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#                          modular_metaclip_2.py file directly. One of our CI enforces this.
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from collections.abc import Callable
from dataclasses import dataclass
from typing import Any, Optional, Union

import torch
from torch import nn

from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import (
    ModelOutput,
    TransformersKwargs,
    auto_docstring,
    can_return_tuple,
    filter_out_non_signature_kwargs,
    torch_int,
)
from ...utils.generic import check_model_inputs
from .configuration_metaclip_2 import MetaClip2Config, MetaClip2TextConfig, MetaClip2VisionConfig


class MetaClip2TextEmbeddings(nn.Module):
    def __init__(self, config: MetaClip2TextConfig):
        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: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = 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


class MetaClip2VisionEmbeddings(nn.Module):
    def __init__(self, config: MetaClip2VisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))

        self.patch_embedding = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            bias=False,
        )

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1
        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing.

        Adapted from:
        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
        """

        num_patches = embeddings.shape[1] - 1
        position_embedding = self.position_embedding.weight.unsqueeze(0)
        num_positions = position_embedding.shape[1] - 1

        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
            return self.position_embedding(self.position_ids)

        class_pos_embed = position_embedding[:, :1]
        patch_pos_embed = position_embedding[:, 1:]

        dim = embeddings.shape[-1]

        new_height = height // self.patch_size
        new_width = width // self.patch_size

        sqrt_num_positions = torch_int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(new_height, new_width),
            mode="bicubic",
            align_corners=False,
        )

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)

        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)

    def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
        batch_size, _, height, width = pixel_values.shape
        if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})."
            )
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))  # shape = [*, width, grid, grid]
        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
        if interpolate_pos_encoding:
            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
        else:
            embeddings = embeddings + self.position_embedding(self.position_ids)
        return embeddings


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    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 MetaClip2Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Union[MetaClip2VisionConfig, MetaClip2TextConfig]):
        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)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)

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

        batch_size, seq_length, embed_dim = hidden_states.shape

        queries = self.q_proj(hidden_states)
        keys = self.k_proj(hidden_states)
        values = self.v_proj(hidden_states)

        queries = queries.view(batch_size, seq_length, -1, self.head_dim).transpose(1, 2)
        keys = keys.view(batch_size, seq_length, -1, self.head_dim).transpose(1, 2)
        values = values.view(batch_size, seq_length, -1, self.head_dim).transpose(1, 2)
        # METACLIP_2 text model uses both `causal_attention_mask` and `attention_mask`
        # in case FA2 kernel is called, `is_causal` should be inferred from `causal_attention_mask`
        if self.config._attn_implementation == "flash_attention_2":
            self.is_causal = causal_attention_mask is not None
        else:
            if attention_mask is not None and causal_attention_mask is not None:
                attention_mask = attention_mask + causal_attention_mask
            elif causal_attention_mask is not None:
                attention_mask = causal_attention_mask

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        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,
            **kwargs,
        )

        attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights


class MetaClip2MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.activation_fn = ACT2FN[config.hidden_act]
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states


class MetaClip2EncoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: Union[MetaClip2VisionConfig, MetaClip2TextConfig]):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.self_attn = MetaClip2Attention(config)
        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = MetaClip2MLP(config)
        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        causal_attention_mask: torch.Tensor,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.FloatTensor:
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            causal_attention_mask=causal_attention_mask,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states


@auto_docstring
class MetaClip2PreTrainedModel(PreTrainedModel):
    config: MetaClip2Config
    base_model_prefix = "metaclip_2"
    input_modalities = ["image", "text"]
    supports_gradient_checkpointing = True
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": MetaClip2EncoderLayer,
        "attentions": MetaClip2Attention,
    }

