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# 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.


import math
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any

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

from ... import initialization as init
from ...activations import ACT2FN
from ...integrations import use_kernel_forward_from_hub
from ...masking_utils import create_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.generic import merge_with_config_defaults
from ...utils.output_capturing import capture_outputs
from .configuration_aimv2 import Aimv2Config, Aimv2TextConfig, Aimv2VisionConfig


@auto_docstring
@dataclass
class Aimv2Output(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
        Contrastive loss for image-text similarity.
    logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
        The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
        similarity scores.
    logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
        The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
        similarity scores.
    text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
        The text embeddings obtained by applying the projection layer to the pooled output of [`Aimv2TextModel`].
    image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
        The image embeddings obtained by applying the projection layer to the pooled output of [`Aimv2VisionModel`].
    text_model_output (`BaseModelOutputWithPooling`):
        The output of the [`Aimv2TextModel`].
    vision_model_output (`BaseModelOutputWithPooling`):
        The output of the [`Aimv2VisionModel`].
    """

    loss: torch.FloatTensor | None = None
    logits_per_image: torch.FloatTensor | None = None
    logits_per_text: torch.FloatTensor | None = None
    text_embeds: torch.FloatTensor | None = None
    image_embeds: torch.FloatTensor | None = None
    text_model_output: BaseModelOutputWithPooling = None
    vision_model_output: BaseModelOutputWithPooling = None

    def to_tuple(self) -> tuple[Any]:
        return tuple(v.to_tuple() if isinstance(v, ModelOutput) else v for v in self.values())


@use_kernel_forward_from_hub("RMSNorm")
class Aimv2RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
        """
        Aimv2RMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class Aimv2MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


def build_2d_sinusoidal_position_embedding(
    height: int,
    width: int,
    embed_dim: int = 256,
    temperature: float = 10000.0,
    cls_token: bool = False,
    device: torch.device | None = None,
    dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
    """2D sinusoidal position embeddings for an image patch grid.

    Each (h, w) position gets an ``embed_dim``-dimensional vector laid out as
    ``[sin_h | cos_h | sin_w | cos_w]``, with row-major (H-outer) patch ordering.

    Args:
        height: Grid height in patches.
        width: Grid width in patches.
        embed_dim: Total embedding dimension; must be divisible by 4.
        temperature: Base for the frequency decay.
        cls_token: If `True`, prepend a zero row for a CLS token.
        device: Target device; defaults to CPU.
        dtype: Output dtype; frequency arithmetic uses float64 internally.

    Returns:
        Tensor of shape ``(height * width [+1], embed_dim)``.
    """
    if embed_dim % 4 != 0:
        raise ValueError(f"`embed_dim` must be divisible by 4, got {embed_dim}")

    pos_dim = embed_dim // 4
    omega = torch.arange(pos_dim, dtype=torch.float64, device=device) / pos_dim
    omega = 1.0 / temperature**omega  # (D/4,)

    grid_h = torch.arange(height, dtype=torch.float64, device=device)
    grid_w = torch.arange(width, dtype=torch.float64, device=device)
    grid_h, grid_w = torch.meshgrid(grid_h, grid_w, indexing="ij")  # (H, W) each

    emb_h = grid_h.flatten().outer(omega)  # (H*W, D/4)
    emb_w = grid_w.flatten().outer(omega)  # (H*W, D/4)

    pos_embed = torch.cat([emb_h.sin(), emb_h.cos(), emb_w.sin(), emb_w.cos()], dim=1)

    if cls_token:
        pos_embed = torch.cat([torch.zeros(1, embed_dim, dtype=torch.float64, device=device), pos_embed], dim=0)

    return pos_embed.to(dtype)


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)

    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 = build_2d_sinusoidal_position_embedding(
                height=height // self.patch_size,
                width=width // self.patch_size,
                embed_dim=self.config.hidden_size,
                device=hidden_states.device,
                dtype=hidden_states.dtype,
            )
            # AIMv2 was trained with [sin_w|cos_w|sin_h|cos_h] layout (matching ViT-MAE's
            # original naming-bug convention); rotate the canonical h-first embedding to match.
            half = pos_embed.shape[-1] // 2
            pos_embed = torch.cat([pos_embed[..., half:], pos_embed[..., :half]], dim=-1)
            pos_embed = pos_embed.unsqueeze(0)
        else:
            pos_embed = self.position_embedding(self.position_ids)

        hidden_states = hidden_states + pos_embed
        return hidden_states


class Aimv2TextEmbeddings(nn.Module):
    def __init__(self, config: Aimv2TextConfig):
        super().__init__()
        embed_dim = config.hidden_size

