip_special_tokens=True)[0]
        "user\n\nWhat is shown in this image?\nassistant\ncat"
        ```"""
        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

        outputs = self.model(
            input_ids=input_ids,
            pixel_values=pixel_values,
            pixel_values_videos=pixel_values_videos,
            image_sizes=image_sizes,
            image_sizes_videos=image_sizes_videos,
            vision_aspect_ratio=vision_aspect_ratio,
            vision_feature_layer=vision_feature_layer,
            vision_feature_select_strategy=vision_feature_select_strategy,
            batch_num_images=batch_num_images,
            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=True,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            **kwargs,
        )

        hidden_states = outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(
                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
            )

        return LlavaOnevisionCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=outputs.image_hidden_states,
            video_hidden_states=outputs.video_hidden_states,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        pixel_values=None,
        image_sizes=None,
        pixel_values_videos=None,
        image_sizes_videos=None,
        attention_mask=None,
        cache_position=None,
        logits_to_keep=None,
        is_first_iteration=False,
        **kwargs,
    ):
        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model

        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            is_first_iteration=is_first_iteration,
            **kwargs,
        )

        if is_first_iteration or not kwargs.get("use_cache", True):
            # Pixel values are used only in the first iteration if available
            # In subsequent iterations, they are already merged with text and cached
            # NOTE: first iteration doesn't have to be prefill, it can be the first
            # iteration with a question and cached system prompt (continue generate from cache)
            model_inputs["pixel_values"] = pixel_values
            model_inputs["image_sizes"] = image_sizes
            model_inputs["pixel_values_videos"] = pixel_values_videos
            model_inputs["image_sizes_videos"] = image_sizes_videos

        return model_inputs

    @auto_docstring
    def get_video_features(
        self,
        pixel_values: torch.FloatTensor,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
            The tensors corresponding to the input video.
        vision_feature_layer (`Union[int, list[int]]`, *optional;*):
            The index of the layer to select the vision feature. If multiple indices are provided,
            the vision feature of the corresponding indices will be concatenated to form the
            vision features.
        vision_feature_select_strategy (`str`, *optional*):
            The feature selection strategy used to select the vision feature from the vision backbone.
            Can be one of `"default"` or `"full"`
        """
        return self.model.get_video_features(
            pixel_values=pixel_values,
            vision_feature_layer=vision_feature_layer,
            vision_feature_select_strategy=vision_feature_select_strategy,
            **kwargs,
        )


__all__ = ["LlavaOnevisionModel", "LlavaOnevisionForConditionalGeneration", "LlavaOnevisionPreTrainedModel"]