modeling_vit.py

# coding=utf-8
# Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch ViT model."""

import collections.abc
import math
from typing import Dict, List, Optional, Set, Tuple, Union
from functools import partial
from enum import Flag, auto
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
import numpy as np
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPooling,
    ImageClassifierOutput,
    MaskedImageModelingOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from transformers.utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_vit import ViTConfig


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "ViTConfig"

# Base docstring
_CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224-in21k"
_EXPECTED_OUTPUT_SHAPE = [1, 197, 768]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat"




class BaseEnumOptions(Flag):
    def __str__(self):
        return self.name

    @classmethod
    def list_names(cls):
        return [m.name for m in cls]
class AttentionGateType(BaseEnumOptions):
    none = 0
    unconditional_per_head = 1
    conditional_per_head = 2
    conditional_per_token = 3

def softmax_n_shifted_zeros(input: torch.Tensor, n: int, dim=-1) -> torch.Tensor:
    """
    $\text(softmax)_n(x_i) = exp(x_i) / (n + \sum_j exp(x_j))$

    Note: softmax_n, with fixed input, is _not_ shift-symmetric when n != 0
    """
    # compute the maxes along the last dimension
    input_maxes = input.max(dim=dim, keepdim=True).values
    # shift the input to prevent overflow (and underflow in the denominator)
    shifted_inputs = torch.subtract(input, input_maxes)
    # compute the numerator and softmax_0 denominator using the shifted input
    numerator = torch.exp(shifted_inputs)
    original_denominator = numerator.sum(dim=dim, keepdim=True)
    # we need to shift the zeros in the same way we shifted the inputs
    shifted_zeros = torch.multiply(input_maxes, -1)
    # and then add this contribution to the denominator
    denominator = torch.add(original_denominator,
                            torch.multiply(torch.exp(shifted_zeros), n))
    return torch.divide(numerator, denominator)
def logit(p, eps=1e-16):
    p = np.clip(p, eps, 1 - eps)
    return -np.log(1 / p - 1)

def softmax_1(input: torch.Tensor, dim=-1) -> torch.Tensor:
    """
    $\text(softmax)_n(x_i) = exp(x_i) / (1 + \sum_j exp(x_j))$
    """
    return softmax_n_shifted_zeros(input, 1, dim=dim)


def clipped_softmax(data, dim=1, eta=1.1, gamma=-0.1, **kw):
    sm_out = torch.nn.functional.softmax(data, dim=dim, **kw)
    stretched_out = sm_out * (eta - gamma) + gamma
    return torch.clip(stretched_out, 0, 1)
def clipped_softmax1(data, dim=1, eta=1.1, gamma=-0.1, **kw):
    sm_out = softmax_1(data, dim=dim, **kw)
    stretched_out = sm_out * (eta - gamma) + gamma
    return torch.clip(stretched_out, 0, 1)

class ViTEmbeddings(nn.Module):
    """
    Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
    """

    def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None:
        super().__init__()

        self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
        self.patch_embeddings = ViTPatchEmbeddings(config)
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size))
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.config = config

    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.

        Source:
        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
        """

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1
        if num_patches == num_positions and height == width:
            return self.position_embeddings
        class_pos_embed = self.position_embeddings[:, 0]
        patch_pos_embed = self.position_embeddings[:, 1:]
        dim = embeddings.shape[-1]
        h0 = height // self.config.patch_size
        w0 = width // self.config.patch_size
        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        h0, w0 = h0 + 0.1, w0 + 0.1
        patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
            mode="bicubic",
            align_corners=False,
        )
        assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)

    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        interpolate_pos_encoding: bool = False,
    ) -> torch.Tensor:
        batch_size, num_channels, height, width = pixel_values.shape
        embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)

        if bool_masked_pos is not None:
            seq_length = embeddings.shape[1]
            mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
            # replace the masked visual tokens by mask_tokens
            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask

        # add the [CLS] token to the embedded patch tokens
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)

        # add positional encoding to each token
        if interpolate_pos_encoding:
            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
        else:
            embeddings = embeddings + self.position_embeddings

        embeddings = self.dropout(embeddings)

        return embeddings


class ViTPatchEmbeddings(nn.Module):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size

