# coding=utf-8
# Copyright 2025 Apple Inc. and The HuggingFace 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.

"""Pytorch implementation of AIMv2 Model"""

import math
from typing import Optional

import torch
import torch.nn.functional as F
from torch import nn

from ...masking_utils import create_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import (
    TransformersKwargs,
    auto_docstring,
    can_return_tuple,
)
from ...utils.deprecation import deprecate_kwarg
from ...utils.generic import check_model_inputs
from ..clip.modeling_clip import CLIPModel, CLIPTextEmbeddings, _get_vector_norm
from ..llama.modeling_llama import LlamaMLP, LlamaRMSNorm
from ..siglip.configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
from ..siglip.modeling_siglip import SiglipAttention, SiglipEncoder, SiglipOutput


class Aimv2VisionConfig(SiglipVisionConfig):
    r"""
    This is the configuration class to store the configuration of a [`Aimv2VisionModel`]. It is used to instantiate a
    AIMv2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the vision encoder of the AIMv2
    [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224) architecture.

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 2816):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_channels (`int`, *optional*, defaults to 3):
            Number of channels in the input images.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 14):
            The size (resolution) of each patch.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        qkv_bias (`bool`, *optional*, defaults to `False`):
            Whether to add a bias to the queries, keys and values.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to add a bias to the Linear layers or Not.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the for initializing all weight matrices.
        use_head (`str`, *optional*, defaults to `True`):
            Whether to use Attention Pooling Head or Not.
        is_native (`str`, *optional*, defaults to `False`):
            Whether to use ckpt trained for image native resolution or not.
    Example:

    ```python
    >>> from transformers import SiglipVisionConfig, SiglipVisionModel

    >>> # Initializing a Aimv2VisionConfig with apple/aimv2-large-patch14-224 style configuration
    >>> configuration = Aimv2VisionConfig()

    >>> # Initializing a Aimv2VisionModel (with random weights) from the apple/aimv2-large-patch14-224 style configuration
    >>> model = Aimv2VisionModel(configuration)

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

    def __init__(
        self,
        hidden_size: int = 1024,
        intermediate_size: int = 2816,
        num_hidden_layers: int = 24,
        num_attention_heads: int = 8,
        num_channels: int = 3,
        image_size: int = 224,
        patch_size: int = 14,
        rms_norm_eps: float = 1e-5,
        attention_dropout: float = 0.0,
        qkv_bias: bool = False,
        mlp_bias: bool = False,
        hidden_act: str = "silu",
        initializer_range: float = 0.02,
        use_head: bool = True,
        is_native: bool = False,
        **kwargs,
    ):
        super().__init__(
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            hidden_act=hidden_act,
            num_channels=num_channels,
            image_size=image_size,
            patch_size=patch_size,
            qkv_bias=qkv_bias,
            **kwargs,
        )

        self.use_head = use_head
        self.initializer_range = initializer_range
        self.attention_dropout = attention_dropout
        self.mlp_bias = mlp_bias
        self.qkv_bias = qkv_bias
        self.rms_norm_eps = rms_norm_eps
        self.is_native = is_native

        del self.layer_norm_eps


class Aimv2TextConfig(SiglipTextConfig):
    r"""
    This is the configuration class to store the configuration of a [`Aimv2TextModel`]. It is used to instantiate a
    AIMv2 text encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the text encoder of the AIMv2
    [apple/aimv2-large-patch14-224-lit](https://huggingface.co/apple/aimv2-large-patch14-224-lit) architecture.

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 49408):
            Vocabulary size of the AIMv2 text model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`Aimv2Model`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 2048):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 6):
            Number of attention heads for each attention layer in the Transformer encoder.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        qkv_bias (`bool`, *optional*, defaults to `False`):
            Whether to add a bias to the queries, keys and values.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to add a bias to the Linear layers or Not.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
        pad_token_id (`int`, *optional*, defaults to 1):
            The id of the padding token in the vocabulary.
        bos_token_id (`int`, *optional*, defaults to 49406):
            The id of the beginning-of-sequence token in the vocabulary.
        eos_token_id (`int`, *optional*, defaults to 49407):
            The id of the end-of-sequence token in the vocabulary.
        max_position_embeddings (`int`, *optional*, defaults to 77):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the for initializing all weight matrices.
    """