    def _init_weights(self, module):
        """Initialize the weights"""
        factor = self.config.initializer_factor
        if isinstance(module, MetaClip2TextEmbeddings):
            module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
            module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
        elif isinstance(module, MetaClip2VisionEmbeddings):
            factor = self.config.initializer_factor
            nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
            nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
            nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
        elif isinstance(module, MetaClip2Attention):
            factor = self.config.initializer_factor
            in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
            out_proj_std = (module.embed_dim**-0.5) * factor
            nn.init.normal_(module.q_proj.weight, std=in_proj_std)
            nn.init.normal_(module.k_proj.weight, std=in_proj_std)
            nn.init.normal_(module.v_proj.weight, std=in_proj_std)
            nn.init.normal_(module.out_proj.weight, std=out_proj_std)
        elif isinstance(module, MetaClip2MLP):
            factor = self.config.initializer_factor
            in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
            fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
            nn.init.normal_(module.fc1.weight, std=fc_std)
            nn.init.normal_(module.fc2.weight, std=in_proj_std)
        elif isinstance(module, MetaClip2Model):
            nn.init.normal_(
                module.text_projection.weight,
                std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
            )
            nn.init.normal_(
                module.visual_projection.weight,
                std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
            )
        elif isinstance(module, MetaClip2VisionModelWithProjection):
            nn.init.normal_(
                module.visual_projection.weight,
                std=self.config.hidden_size**-0.5 * self.config.initializer_factor,
            )
        elif isinstance(module, MetaClip2TextModelWithProjection):
            nn.init.normal_(
                module.text_projection.weight,
                std=self.config.hidden_size**-0.5 * self.config.initializer_factor,
            )
        elif isinstance(module, MetaClip2ForImageClassification):
            nn.init.normal_(
                module.classifier.weight,
                std=self.config.vision_config.hidden_size**-0.5 * self.config.initializer_factor,
            )

        if isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()


class MetaClip2Encoder(nn.Module):
    """
    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
    [`MetaClip2EncoderLayer`].

    Args:
        config: MetaClip2Config
    """

    def __init__(self, config: MetaClip2Config):
        super().__init__()
        self.config = config
        self.layers = nn.ModuleList([MetaClip2EncoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        inputs_embeds,
        attention_mask: Optional[torch.Tensor] = None,
        causal_attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutput:
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

                [What are attention masks?](../glossary#attention-mask)
            causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Causal mask for the text model. Mask values selected in `[0, 1]`:

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

                [What are attention masks?](../glossary#attention-mask)
        """
        hidden_states = inputs_embeds
        for encoder_layer in self.layers:
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask,
                causal_attention_mask,
                **kwargs,
            )

        return BaseModelOutput(
            last_hidden_state=hidden_states,
        )


class MetaClip2TextTransformer(nn.Module):
    def __init__(self, config: MetaClip2TextConfig):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size
        self.embeddings = MetaClip2TextEmbeddings(config)
        self.encoder = MetaClip2Encoder(config)
        self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

        # For `pooled_output` computation
        self.eos_token_id = config.eos_token_id

    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        input_ids,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        input_shape = input_ids.size()
        input_ids = input_ids.view(-1, input_shape[-1])

        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)

        # CLIP's text model uses causal mask, prepare it here.
        # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
        causal_attention_mask = _create_4d_causal_attention_mask(
            input_shape, hidden_states.dtype, device=hidden_states.device
        )

        # expand attention_mask
        if attention_mask is not None and self.config._attn_implementation != "flash_attention_2":
            # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
            attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)

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

        last_hidden_state = encoder_outputs.last_hidden_state
        last_hidden_state = self.final_layer_norm(last_hidden_state)

        # Use robust pooling like CLIP - finds the first EOS token position per sequence
        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,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


@auto_docstring(
    custom_intro="""
    The text model from METACLIP_2 without any head or projection on top.
    """
)
class MetaClip2TextModel(MetaClip2PreTrainedModel):
    """
    The text model from MetaClip2 without any head or projection on top.
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

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

    Args:
        config ([`MetaClip2TextConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Examples:

    ```python
    >>> from transformers import AutoTokenizer, MetaClip2TextModel

    >>> model = MetaClip2TextModel.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

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

    >>> outputs = model(**inputs)
    >>> last_hidden_state = outputs.last_hidden_state
    >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
    ```"""

    config: MetaClip2TextConfig
    input_modalities = "text"

