# coding=utf-8
# Copyright 2022 Facebook AI and 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.
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"""PyTorch ViT MAE (masked autoencoder) model."""

import collections.abc
from copy import deepcopy
from dataclasses import dataclass
from typing import Callable, Optional

import numpy as np
import torch
from torch import nn

from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, logging, torch_int
from ...utils.generic import can_return_tuple, check_model_inputs
from .configuration_vit_mae import ViTMAEConfig


logger = logging.get_logger(__name__)


@dataclass
@auto_docstring(
    custom_intro="""
    Class for ViTMAEModel's outputs, with potential hidden states and attentions.
    """
)
class ViTMAEModelOutput(ModelOutput):
    r"""
    mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
        Tensor indicating which patches are masked (1) and which are not (0).
    ids_restore (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
        Tensor containing the original index of the (shuffled) masked patches.
    """

    last_hidden_state: Optional[torch.FloatTensor] = None
    mask: Optional[torch.LongTensor] = None
    ids_restore: Optional[torch.LongTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None


@dataclass
@auto_docstring(
    custom_intro="""
    Class for ViTMAEDecoder's outputs, with potential hidden states and attentions.
    """
)
class ViTMAEDecoderOutput(ModelOutput):
    r"""
    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
        Pixel reconstruction logits.
    """

    logits: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None


@dataclass
@auto_docstring(
    custom_intro="""
    Class for ViTMAEForPreTraining's outputs, with potential hidden states and attentions.
    """
)
class ViTMAEForPreTrainingOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`):
        Pixel reconstruction loss.
    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
        Pixel reconstruction logits.
    mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
        Tensor indicating which patches are masked (1) and which are not (0).
    ids_restore (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
        Tensor containing the original index of the (shuffled) masked patches.
    """

    loss: Optional[torch.FloatTensor] = None
    logits: Optional[torch.FloatTensor] = None
    mask: Optional[torch.LongTensor] = None
    ids_restore: Optional[torch.LongTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None


def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False):
    """
    Create 2D sin/cos positional embeddings.

    Args:
        embed_dim (`int`):
            Embedding dimension.
        grid_size (`int`):
            The grid height and width.
        add_cls_token (`bool`, *optional*, defaults to `False`):
            Whether or not to add a classification (CLS) token.

    Returns:
        (`torch.FloatTensor` of shape (grid_size*grid_size, embed_dim) or (1+grid_size*grid_size, embed_dim): the
        position embeddings (with or without classification token)
    """
    grid_h = np.arange(grid_size, dtype=np.float32)
    grid_w = np.arange(grid_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if add_cls_token:
        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be even")

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
    """
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be even")

    omega = np.arange(embed_dim // 2, dtype=float)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb


class ViTMAEEmbeddings(nn.Module):
    """
    Construct the CLS token, position and patch embeddings.

    """

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

        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.patch_embeddings = ViTMAEPatchEmbeddings(config)
        self.num_patches = self.patch_embeddings.num_patches
        # fixed sin-cos embedding
        self.position_embeddings = nn.Parameter(
            torch.zeros(1, self.num_patches + 1, config.hidden_size), requires_grad=False
        )
        self.patch_size = config.patch_size
        self.config = config

    def initialize_weights(self):
        # initialize (and freeze) position embeddings by sin-cos embedding
        pos_embed = get_2d_sincos_pos_embed(
            self.position_embeddings.shape[-1], int(self.patch_embeddings.num_patches**0.5), add_cls_token=True
        )
        self.position_embeddings.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        # initialize patch_embeddings like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embeddings.projection.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=self.config.initializer_range)

    # Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
    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
        num_positions = self.position_embeddings.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_embeddings

        class_pos_embed = self.position_embeddings[:, :1]
        patch_pos_embed = self.position_embeddings[:, 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 random_masking(self, sequence, noise=None):
        """
        Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random
        noise.