    def __init__(
        self,
        vocab_size: int = 49408,
        hidden_size: int = 768,
        intermediate_size: int = 2048,
        num_hidden_layers: int = 12,
        num_attention_heads: int = 6,
        rms_norm_eps: float = 1e-5,
        attention_dropout: float = 0.0,
        qkv_bias: bool = False,
        mlp_bias: bool = False,
        hidden_act: str = "silu",
        pad_token_id: Optional[int] = None,
        bos_token_id: Optional[int] = None,
        eos_token_id: int = 49407,
        max_position_embeddings: int = 77,
        initializer_range: bool = 0.02,
        **kwargs,
    ):
        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            hidden_act=hidden_act,
            max_position_embeddings=max_position_embeddings,
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **kwargs,
        )

        self.initializer_range = initializer_range
        self.attention_dropout = attention_dropout
        self.mlp_bias = mlp_bias
        self.qkv_bias = qkv_bias
        self.rms_norm_eps = rms_norm_eps

        del self.bos_token_id
        del self.pad_token_id
        del self.projection_size
        del self.layer_norm_eps


class Aimv2Config(SiglipConfig):
    r"""
    [`Aimv2Config`] is the configuration class to store the configuration of a [`Aimv2Model`]. It is used to
    instantiate a AIMv2 model according to the specified arguments, defining the text model and vision model configs.
    Instantiating a configuration with the defaults will yield a similar configuration to that of the AIMv2
    [apple/aimv2-large-patch14-224-lit](https://huggingface.co/apple/aimv2-large-patch14-224-lit) architecture.

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        text_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`Aimv2TextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`Aimv2VisionConfig`].
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and vision projection layers.
        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
            The initial value of the *logit_scale* parameter.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import Aimv2Config, Aimv2Model

    >>> # Initializing a Aimv2Config with apple/aimv2-large-patch14-224-lit style configuration
    >>> configuration = Aimv2Config()

    >>> # Initializing a Aimv2Model (with random weights) from the apple/aimv2-large-patch14-224-lit style configuration
    >>> model = Aimv2Model(configuration)

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

    >>> # We can also initialize a Aimv2Config from a Aimv2TextConfig and a Aimv2VisionConfig
    >>> from transformers import Aimv2TextConfig, Aimv2VisionConfig

    >>> # Initializing a AIMv2Text and AIMv2Vision configuration
    >>> config_text = Aimv2TextConfig()
    >>> config_vision = Aimv2VisionConfig()

    >>> config = Aimv2Config(text_config=config_text, vision_config=config_vision)
    ```"""

    def __init__(
        self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs
    ):
        self.projection_dim = projection_dim
        self.logit_scale_init_value = logit_scale_init_value
        self.max_logit_scale = 100.0
        super().__init__(text_config, vision_config, **kwargs)

        del self.initializer_factor


class Aimv2Output(SiglipOutput):
    pass


class Aimv2RMSNorm(LlamaRMSNorm):
    pass


class Aimv2MLP(LlamaMLP):
    pass


class Aimv2VisionEmbeddings(nn.Module):
    def __init__(self, config: Aimv2VisionConfig):
        super().__init__()
        self.config = config
        self.patch_size = config.patch_size
        self.patch_embed = nn.Conv2d(
            config.num_channels, config.hidden_size, kernel_size=config.patch_size, stride=config.patch_size
        )
        self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)

        num_patches = (config.image_size // config.patch_size) ** 2
        if not self.config.is_native:
            self.position_embedding = nn.Embedding(num_patches, config.hidden_size)
        self.register_buffer("position_ids", torch.arange(num_patches).expand((1, -1)), persistent=False)

    @staticmethod
    def build_2d_sincos_position_embedding(
        height, width, embed_dim=256, temperature=10000.0, device="cpu", dtype=torch.float32
    ) -> torch.Tensor:
        grid_w = torch.arange(int(width), dtype=dtype, device=device)
        grid_h = torch.arange(int(height), dtype=dtype, device=device)
        grid_h, grid_w = torch.meshgrid(grid_w, grid_h, indexing="xy")

        pos_dim = embed_dim // 4
        omega = torch.arange(pos_dim, dtype=dtype, device=device) / pos_dim
        omega = 1.0 / (temperature**omega)

        out_h = grid_h.flatten()[..., None] @ omega[None, :]
        out_w = grid_w.flatten()[..., None] @ omega[None, :]

        return torch.concat([out_h.sin(), out_h.cos(), out_w.sin(), out_w.cos()], dim=1)[None, :, :]

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        _, _, height, width = pixel_values.size()
        hidden_states = self.patch_embed(pixel_values).flatten(2).transpose(1, 2)
        hidden_states = self.rms_norm(hidden_states)

        if self.config.is_native:
            pos_embed = self.build_2d_sincos_position_embedding(
                height // self.patch_size,
                width // self.patch_size,
                embed_dim=self.config.hidden_size,
                device=hidden_states.device,
                dtype=hidden_states.dtype,
            )
        else:
            pos_embed = self.position_embedding(self.position_ids)

        hidden_states = hidden_states + pos_embed
        return hidden_states


class Aimv2TextEmbeddings(CLIPTextEmbeddings):
    pass


class Aimv2Attention(SiglipAttention):
    def __init__(self, config):
        super().__init__(config)
        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)


class Aimv2EncoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: Aimv2VisionConfig):
        super().__init__()
        self.attention = Aimv2Attention(config)
        self.ffn = Aimv2MLP(config)
        self.rms_norm1 = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)
        self.rms_norm2 = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        norm_hidden_states = self.rms_norm1(hidden_states)
        attn_output, _ = self.attention(hidden_states=norm_hidden_states, attention_mask=attention_mask, **kwargs)

        hidden_states = hidden_states + attn_output
        norm_hidden_states = self.rms_norm2(hidden_states)
        mlp_output = self.ffn(norm_hidden_states)

        hidden_states = hidden_states + mlp_output
        return hidden_states


class Aimv2Encoder(SiglipEncoder):
    pass


class Aimv2AttentionPoolingHead(nn.Module):
    def __init__(self, config: Aimv2VisionConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads

        self.k_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.qkv_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.qkv_bias)

        self.cls_token = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
        self.output_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, seq_len, hidden_dim = hidden_states.shape

        cls_token = self.cls_token.expand(batch_size, -1, -1)

        key = self.k_proj(hidden_states).reshape(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads)
        value = self.v_proj(hidden_states).reshape(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads)
        query = cls_token.reshape(batch_size, 1, self.num_heads, hidden_dim // self.num_heads)

        key = key.permute(0, 2, 1, 3)
        value = value.permute(0, 2, 1, 3)
        query = query.permute(0, 2, 1, 3)

        attn_output = F.scaled_dot_product_attention(query, key, value)

        attn_output = attn_output.transpose(1, 2).reshape(batch_size, 1, hidden_dim)
        attn_output = attn_output.mean(dim=1)

        output = self.output_proj(attn_output)
        return output


@auto_docstring
class Aimv2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models. The model is only intended for inference and doesn't support finetuning.
    """

    config: Aimv2Config
    base_model_prefix = "aimv2"
    input_modalities = "image"
    supports_gradient_checkpointing = True
    _no_split_modules = [
        "Aimv2EncoderLayer",
        "Aimv2AttentionPoolingHead",
        "Aimv2VisionEmbeddings",
        "Aimv2TextEmbeddings",
    ]
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True

    def _init_weights(self, module):
        super()._init_weights(module)
        if hasattr(module, "logit_scale"):
            if isinstance(module.logit_scale, nn.Parameter):
                module.logit_scale.data.fill_(math.log(1 / 0.07))
        elif isinstance(module, Aimv2AttentionPoolingHead):
            module.cls_token.data.normal_(mean=0.0, std=self.config.initializer_range)


@auto_docstring(
    custom_intro="""
    The Vision model from AIMv2 without any head or projection on top.
    """
)
class Aimv2VisionModel(Aimv2PreTrainedModel):
    config: Aimv2VisionConfig
    main_input_name = "pixel_values"
    _can_record_outputs = {
        "hidden_states": Aimv2EncoderLayer,
        "attentions": Aimv2Attention,
    }

    def __init__(self, config: Aimv2VisionConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = Aimv2VisionEmbeddings(config)
        self.encoder = Aimv2Encoder(config)
        # The only change from SiglipVisionTransformer is, layernorm -> rms_norm.
        self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)

        self.use_head = config.use_head
        if self.use_head:
            self.head = Aimv2AttentionPoolingHead(config)

        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.embeddings.patch_embed

    @deprecate_kwarg("attention_mask", version="v4.58.0")
    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        pixel_values,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        r"""
        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, Siglip2VisionModel