    _no_split_modules = ["MetaClip2TextEmbeddings", "MetaClip2EncoderLayer"]
    _supports_flash_attn = False  # mask creation only accounts for sdpa/eager

    def __init__(self, config: MetaClip2TextConfig):
        super().__init__(config)
        self.text_model = MetaClip2TextTransformer(config)
        # Initialize weights and apply final processing
        self.post_init()

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

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

    @check_model_inputs()
    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        r"""
        Examples:

        ```python
        >>> from transformers import AutoTokenizer, MetaClip2TextModel

        >>> model = MetaClip2TextModel.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

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

        >>> outputs = model(**inputs)
        >>> last_hidden_state = outputs.last_hidden_state
        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
        ```"""

        return self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            **kwargs,
        )


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for text model's outputs that also contains a pooling of the last hidden states.
    """
)
class MetaClip2TextModelOutput(ModelOutput):
    r"""
    text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
        The text embeddings obtained by applying the projection layer to the pooler_output.
    """

    text_embeds: Optional[torch.FloatTensor] = None
    last_hidden_state: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[tuple[torch.FloatTensor, ...]] = None


@auto_docstring
class MetaClip2TextModelWithProjection(MetaClip2PreTrainedModel):
    """
    MetaClip2 text model with a projection layer on top (a linear layer on top of the pooled output).

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

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

    Args:
        config ([`MetaClip2TextConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Examples:

    ```python
    >>> from transformers import AutoTokenizer, MetaClip2TextModelWithProjection

    >>> model = MetaClip2TextModelWithProjection.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

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

    >>> outputs = model(**inputs)
    >>> text_embeds = outputs.text_embeds
    ```"""

    config: MetaClip2TextConfig
    input_modalities = "text"

    _supports_flash_attn = False
    _no_split_modules = ["MetaClip2TextEmbeddings", "MetaClip2EncoderLayer"]

    def __init__(self, config: MetaClip2TextConfig):
        super().__init__(config)

        text_model = MetaClip2TextModel._from_config(config)
        self.text_model = text_model.text_model

        self.text_projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

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

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

    @check_model_inputs()
    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MetaClip2TextModelOutput:
        r"""
        Examples:

        ```python
        >>> from transformers import AutoTokenizer, MetaClip2TextModelWithProjection

        >>> model = MetaClip2TextModelWithProjection.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

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

        >>> outputs = model(**inputs)
        >>> text_embeds = outputs.text_embeds
        ```"""

        text_outputs: BaseModelOutputWithPooling = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            **kwargs,
        )
        pooled_output = text_outputs.pooler_output
        text_embeds = self.text_projection(pooled_output)

        return MetaClip2TextModelOutput(
            text_embeds=text_embeds,
            last_hidden_state=text_outputs.last_hidden_state,
        )


@dataclass
@auto_docstring
class MetaClip2Output(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 [`MetaClip2TextModel`].
    image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
        The image embeddings obtained by applying the projection layer to the pooled output of [`MetaClip2VisionModel`].
    text_model_output (`BaseModelOutputWithPooling`):
        The output of the [`MetaClip2TextModel`].
    vision_model_output (`BaseModelOutputWithPooling`):
        The output of the [`MetaClip2VisionModel`].
    """

    loss: Optional[torch.FloatTensor] = None
    logits_per_image: Optional[torch.FloatTensor] = None
    logits_per_text: Optional[torch.FloatTensor] = None
    text_embeds: Optional[torch.FloatTensor] = None
    image_embeds: Optional[torch.FloatTensor] = None
    text_model_output: BaseModelOutputWithPooling = None
    vision_model_output: BaseModelOutputWithPooling = None

    def to_tuple(self) -> tuple[Any]:
        return tuple(
            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
            for k in self.keys()
        )


# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/2021-03-07-metaclip_2.html
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))


def metaclip_2_loss(similarity: torch.Tensor) -> torch.Tensor:
    caption_loss = contrastive_loss(similarity)
    image_loss = contrastive_loss(similarity.t())
    return (caption_loss + image_loss) / 2.0


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 MetaClip2Model(MetaClip2PreTrainedModel):
    """
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

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

    Args:
        config ([`MetaClip2Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Examples:

    ```python
    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, MetaClip2Model

    >>> model = MetaClip2Model.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
    >>> processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

    >>> 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
    ```"""

    config: MetaClip2Config
    _no_split_modules = ["MetaClip2TextEmbeddings", "MetaClip2EncoderLayer", "MetaClip2VisionEmbeddings"]
    _supports_flash_attn = False  # mask creation only accounts for sdpa/eager

    def __init__(self, config: MetaClip2Config):
        super().__init__(config)

        if not isinstance(config.text_config, MetaClip2TextConfig):
            raise TypeError(
                "config.text_config is expected to be of type MetaClip2TextConfig but is of type"
                f" {type(config.text_config)}."
            )

        if not isinstance(config.vision_config, MetaClip2VisionConfig):
            raise TypeError(
                "config.vision_config is expected to be of type MetaClip2VisionConfig but is of type"
                f" {type(config.vision_config)}."
            )

        text_config = config.text_config
        vision_config = config.vision_config

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

        text_model = MetaClip2TextModel._from_config(text_config)
        self.text_model = text_model.text_model

        vision_model = MetaClip2VisionModel._from_config(vision_config)
        self.vision_model = vision_model.vision_model

        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))

        # Initialize weights and apply final processing
        self.post_init()

    @filter_out_non_signature_kwargs()
    @auto_docstring
    def get_text_features(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
    ) -> torch.FloatTensor:
        r"""
        Returns:
            text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
            applying the projection layer to the pooled output of [`MetaClip2TextModel`].

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, MetaClip2Model

        >>> model = MetaClip2Model.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
        >>> 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,
        )
        pooled_output = text_outputs.pooler_output
        text_features = self.text_projection(pooled_output)

        return text_features

    @filter_out_non_signature_kwargs()
    @auto_docstring
    def get_image_features(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        interpolate_pos_encoding: bool = False,
    ) -> torch.FloatTensor:
        r"""
        Returns:
            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
            applying the projection layer to the pooled output of [`MetaClip2VisionModel`].

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, MetaClip2Model

        >>> model = MetaClip2Model.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
        >>> processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

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

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

        >>> image_features = model.get_image_features(**inputs)
        ```"""
        vision_outputs: BaseModelOutputWithPooling = self.vision_model(
            pixel_values=pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
        )
        pooled_output = vision_outputs.pooler_output
        image_features = self.visual_projection(pooled_output)

        return image_features

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        return_loss: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MetaClip2Output:
        r"""
        return_loss (`bool`, *optional*):
            Whether or not to return the contrastive loss.

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, MetaClip2Model

        >>> model = MetaClip2Model.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
        >>> processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

        >>> 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,
            interpolate_pos_encoding=interpolate_pos_encoding,
            **kwargs,
        )

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

        # cosine similarity as logits
        logits_per_text = torch.matmul(text_embeds, image_embeds.t().to(text_embeds.device))
        logits_per_text = logits_per_text * self.logit_scale.exp().to(text_embeds.device)

        logits_per_image = logits_per_text.t()

        loss = None
        if return_loss:
            loss = metaclip_2_loss(logits_per_text)

        return MetaClip2Output(
            loss=loss,
            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,
        )


class MetaClip2VisionTransformer(nn.Module):
    def __init__(self, config: MetaClip2VisionConfig):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = MetaClip2VisionEmbeddings(config)
        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
        self.encoder = MetaClip2Encoder(config)
        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        interpolate_pos_encoding: Optional[bool] = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
        hidden_states = self.pre_layrnorm(hidden_states)

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

        last_hidden_state = encoder_outputs.last_hidden_state
        pooled_output = last_hidden_state[:, 0, :]
        pooled_output = self.post_layernorm(pooled_output)

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


@auto_docstring(
    custom_intro="""
    The vision model from METACLIP_2 without any head or projection on top.
    """
)
class MetaClip2VisionModel(MetaClip2PreTrainedModel):
    """
    The vision model from MetaClip2 without any head or projection on top.

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

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

    Args:
        config ([`MetaClip2VisionConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Examples:

    ```python
    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, MetaClip2VisionModel

    >>> model = MetaClip2VisionModel.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
    >>> processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

    >>> 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 CLS states
    ```"""

    config: MetaClip2VisionConfig
    main_input_name = "pixel_values"
    input_modalities = "image"
    _no_split_modules = ["MetaClip2EncoderLayer"]

    def __init__(self, config: MetaClip2VisionConfig):
        super().__init__(config)
        self.vision_model = MetaClip2VisionTransformer(config)
        # Initialize weights and apply final processing
        self.post_init()

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

    @check_model_inputs(tie_last_hidden_states=False)
    @can_return_tuple
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        interpolate_pos_encoding: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        r"""
        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, MetaClip2VisionModel

        >>> model = MetaClip2VisionModel.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
        >>> processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

        >>> 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 CLS states
        ```"""

        return self.vision_model(
            pixel_values=pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
            **kwargs,
        )


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
    """
)
class MetaClip2VisionModelOutput(ModelOutput):
    r"""
    image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
        The image embeddings obtained by applying the projection layer to the pooler_output.
    """

    image_embeds: Optional[torch.FloatTensor] = None
    last_hidden_state: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[tuple[torch.FloatTensor, ...]] = None


@auto_docstring
class MetaClip2VisionModelWithProjection(MetaClip2PreTrainedModel):
    """
    MetaClip2 vision model with a projection layer on top (a linear layer on top of the pooled output).

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

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

    Args:
        config ([`MetaClip2VisionConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Examples:

    ```python
    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, MetaClip2VisionModelWithProjection

    >>> model = MetaClip2VisionModelWithProjection.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
    >>> processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

    >>> 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)
    >>> image_embeds = outputs.image_embeds
    ```"""

    config: MetaClip2VisionConfig
    main_input_name = "pixel_values"
    input_modalities = "image"

    def __init__(self, config: MetaClip2VisionConfig):
        super().__init__(config)

        vision_model = MetaClip2VisionModel._from_config(config)
        self.vision_model = vision_model.vision_model

        self.visual_projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

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

    @check_model_inputs(tie_last_hidden_states=False)
    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        interpolate_pos_encoding: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MetaClip2VisionModelOutput:
        r"""
        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, MetaClip2VisionModelWithProjection

        >>> model = MetaClip2VisionModelWithProjection.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
        >>> processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

        >>> 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)
        >>> image_embeds = outputs.image_embeds
        ```"""

        vision_outputs: BaseModelOutputWithPooling = self.vision_model(
            pixel_values=pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
            **kwargs,
        )
        pooled_output = vision_outputs.pooler_output
        image_embeds = self.visual_projection(pooled_output)

        return MetaClip2VisionModelOutput(
            image_embeds=image_embeds,
            last_hidden_state=vision_outputs.last_hidden_state,
        )


@auto_docstring(
    custom_intro="""
    METACLIP_2 vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of
    the patch tokens) e.g. for ImageNet.
    """
)
class MetaClip2ForImageClassification(MetaClip2PreTrainedModel):
    main_input_name = "pixel_values"
    input_modalities = "image"

    def __init__(self, config: MetaClip2Config) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        vision_model = MetaClip2VisionModel._from_config(config.vision_config)
        self.vision_model = vision_model.vision_model

        # Classifier head
        self.classifier = (
            nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
        )

        # Initialize weights and apply final processing
        self.post_init()

    @check_model_inputs()
    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> ImageClassifierOutput:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        outputs: BaseModelOutputWithPooling = self.vision_model(
            pixel_values,
            **kwargs,
        )

        sequence_output = outputs.last_hidden_state

        sequence_output = torch.mean(sequence_output[:, 1:, :], dim=1)
        logits = self.classifier(sequence_output)

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

        return ImageClassifierOutput(
            loss=loss,
            logits=logits,
        )


__all__ = [
    "MetaClip2Model",
    "MetaClip2PreTrainedModel",
    "MetaClip2TextModel",
    "MetaClip2TextModelWithProjection",
    "MetaClip2VisionModel",
    "MetaClip2VisionModelWithProjection",
    "MetaClip2ForImageClassification",
]