        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
        )

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
    ) -> torch.Tensor:
        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
        max_position_embedding = self.position_embedding.weight.shape[0]

        if seq_length > max_position_embedding:
            raise ValueError(
                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
            )

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

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

        position_embeddings = self.position_embedding(position_ids)
        embeddings = inputs_embeds + position_embeddings

        return embeddings


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)

    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class Aimv2Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads})."
            )
        self.scale = self.head_dim**-0.5
        self.dropout = config.attention_dropout
        self.is_causal = False
        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)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        **kwargs,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        """Input shape: Batch x Time x Channel"""

        input_shape = hidden_states.shape[:-1]

        hidden_shape = (*input_shape, -1, self.head_dim)
        queries = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        keys = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        values = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            queries,
            keys,
            values,
            attention_mask,
            is_causal=self.is_causal,
            scaling=self.scale,
            dropout=0.0 if not self.training else self.dropout,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights


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: torch.Tensor | None = 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(nn.Module):
    """
    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
    [`Aimv2EncoderLayer`].

    Args:
        config: Aimv2Config
    """

    def __init__(self, config: Aimv2Config):
        super().__init__()
        self.config = config
        self.layers = nn.ModuleList([Aimv2EncoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    # Ignore copy
    @auto_docstring
    def forward(
        self,
        inputs_embeds,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutput:
        hidden_states = inputs_embeds
        for encoder_layer in self.layers:
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask,
                **kwargs,
            )

        return BaseModelOutput(last_hidden_state=hidden_states)


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

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if hasattr(module, "logit_scale"):
            if isinstance(module.logit_scale, nn.Parameter):
                init.constant_(module.logit_scale, math.log(1 / 0.07))
        elif isinstance(module, Aimv2AttentionPoolingHead):
            init.normal_(module.cls_token, mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, Aimv2VisionEmbeddings):
            init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
        elif isinstance(module, Aimv2TextEmbeddings):
            init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))


@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

    @merge_with_config_defaults
    @capture_outputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        pixel_values,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        r"""
        Examples:

        ```python
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO
        >>> 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"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))

        >>> 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

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

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


def _get_vector_norm(tensor: torch.Tensor) -> torch.Tensor:
    """
    This method is equivalent to tensor.norm(p=2, dim=-1, keepdim=True) and used to make
    model `executorch` exportable. See issue https://github.com/pytorch/executorch/issues/3566
    """
    square_tensor = torch.pow(tensor, 2)
    sum_tensor = torch.sum(square_tensor, dim=-1, keepdim=True)
    normed_tensor = torch.pow(sum_tensor, 0.5)
    return normed_tensor


@auto_docstring
class Aimv2Model(Aimv2PreTrainedModel):
    _supports_flash_attn = True

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

    @can_return_tuple
    @auto_docstring
    def get_text_features(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        Examples:

        ```python
        >>> import torch
        >>> from transformers import AutoTokenizer, Aimv2Model

        >>> model = Aimv2Model.from_pretrained("openai/aimv2-vit-base-patch32")
        >>> tokenizer = AutoTokenizer.from_pretrained("openai/aimv2-vit-base-patch32")

        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")

        >>> with torch.inference_mode():
        ...     text_features = model.get_text_features(**inputs)
        ```"""
        text_outputs: BaseModelOutputWithPooling = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            return_dict=True,
            **kwargs,
        )
        pooled_output = text_outputs.pooler_output
        text_outputs.pooler_output = self.text_projection(pooled_output)

        return text_outputs

    @can_return_tuple
    @auto_docstring
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        interpolate_pos_encoding: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        Examples:

        ```python
        >>> import torch
        >>> from transformers import AutoProcessor, Aimv2Model
        >>> from transformers.image_utils import load_image

        >>> model = Aimv2Model.from_pretrained("openai/aimv2-vit-base-patch32")
        >>> processor = AutoProcessor.from_pretrained("openai/aimv2-vit-base-patch32")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = load_image(url)

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

        >>> with torch.inference_mode():
        ...     image_features = model.get_image_features(**inputs)
        ```"""
        vision_outputs: BaseModelOutputWithPooling = self.vision_model(
            pixel_values=pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
            return_dict=True,
            **kwargs,
        )
        pooled_output = vision_outputs.pooler_output
        vision_outputs.pooler_output = self.visual_projection(pooled_output)

        return vision_outputs

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

        ```python
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO
        >>> 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"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))

        >>> 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__ = ["Aimv2VisionModel", "Aimv2Model", "Aimv2PreTrainedModel", "Aimv2TextModel"]