        >>> model = Aimv2VisionModel.from_pretrained("apple/aimv2-large-patch14-native")
        >>> processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-native")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> last_hidden_state = outputs.last_hidden_state
        >>> pooled_output = outputs.pooler_output  # pooled features
        ```"""
        hidden_states = self.embeddings(pixel_values)

        encoder_outputs: BaseModelOutput = self.encoder(
            inputs_embeds=hidden_states,
            **kwargs,
        )

        last_hidden_state = encoder_outputs.last_hidden_state
        last_hidden_state = self.rms_norm(last_hidden_state)

        pooler_output = self.head(last_hidden_state) if self.use_head else None

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooler_output,
        )


@auto_docstring(
    custom_intro="""
    The text model from AIMv2 without any head or projection on top.
    """
)
class Aimv2TextModel(Aimv2PreTrainedModel):
    main_input_name = "input_ids"

    _can_record_outputs = {
        "hidden_states": Aimv2EncoderLayer,
        "attentions": Aimv2Attention,
    }

    def __init__(self, config: Aimv2TextConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = Aimv2TextEmbeddings(config)
        self.encoder = Aimv2Encoder(config)
        self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)

        self.eos_token_id = config.eos_token_id

        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.embeddings.token_embedding

    def set_input_embeddings(self, value):
        self.embeddings.token_embedding = value

    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        input_ids,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        hidden_states = self.embeddings(input_ids)
        batch_size, seq_len, _ = hidden_states.shape

        cache_position = torch.arange(seq_len, dtype=torch.long, device=hidden_states.device)
        position_ids = cache_position.unsqueeze(0).expand(batch_size, -1)
        if attention_mask is not None:
            attention_mask = create_causal_mask(
                config=self.config,
                input_embeds=hidden_states,
                position_ids=position_ids,
                attention_mask=attention_mask,
                cache_position=cache_position,
                past_key_values=None,
            )

        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            attention_mask=attention_mask,
            **kwargs,
        )

        last_hidden_state = encoder_outputs.last_hidden_state
        last_hidden_state = self.rms_norm(last_hidden_state)

        # Get pooled output
        pooled_output = last_hidden_state[
            torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
            (input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id).int().argmax(dim=-1),
        ]

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
        )


@auto_docstring
class Aimv2Model(CLIPModel):
    _supports_flash_attn = True

    def __init__(self, config: Aimv2Config):
        PreTrainedModel.__init__(self, config)

        self.projection_dim = config.projection_dim
        self.vision_embed_dim = config.vision_config.hidden_size
        self.text_embed_dim = config.text_config.hidden_size

        self.vision_model = Aimv2VisionModel._from_config(config.vision_config)
        self.text_model = Aimv2TextModel._from_config(config.text_config)

        self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
        self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)

        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
        self.max_log_logit_scale = math.log(config.max_logit_scale)

        self.post_init()

    @auto_docstring
    @can_return_tuple
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Aimv2Output:
        r"""
        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, Aimv2Model

        >>> model = Aimv2Model.from_pretrained("apple/aimv2-large-patch14-224-lit")
        >>> processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-224-lit")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(
        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
        ... )

        >>> outputs = model(**inputs)
        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
        >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
        ```"""
        vision_outputs: BaseModelOutputWithPooling = self.vision_model(
            pixel_values=pixel_values,
            **kwargs,
        )

        text_outputs: BaseModelOutputWithPooling = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            **kwargs,
        )

        image_embeds = vision_outputs.pooler_output
        image_embeds = self.visual_projection(image_embeds)

        text_embeds = text_outputs.pooler_output
        text_embeds = self.text_projection(text_embeds)

        # normalized features
        image_embeds = image_embeds / _get_vector_norm(image_embeds)
        text_embeds = text_embeds / _get_vector_norm(text_embeds)

        logit_scale = self.logit_scale.clamp(0.0, self.max_log_logit_scale).exp().to(text_embeds.device)
        logits_per_text = (logit_scale * text_embeds) @ image_embeds.t()
        logits_per_image = logits_per_text.t()

        return Aimv2Output(
            logits_per_image=logits_per_image,
            logits_per_text=logits_per_text,
            text_embeds=text_embeds,
            image_embeds=image_embeds,
            text_model_output=text_outputs,
            vision_model_output=vision_outputs,
        )


__all__ = [
    "Aimv2Config",
    "Aimv2VisionConfig",
    "Aimv2TextConfig",
    "Aimv2VisionModel",
    "Aimv2Model",
    "Aimv2PreTrainedModel",
    "Aimv2TextModel",
]