        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)

    def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
        batch_size, num_channels, height, width = pixel_values.shape
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
                f" Expected {self.num_channels} but got {num_channels}."
            )
        if not interpolate_pos_encoding:
            if height != self.image_size[0] or width != self.image_size[1]:
                raise ValueError(
                    f"Input image size ({height}*{width}) doesn't match model"
                    f" ({self.image_size[0]}*{self.image_size[1]})."
                )
        embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return embeddings


class ViTSelfAttention(nn.Module):
    def __init__(self, config: ViTConfig,
            gamma=None,
            ssm_eps=None,
            tau=None,
            skip_attn=False,
            attn_gate_type=AttentionGateType.conditional_per_token,
            attn_gate_init=0.25,
            attn_gate_mlp=False,
            attn_gate_mlp2=False,
            max_seq_length=None,
            fine_tuning=False,
            attn_gate_linear_all_features=False) -> None:
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
                f"heads {config.num_attention_heads}."
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.softmax_fn = nn.functional.softmax
        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.gamma = gamma
        self.ssm_eps = ssm_eps
        self.tau = tau
        self.max_seq_length = max_seq_length

        # define softmax function
    
        self.skip_attn = skip_attn

        # attention gating
        self.last_gate_avg_prob = None
        self.last_gate_all_probs = None

        self.attn_gate_type = attn_gate_type
        self.attn_gate_init = attn_gate_init
        self.attn_gate_mlp = attn_gate_mlp
        self.attn_gate_mlp2 = attn_gate_mlp2
        self.attn_gate_linear_all_features = attn_gate_linear_all_features

        self.alpha = None
        self.gate_fn = torch.sigmoid
        self.pooling_fn = partial(torch.mean, dim=1, keepdims=True)

        self.fine_tuning = fine_tuning

        # gate scaling factor
        self.gate_scaling_factor = 1.0
        if self.fine_tuning and self.attn_gate_init is not None:
            self.gate_scaling_factor = 1.0 / self.attn_gate_init

        # define gate
        if self.attn_gate_type == AttentionGateType.unconditional_per_head:
            init_alpha = torch.zeros(size=(self.num_attention_heads,))
            self.alpha = nn.Parameter(init_alpha, requires_grad=True)

        elif self.attn_gate_type in (
            AttentionGateType.conditional_per_head,
            AttentionGateType.conditional_per_token,
        ):
            if self.attn_gate_linear_all_features:
                self.alpha = nn.Linear(self.all_head_size, self.num_attention_heads, bias=True)

            else:  # separate predictors for each head
                module_list = []
                for _ in range(self.num_attention_heads):
                    if self.attn_gate_mlp:
                        fc = nn.Sequential(
                            nn.Linear(
                                self.attention_head_size, self.attention_head_size // 4, bias=True
                            ),
                            nn.ReLU(),
                            nn.Linear(self.attention_head_size // 4, 1, bias=True),
                        )
                    elif self.attn_gate_mlp2:
                        fc = nn.Sequential(
                            nn.Linear(
                                self.attention_head_size, self.attention_head_size, bias=True
                            ),
                            nn.ReLU(),
                            nn.Linear(self.attention_head_size, 1, bias=True),
                        )
                    else:
                        fc = nn.Linear(self.attention_head_size, 1, bias=True)

                        if self.attn_gate_init is not None:
                            init_bias = logit(self.attn_gate_init)
                            torch.nn.init.constant_(fc.bias, init_bias)

                        if self.fine_tuning:
                            # init to a very small values
                            torch.nn.init.normal_(fc.weight, mean=0.0, std=0.01)

                    module_list.append(fc)
                self.alpha = nn.ModuleList(module_list)


    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        mixed_query_layer = self.query(hidden_states)

        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)

        # Normalize the attention scores to probabilities.
        attention_probs = self.softmax_fn(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)


        # *** Gating ***
        if self.attn_gate_type == AttentionGateType.unconditional_per_head:
            gate = self.gate_fn(self.alpha)  # (H,)
            context_layer *= gate.view(-1, 1, 1)  # (B, H, T, d_head)

            self.last_gate_avg_prob = gate.view(-1)

        elif self.attn_gate_type in (
            AttentionGateType.conditional_per_head,
            AttentionGateType.conditional_per_token,
        ):
            
            x = hidden_states
            
            if self.attn_gate_linear_all_features:  # assume per_token
                alpha = self.alpha(x)  # (B, T, H)
                gate = self.gate_fn(alpha)
                gate = gate.permute(0, 2, 1).contiguous()  # (B, H, T)
                gate = gate.unsqueeze(3)  # (B, H, T, 1)

            else:
                x = self.transpose_for_scores(x)  # (B, H, T, d_head)

                alpha = []
                for head_idx in range(self.num_attention_heads):
                    x_head = x[:, head_idx, ...]  # (B, T, d_head)
                    fc_head = self.alpha[head_idx]
                    alpha_head = fc_head(x_head)  # (B, T, 1)
                    if self.attn_gate_type == AttentionGateType.conditional_per_head:
                        alpha_head = self.pooling_fn(alpha_head)  # (B, 1, 1)
                    alpha.append(alpha_head)
                alpha = torch.stack(alpha, dim=1)  # (B, H, *, 1)
                gate = self.gate_fn(alpha)

            context_layer *= gate * self.gate_scaling_factor

            self.last_gate_all_probs = gate  # all gates to see the distributions
            avg_gate = gate.mean(dim=0)
            self.last_gate_avg_prob = avg_gate.view(self.num_attention_heads, -1).mean(dim=1)