        Args:
            sequence (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`)
            noise (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) which is
                mainly used for testing purposes to control randomness and maintain the reproducibility
        """
        batch_size, seq_length, dim = sequence.shape
        len_keep = int(seq_length * (1 - self.config.mask_ratio))

        if noise is None:
            noise = torch.rand(batch_size, seq_length, device=sequence.device)  # noise in [0, 1]

        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1).to(sequence.device)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1).to(sequence.device)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        sequence_unmasked = torch.gather(sequence, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, dim))

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([batch_size, seq_length], device=sequence.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return sequence_unmasked, mask, ids_restore

    def forward(self, pixel_values, noise=None, interpolate_pos_encoding: bool = False):
        batch_size, num_channels, height, width = pixel_values.shape
        embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
        if interpolate_pos_encoding:
            position_embeddings = self.interpolate_pos_encoding(embeddings, height, width)
        else:
            position_embeddings = self.position_embeddings

        # add position embeddings w/o cls token
        embeddings = embeddings + position_embeddings[:, 1:, :]

        # masking: length -> length * config.mask_ratio
        embeddings, mask, ids_restore = self.random_masking(embeddings, noise)

        # append cls token
        cls_token = self.cls_token + position_embeddings[:, :1, :]
        cls_tokens = cls_token.expand(embeddings.shape[0], -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)

        return embeddings, mask, ids_restore


class ViTMAEPatchEmbeddings(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, interpolate_pos_encoding: bool = False):
        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."
            )

        if not interpolate_pos_encoding and (height != self.image_size[0] or width != self.image_size[1]):
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
            )
        x = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return x


# Copied from transformers.models.vit.modeling_vit.eager_attention_forward
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,
):
    # Take the dot product between "query" and "key" to get the raw attention scores.
    attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling

    # Normalize the attention scores to probabilities.
    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)

    # This is actually dropping out entire tokens to attend to, which might
    # seem a bit unusual, but is taken from the original Transformer paper.
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)

    # Mask heads if we want to
    if attention_mask is not None:
        attn_weights = attn_weights * attention_mask

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

    return attn_output, attn_weights


# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention ViT->ViTMAE
class ViTMAESelfAttention(nn.Module):
    def __init__(self, config: ViTMAEConfig):
        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.config = config
        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.dropout_prob = config.attention_probs_dropout_prob
        self.scaling = self.attention_head_size**-0.5
        self.is_causal = False

        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)

    def forward(
        self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None
    ) -> tuple[torch.Tensor, torch.Tensor]:
        batch_size = hidden_states.shape[0]
        new_shape = batch_size, -1, self.num_attention_heads, self.attention_head_size

        key_layer = self.key(hidden_states).view(*new_shape).transpose(1, 2)
        value_layer = self.value(hidden_states).view(*new_shape).transpose(1, 2)
        query_layer = self.query(hidden_states).view(*new_shape).transpose(1, 2)

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

        context_layer, attention_probs = attention_interface(
            self,
            query_layer,
            key_layer,
            value_layer,
            head_mask,
            is_causal=self.is_causal,
            scaling=self.scaling,
            dropout=0.0 if not self.training else self.dropout_prob,
        )

        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.reshape(new_context_layer_shape)

        return context_layer, attention_probs


# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->ViTMAE
class ViTMAESelfOutput(nn.Module):
    """
    The residual connection is defined in ViTMAELayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: ViTMAEConfig):
        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


# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTMAE
class ViTMAEAttention(nn.Module):
    def __init__(self, config: ViTMAEConfig):
        super().__init__()
        self.attention = ViTMAESelfAttention(config)
        self.output = ViTMAESelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads: set[int]):
        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) -> torch.Tensor:
        self_attn_output, _ = self.attention(hidden_states, head_mask)
        output = self.output(self_attn_output, hidden_states)
        return output


# Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->ViTMAE
class ViTMAEIntermediate(nn.Module):
    def __init__(self, config: ViTMAEConfig):
        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


# Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->ViTMAE
class ViTMAEOutput(nn.Module):
    def __init__(self, config: ViTMAEConfig):
        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


# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->ViTMAE,VIT->VITMAE
class ViTMAELayer(GradientCheckpointingLayer):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: ViTMAEConfig):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = ViTMAEAttention(config)
        self.intermediate = ViTMAEIntermediate(config)
        self.output = ViTMAEOutput(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) -> torch.Tensor:
        hidden_states_norm = self.layernorm_before(hidden_states)
        attention_output = self.attention(hidden_states_norm, head_mask)

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in ViTMAE, 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)

        return layer_output


# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTMAE
class ViTMAEEncoder(nn.Module):
    def __init__(self, config: ViTMAEConfig):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([ViTMAELayer(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) -> BaseModelOutput:
        for i, layer_module in enumerate(self.layer):
            layer_head_mask = head_mask[i] if head_mask is not None else None
            hidden_states = layer_module(hidden_states, layer_head_mask)

        return BaseModelOutput(last_hidden_state=hidden_states)


@auto_docstring
class ViTMAEPreTrainedModel(PreTrainedModel):
    config: ViTMAEConfig
    base_model_prefix = "vit"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": ViTMAELayer,
        "attentions": ViTMAESelfAttention,
    }

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            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, ViTMAEEmbeddings):
            module.initialize_weights()
        elif isinstance(module, ViTMAEDecoder):
            module.mask_token.data.zero_()
            module.decoder_pos_embed.data.zero_()


@auto_docstring
class ViTMAEModel(ViTMAEPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config

        self.embeddings = ViTMAEEmbeddings(config)
        self.encoder = ViTMAEEncoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

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

    def get_input_embeddings(self):
        return self.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune):
        """
        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)

    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        noise: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        interpolate_pos_encoding: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> ViTMAEModelOutput:
        r"""
        noise (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mainly used for testing purposes to control randomness and maintain the reproducibility
        interpolate_pos_encoding (`bool`, *optional*, default `False`):
            Whether to interpolate the pre-trained position encodings. This is mainly used to use the model on higher
            resolution images.

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, ViTMAEModel
        >>> 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("facebook/vit-mae-base")
        >>> model = ViTMAEModel.from_pretrained("facebook/vit-mae-base")

        >>> inputs = image_processor(images=image, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""

        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)

        embedding_output, mask, ids_restore = self.embeddings(
            pixel_values, noise=noise, interpolate_pos_encoding=interpolate_pos_encoding
        )

        encoder_outputs: BaseModelOutput = self.encoder(embedding_output, head_mask=head_mask)
        sequence_output = encoder_outputs.last_hidden_state
        sequence_output = self.layernorm(sequence_output)

        return ViTMAEModelOutput(last_hidden_state=sequence_output, mask=mask, ids_restore=ids_restore)


class ViTMAEDecoder(nn.Module):
    def __init__(self, config: ViTMAEConfig, num_patches: int):
        super().__init__()
        self.decoder_embed = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=True)
        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size))
        self.decoder_pos_embed = nn.Parameter(
            torch.zeros(1, num_patches + 1, config.decoder_hidden_size), requires_grad=False
        )  # fixed sin-cos embedding

        decoder_config = deepcopy(config)
        decoder_config.hidden_size = config.decoder_hidden_size
        decoder_config.num_hidden_layers = config.decoder_num_hidden_layers
        decoder_config.num_attention_heads = config.decoder_num_attention_heads
        decoder_config.intermediate_size = config.decoder_intermediate_size
        self.decoder_layers = nn.ModuleList(
            [ViTMAELayer(decoder_config) for _ in range(config.decoder_num_hidden_layers)]
        )

        self.decoder_norm = nn.LayerNorm(config.decoder_hidden_size, eps=config.layer_norm_eps)
        self.decoder_pred = nn.Linear(
            config.decoder_hidden_size, config.patch_size**2 * config.num_channels, bias=True
        )  # encoder to decoder
        self.gradient_checkpointing = False
        self.config = decoder_config
        self.initialize_weights(num_patches)

    def interpolate_pos_encoding(self, embeddings: torch.Tensor) -> torch.Tensor:
        """
        This method is a modified version of the interpolation function for ViT-mae model at the decoder, that
        allows to interpolate the pre-trained decoder position encodings, to be able to use the model on higher
        resolution images.

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

        # -1 removes the class dimension since we later append it without interpolation
        embeddings_positions = embeddings.shape[1] - 1

        # Separation of class token and patch tokens
        class_pos_embed = self.decoder_pos_embed[:, :1]
        patch_pos_embed = self.decoder_pos_embed[:, 1:]

        # To retain the final 3d tensor with the required dimensions
        dim = self.decoder_pos_embed.shape[-1]

        # Increasing a dimension to enable bicubic interpolation
        patch_pos_embed = patch_pos_embed.reshape(1, 1, -1, dim)

        # permute to bring the dimension to be interpolated, to the last
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        # Interpolating the decoder position embeddings shape wrt embeddings shape i.e (x).
        # we keep the second last dimension constant
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(patch_pos_embed.shape[-2], embeddings_positions),
            mode="bicubic",
            align_corners=False,
        )

        # Converting back to the original shape
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        # Adding the class token back
        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)

    def initialize_weights(self, num_patches):
        # initialize (and freeze) position embeddings by sin-cos embedding
        decoder_pos_embed = get_2d_sincos_pos_embed(
            self.decoder_pos_embed.shape[-1], int(num_patches**0.5), add_cls_token=True
        )
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.mask_token, std=self.config.initializer_range)

    def forward(self, hidden_states: torch.Tensor, ids_restore: torch.Tensor, interpolate_pos_encoding: bool = False):
        # Embed tokens
        x = self.decoder_embed(hidden_states)

        # Append mask tokens to sequence
        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token

        # Unshuffle
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]).to(x_.device))
        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token

        # Add pos embed
        if interpolate_pos_encoding:
            decoder_pos_embed = self.interpolate_pos_encoding(x)
        else:
            decoder_pos_embed = self.decoder_pos_embed
        hidden_states = x + decoder_pos_embed

        # Apply Transformer layers (blocks)
        for layer_module in self.decoder_layers:
            hidden_states = layer_module(hidden_states, head_mask=None)

        hidden_states = self.decoder_norm(hidden_states)

        # Predictor projection
        logits = self.decoder_pred(hidden_states)

        # Remove cls token
        logits = logits[:, 1:, :]

        return ViTMAEDecoderOutput(logits=logits)


@auto_docstring(
    custom_intro="""
    The ViTMAE Model transformer with the decoder on top for self-supervised pre-training.

    <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>
    """
)
class ViTMAEForPreTraining(ViTMAEPreTrainedModel):
    def __init__(self, config: ViTMAEConfig):
        super().__init__(config)
        self.config = config

        self.vit = ViTMAEModel(config)
        self.decoder = ViTMAEDecoder(config, num_patches=self.vit.embeddings.num_patches)

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

    def get_input_embeddings(self):
        return self.vit.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune):
        """
        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)

    def patchify(self, pixel_values, interpolate_pos_encoding: bool = False):
        """
        Args:
            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
                Pixel values.
            interpolate_pos_encoding (`bool`, *optional*, default `False`):
                interpolation flag passed during the forward pass.

        Returns:
            `torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
                Patchified pixel values.
        """
        patch_size, num_channels = self.config.patch_size, self.config.num_channels
        # sanity checks
        if not interpolate_pos_encoding and (
            pixel_values.shape[2] != pixel_values.shape[3] or pixel_values.shape[2] % patch_size != 0
        ):
            raise ValueError("Make sure the pixel values have a squared size that is divisible by the patch size")
        if pixel_values.shape[1] != num_channels:
            raise ValueError(
                "Make sure the number of channels of the pixel values is equal to the one set in the configuration"
            )

        # patchify
        batch_size = pixel_values.shape[0]
        num_patches_h = pixel_values.shape[2] // patch_size
        num_patches_w = pixel_values.shape[3] // patch_size
        patchified_pixel_values = pixel_values.reshape(
            batch_size, num_channels, num_patches_h, patch_size, num_patches_w, patch_size
        )
        patchified_pixel_values = torch.einsum("nchpwq->nhwpqc", patchified_pixel_values)
        patchified_pixel_values = patchified_pixel_values.reshape(
            batch_size, num_patches_h * num_patches_w, patch_size**2 * num_channels
        )
        return patchified_pixel_values

    def unpatchify(self, patchified_pixel_values, original_image_size: Optional[tuple[int, int]] = None):
        """
        Args:
            patchified_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
                Patchified pixel values.
            original_image_size (`tuple[int, int]`, *optional*):
                Original image size.

        Returns:
            `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`:
                Pixel values.
        """
        patch_size, num_channels = self.config.patch_size, self.config.num_channels
        original_image_size = (
            original_image_size
            if original_image_size is not None
            else (self.config.image_size, self.config.image_size)
        )
        original_height, original_width = original_image_size
        num_patches_h = original_height // patch_size
        num_patches_w = original_width // patch_size
        # sanity check
        if num_patches_h * num_patches_w != patchified_pixel_values.shape[1]:
            raise ValueError(
                f"The number of patches in the patchified pixel values {patchified_pixel_values.shape[1]}, does not match the number of patches on original image {num_patches_h}*{num_patches_w}"
            )

        # unpatchify
        batch_size = patchified_pixel_values.shape[0]
        patchified_pixel_values = patchified_pixel_values.reshape(
            batch_size,
            num_patches_h,
            num_patches_w,
            patch_size,
            patch_size,
            num_channels,
        )
        patchified_pixel_values = torch.einsum("nhwpqc->nchpwq", patchified_pixel_values)
        pixel_values = patchified_pixel_values.reshape(
            batch_size,
            num_channels,
            num_patches_h * patch_size,
            num_patches_w * patch_size,
        )
        return pixel_values

    def forward_loss(self, pixel_values, pred, mask, interpolate_pos_encoding: bool = False):
        """
        Args:
            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
                Pixel values.
            pred (`torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
                Predicted pixel values.
            mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
                Tensor indicating which patches are masked (1) and which are not (0).
            interpolate_pos_encoding (`bool`, *optional*, default `False`):
                interpolation flag passed during the forward pass.

        Returns:
            `torch.FloatTensor`: Pixel reconstruction loss.
        """
        target = self.patchify(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
        if self.config.norm_pix_loss:
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.0e-6) ** 0.5

        loss = (pred - target) ** 2
        loss = loss.mean(dim=-1)  # [N, L], mean loss per patch
        loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches
        return loss

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        noise: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        interpolate_pos_encoding: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> ViTMAEForPreTrainingOutput:
        r"""
        noise (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mainly used for testing purposes to control randomness and maintain the reproducibility
        interpolate_pos_encoding (`bool`, *optional*, default `False`):
            Whether to interpolate the pre-trained position encodings. This is mainly used to use the model on higher
            resolution images.

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, ViTMAEForPreTraining
        >>> 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("facebook/vit-mae-base")
        >>> model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")

        >>> inputs = image_processor(images=image, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> loss = outputs.loss
        >>> mask = outputs.mask
        >>> ids_restore = outputs.ids_restore
        ```"""

        outputs: ViTMAEModelOutput = self.vit(
            pixel_values, noise=noise, head_mask=head_mask, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs
        )

        latent = outputs.last_hidden_state
        ids_restore = outputs.ids_restore
        mask = outputs.mask

        decoder_outputs: ViTMAEDecoderOutput = self.decoder(
            latent, ids_restore, interpolate_pos_encoding=interpolate_pos_encoding
        )
        logits = decoder_outputs.logits  # shape (batch_size, num_patches, patch_size*patch_size*num_channels)

        loss = self.forward_loss(pixel_values, logits, mask, interpolate_pos_encoding=interpolate_pos_encoding)

        return ViTMAEForPreTrainingOutput(
            loss=loss,
            logits=logits,
            mask=mask,
            ids_restore=ids_restore,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = ["ViTMAEForPreTraining", "ViTMAELayer", "ViTMAEModel", "ViTMAEPreTrainedModel"]