        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        return outputs

def scaled_dot_product_attention(query, key, value, softmax_fn, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None) -> torch.Tensor:
    # Efficient implementation equivalent to the following:
    device = "cuda" if torch.cuda.is_available() else "cpu"
    L, S = query.size(-2), key.size(-2)
    scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
    attn_bias = torch.zeros(L, S, dtype=query.dtype, device=query.device)
    if is_causal:
        assert attn_mask is None
        temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
        attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
        attn_bias.to(query.dtype)

    if attn_mask is not None:
        if attn_mask.dtype == torch.bool:
            attn_mask.masked_fill_(attn_mask.logical_not(), float("-inf"))
        else:
            attn_bias += attn_mask
    attn_weight = query @ key.transpose(-2, -1) * scale_factor
    attn_weight += attn_bias
    attn_weight = softmax_fn(attn_weight, dim=-1)
    attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
    return attn_weight @ value

class ViTSdpaSelfAttention(ViTSelfAttention):
    def __init__(self, config: ViTConfig) -> None:
        super().__init__(config)
        self.attention_probs_dropout_prob = config.attention_probs_dropout_prob

    def forward(
        self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        mixed_query_layer = self.query(hidden_states)

        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)

        context_layer = scaled_dot_product_attention(
            query_layer,
            key_layer,
            value_layer,
            dropout_p=self.attention_probs_dropout_prob if self.training else 0.0,
            attn_mask=head_mask,
            softmax_fn = self.softmax_fn,
            is_causal=False,
            scale=None,
        )

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        return context_layer, None


class ViTSelfOutput(nn.Module):
    """
    The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: ViTConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)

        return hidden_states


class ViTAttention(nn.Module):
    def __init__(self, config: ViTConfig) -> None:
        super().__init__()
        self.attention = ViTSelfAttention(config)
        self.output = ViTSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads: Set[int]) -> None:
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.attention.query = prune_linear_layer(self.attention.query, index)
        self.attention.key = prune_linear_layer(self.attention.key, index)
        self.attention.value = prune_linear_layer(self.attention.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        self_outputs = self.attention(hidden_states, head_mask, output_attentions)

        attention_output = self.output(self_outputs[0], hidden_states)

        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


class ViTSdpaAttention(ViTAttention):
    def __init__(self, config: ViTConfig) -> None:
        super().__init__(config)
        self.attention = ViTSdpaSelfAttention(config)


class ViTIntermediate(nn.Module):
    def __init__(self, config: ViTConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)

        return hidden_states


class ViTOutput(nn.Module):
    def __init__(self, config: ViTConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)

        hidden_states = hidden_states + input_tensor

        return hidden_states


VIT_ATTENTION_CLASSES = {
    "eager": ViTAttention,
    "sdpa": ViTSdpaAttention,
}


class ViTLayer(nn.Module):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: ViTConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = VIT_ATTENTION_CLASSES[config._attn_implementation](config)
        self.intermediate = ViTIntermediate(config)
        self.output = ViTOutput(config)
        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        self_attention_outputs = self.attention(
            self.layernorm_before(hidden_states),  # in ViT, layernorm is applied before self-attention
            head_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in ViT, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(hidden_states)
        layer_output = self.intermediate(layer_output)

        # second residual connection is done here
        layer_output = self.output(layer_output, hidden_states)

        outputs = (layer_output,) + outputs

        return outputs


class ViTEncoder(nn.Module):
    def __init__(self, config: ViTConfig) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ) -> Union[tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    layer_module.__call__,
                    hidden_states,
                    layer_head_mask,
                    output_attentions,
                )
            else:
                layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class ViTPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = ViTConfig
    base_model_prefix = "vit"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["ViTEmbeddings", "ViTLayer"]
    _supports_sdpa = True

    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
            # `trunc_normal_cpu` not implemented in `half` issues
            module.weight.data = nn.init.trunc_normal_(
                module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
            ).to(module.weight.dtype)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, ViTEmbeddings):
            module.position_embeddings.data = nn.init.trunc_normal_(
                module.position_embeddings.data.to(torch.float32),
                mean=0.0,
                std=self.config.initializer_range,
            ).to(module.position_embeddings.dtype)

            module.cls_token.data = nn.init.trunc_normal_(
                module.cls_token.data.to(torch.float32),
                mean=0.0,
                std=self.config.initializer_range,
            ).to(module.cls_token.dtype)


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

    Parameters:
        config ([`ViTConfig`]): 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.
"""

VIT_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
            for details.

        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        interpolate_pos_encoding (`bool`, *optional*):
            Whether to interpolate the pre-trained position encodings.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare ViT Model transformer outputting raw hidden-states without any specific head on top.",
    VIT_START_DOCSTRING,
)
class ViTModel(ViTPreTrainedModel):
    def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False):
        super().__init__(config)
        self.config = config

        self.embeddings = ViTEmbeddings(config, use_mask_token=use_mask_token)
        self.encoder = ViTEncoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pooler = ViTPooler(config) if add_pooling_layer else None

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

    def get_input_embeddings(self) -> ViTPatchEmbeddings:
        return self.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        r"""
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?)
        expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
        if pixel_values.dtype != expected_dtype:
            pixel_values = pixel_values.to(expected_dtype)

        embedding_output = self.embeddings(
            pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
        )

        encoder_outputs = self.encoder(
            embedding_output,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
            return head_outputs + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class ViTPooler(nn.Module):
    def __init__(self, config: ViTConfig):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


@add_start_docstrings(
    """ViT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://arxiv.org/abs/2111.09886).

    <Tip>

    Note that we provide a script to pre-train this model on custom data in our [examples
    directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).

    </Tip>
    """,
    VIT_START_DOCSTRING,
)
class ViTForMaskedImageModeling(ViTPreTrainedModel):
    def __init__(self, config: ViTConfig) -> None:
        super().__init__(config)

        self.vit = ViTModel(config, add_pooling_layer=False, use_mask_token=True)

        self.decoder = nn.Sequential(
            nn.Conv2d(
                in_channels=config.hidden_size,
                out_channels=config.encoder_stride**2 * config.num_channels,
                kernel_size=1,
            ),
            nn.PixelShuffle(config.encoder_stride),
        )

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

    @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=MaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, MaskedImageModelingOutput]:
        r"""
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).

        Returns:

        Examples:
        ```python
        >>> from transformers import AutoImageProcessor, ViTForMaskedImageModeling
        >>> import torch
        >>> from PIL import Image
        >>> import requests

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

        >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
        >>> model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k")

        >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
        >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
        >>> # create random boolean mask of shape (batch_size, num_patches)
        >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()

        >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
        >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
        >>> list(reconstructed_pixel_values.shape)
        [1, 3, 224, 224]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if bool_masked_pos is not None and (self.config.patch_size != self.config.encoder_stride):
            raise ValueError(
                "When `bool_masked_pos` is provided, `patch_size` must be equal to `encoder_stride` to ensure that "
                "the reconstructed image has the same dimensions as the input. "
                f"Got `patch_size` = {self.config.patch_size} and `encoder_stride` = {self.config.encoder_stride}."
            )

        outputs = self.vit(
            pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        # Reshape to (batch_size, num_channels, height, width)
        sequence_output = sequence_output[:, 1:]
        batch_size, sequence_length, num_channels = sequence_output.shape
        height = width = math.floor(sequence_length**0.5)
        sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)

        # Reconstruct pixel values
        reconstructed_pixel_values = self.decoder(sequence_output)

        masked_im_loss = None
        if bool_masked_pos is not None:
            size = self.config.image_size // self.config.patch_size
            bool_masked_pos = bool_masked_pos.reshape(-1, size, size)
            mask = (
                bool_masked_pos.repeat_interleave(self.config.patch_size, 1)
                .repeat_interleave(self.config.patch_size, 2)
                .unsqueeze(1)
                .contiguous()
            )
            reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none")
            masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels

        if not return_dict:
            output = (reconstructed_pixel_values,) + outputs[1:]
            return ((masked_im_loss,) + output) if masked_im_loss is not None else output

        return MaskedImageModelingOutput(
            loss=masked_im_loss,
            reconstruction=reconstructed_pixel_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
    the [CLS] token) e.g. for ImageNet.

    <Tip>

        Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by
        setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
        position embeddings to the higher resolution.

    </Tip>
    """,
    VIT_START_DOCSTRING,
)
class ViTForImageClassification(ViTPreTrainedModel):
    def __init__(self, config: ViTConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.vit = ViTModel(config, add_pooling_layer=False)

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

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

    @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=ImageClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, 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).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.vit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        logits = self.classifier(sequence_output[:, 0, :])

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )