pae-dinov3-large-d32/decoder/decoder_pae.py

from __future__ import annotations

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import (
    ModelOutput,
)
from transformers.activations import ACT2FN
class ViTMAEConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ViTMAEModel`]. It is used to instantiate an ViT
    MAE model according to the specified arguments, defining the model architecture. Instantiating a configuration with
    the defaults will yield a similar configuration to that of the ViT
    [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) architecture.

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


    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        decoder_num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the decoder.
        decoder_hidden_size (`int`, *optional*, defaults to 512):
            Dimensionality of the decoder.
        decoder_num_hidden_layers (`int`, *optional*, defaults to 8):
            Number of hidden layers in the decoder.
        decoder_intermediate_size (`int`, *optional*, defaults to 2048):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder.
        mask_ratio (`float`, *optional*, defaults to 0.75):
            The ratio of the number of masked tokens in the input sequence.
        norm_pix_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to train with normalized pixels (see Table 3 in the paper). Using normalized pixels improved
            representation quality in the experiments of the authors.

    Example:

    ```python
    >>> from transformers import ViTMAEConfig, ViTMAEModel

    >>> # Initializing a ViT MAE vit-mae-base style configuration
    >>> configuration = ViTMAEConfig()

    >>> # Initializing a model (with random weights) from the vit-mae-base style configuration
    >>> model = ViTMAEModel(configuration)

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

    model_type = "vit_mae"

    def __init__(
        self,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        image_size=224,
        patch_size=16,
        num_channels=3,
        qkv_bias=True,
        decoder_num_attention_heads=16,
        decoder_hidden_size=512,
        decoder_num_hidden_layers=8,
        decoder_intermediate_size=2048,
        mask_ratio=0.75,
        norm_pix_loss=False,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.qkv_bias = qkv_bias
        self.decoder_num_attention_heads = decoder_num_attention_heads
        self.decoder_hidden_size = decoder_hidden_size
        self.decoder_num_hidden_layers = decoder_num_hidden_layers
        self.decoder_intermediate_size = decoder_intermediate_size
        self.mask_ratio = mask_ratio
        self.norm_pix_loss = norm_pix_loss

# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch ViT MAE (masked autoencoder) model."""

import collections.abc
import math
from copy import deepcopy
from dataclasses import dataclass
from typing import Optional, Set, Tuple, Union

import numpy as np
import torch
from torch import nn
# correct the above import to the following
from transformers.modeling_outputs import BaseModelOutput

try:
    from flash_attn import flash_attn_func
    HAS_FLASH_ATTN = True  # if set to False, FlashAttention is unavailable
except:
    print('FlashAttention is not installed.')
    HAS_FLASH_ATTN = False


@dataclass
class ViTMAEModelOutput(ModelOutput):
    """
    Class for ViTMAEModel's outputs, with potential hidden states and attentions.

    Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        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.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
            plus the initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
            the self-attention heads.
    """

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


@dataclass
class ViTMAEDecoderOutput(ModelOutput):
    """
    Class for ViTMAEDecoder's outputs, with potential hidden states and attentions.

    Args:
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
            Pixel reconstruction logits.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
            plus the initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
            the self-attention heads.
    """

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


@dataclass
class ViTMAEForPreTrainingOutput(ModelOutput):
    """
    Class for ViTMAEForPreTraining's outputs, with potential hidden states and attentions.

    Args:
        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.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
            plus the initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
            the self-attention heads.
    """

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    mask: torch.LongTensor = None
    ids_restore: 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.config = config
        self.initialize_weights()

    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)

    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,
        )
        if int(h0) != patch_pos_embed.shape[-2] or int(w0) != patch_pos_embed.shape[-1]:
            raise ValueError("Width or height does not match with the interpolated position embeddings")
        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 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.ViTSelfAttention ViT->ViTMAE
class ViTMAESelfAttention(nn.Module):
    def __init__(self, config: ViTMAEConfig) -> 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.has_flash_attn = HAS_FLASH_ATTN
        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.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.attention_probs_dropout_prob = config.attention_probs_dropout_prob

    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 reshape_for_flash(self, x: torch.Tensor) -> torch.Tensor:
        """Reshape to (B, N, num_heads, head_dim) for flash_attn_func."""
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        return x.view(new_x_shape)

    def forward(
        self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        # Use FlashAttention when available and no special outputs are needed
        if self.has_flash_attn and not output_attentions and head_mask is None:
            query_layer = self.reshape_for_flash(self.query(hidden_states))
            key_layer = self.reshape_for_flash(self.key(hidden_states))
            value_layer = self.reshape_for_flash(self.value(hidden_states))

            context_layer = flash_attn_func(
                query_layer, key_layer, value_layer,
                dropout_p=self.attention_probs_dropout_prob if self.training else 0.0,
                causal=False, deterministic=True
            )

            context_layer = context_layer.reshape(hidden_states.size(0), -1, self.all_head_size)
            return (context_layer,)

        # Fallback: manual attention computation
        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)

        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)

        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)

        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)

        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


# Copied from transformers.models.vit.modeling_vit.ViTSdpaSelfAttention ViT->ViTMAE
class ViTMAESdpaSelfAttention(ViTMAESelfAttention):
    def __init__(self, config: ViTMAEConfig) -> 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]]:
        # Use FlashAttention when available and no special outputs are needed
        if self.has_flash_attn and not output_attentions and head_mask is None:
            query_layer = self.reshape_for_flash(self.query(hidden_states))
            key_layer = self.reshape_for_flash(self.key(hidden_states))
            value_layer = self.reshape_for_flash(self.value(hidden_states))

            context_layer = flash_attn_func(
                query_layer, key_layer, value_layer,
                dropout_p=self.attention_probs_dropout_prob if self.training else 0.0,
                causal=False, deterministic=True
            )

            context_layer = context_layer.reshape(hidden_states.size(0), -1, self.all_head_size)
            return context_layer, None

        # Fallback: PyTorch SDPA
        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 = torch.nn.functional.scaled_dot_product_attention(
            query_layer,
            key_layer,
            value_layer,
            head_mask,
            self.attention_probs_dropout_prob if self.training else 0.0,
            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


# 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) -> 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


# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTMAE
class ViTMAEAttention(nn.Module):
    def __init__(self, config: ViTMAEConfig) -> None:
        super().__init__()
        self.attention = ViTMAESelfAttention(config)
        self.output = ViTMAESelfOutput(config)

    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

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


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



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

    def __init__(self, config: ViTMAEConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = ViTMAEAttention(config) # no SPDA by default
        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,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        self_attention_outputs = self.attention(
            self.layernorm_before(hidden_states),  # in ViTMAE, 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 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)

        outputs = (layer_output,) + outputs

        return outputs


class GeneralDecoder(nn.Module):
    def __init__(self, config, num_patches):
        super().__init__()
        self.decoder_embed = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=True)
        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 = config
        self.num_patches = num_patches
        self.initialize_weights(num_patches)
        self.decoder_config = decoder_config
        self.set_trainable_cls_token()
    def set_trainable_cls_token(self, tensor: Optional[torch.Tensor] = None):
        # register a trainable CLS token
        tensor = torch.zeros(1, 1, self.decoder_config.hidden_size) if tensor is None else tensor
        self.trainable_cls_token = nn.Parameter(tensor)
    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 deocder, that
        allows to interpolate the pre-trained decoder 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
        """

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

        # Separation of class token and patch tokens
        class_pos_embed = self.decoder_pos_embed[:, 0, :]
        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).
        # 1 keeps the other dimension constant
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            scale_factor=(1, embeddings_positions / num_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.unsqueeze(0), patch_pos_embed), dim=1)
    def interpolate_latent(self, x: torch.Tensor) -> torch.Tensor:
        b, l, c = x.shape
        if l == self.num_patches:
            return x
        # interpolate the latent
        #print(f"interpolating latent from {l} to {self.num_patches}, x.shape = {x.shape}")
        h, w = int(l**0.5), int(l**0.5)
        x = x.reshape(b, h, w, c)
        x = x.permute(0, 3, 1, 2)
        target_size = (int(self.num_patches**0.5), int(self.num_patches**0.5))
        x = nn.functional.interpolate(x, size=target_size, mode="bilinear", align_corners=False)
        x = x.permute(0, 2, 3, 1).contiguous().view(b, self.num_patches, c)
        return x
    
    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 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(
        self,
        hidden_states,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
        interpolate_pos_encoding: bool = False,
        drop_cls_token: bool = False,
    ):
        # embed tokens
        x = self.decoder_embed(hidden_states)
        #print(f"x.shape = {x.shape}")
        if drop_cls_token:
            x_ = x[:, 1:, :]  # no cls token
            x_ = self.interpolate_latent(x_)
        else:
            x_ = self.interpolate_latent(x) # interpolate the whole latent
        cls_token = self.trainable_cls_token.expand(x_.shape[0], -1, -1)
        x = torch.cat([cls_token, x_], dim=1)
        
        # add pos embed
        if interpolate_pos_encoding:
            assert drop_cls_token, "interpolate_pos_encoding only works with drop_cls_token=True"
            decoder_pos_embed = self.interpolate_pos_encoding(x)
        else:
            decoder_pos_embed = self.decoder_pos_embed
        hidden_states = x + decoder_pos_embed
        #print(f"hidden_states.shape = {hidden_states.shape}")
        # apply Transformer layers (blocks)
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.decoder_layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    layer_module.__call__,
                    hidden_states,
                    None,
                    output_attentions,
                )
            else:
                layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=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,)

        hidden_states = self.decoder_norm(hidden_states)

        # predictor projection
        logits = self.decoder_pred(hidden_states)

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

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

import json
from math import sqrt
from pathlib import Path
from typing import Any, Dict, Optional

import torch
import torch.nn as nn
from timm.models.vision_transformer import Attention
from transformers import AutoConfig, AutoImageProcessor

try:
    from diffusers.configuration_utils import ConfigMixin, register_to_config
    from diffusers.models.modeling_utils import ModelMixin
except Exception:  # pragma: no cover
    class ConfigMixin:
        config_name = "config.json"

    class ModelMixin(nn.Module):
        pass

    def register_to_config(init):
        return init


_PIXEL_DECODER_DEFAULTS: Dict[str, Any] = {
    "decoder_hidden_size": 1024,
    "decoder_intermediate_size": 4096,
    "decoder_num_attention_heads": 16,
    "decoder_num_hidden_layers": 24,
    "layer_norm_eps": 1e-12,
    "num_channels": 3,
    "hidden_act": "gelu",
    "qkv_bias": True,
    "hidden_dropout_prob": 0.0,
    "attention_probs_dropout_prob": 0.0,
    "initializer_range": 0.02,
}


def _resolve_local_asset_path(path: str, model_root: Optional[Path] = None) -> str:
    candidate = Path(path)
    if candidate.is_file():
        return str(candidate)
    search_roots = []
    if model_root is not None:
        search_roots.extend([model_root, model_root.parent])
    for root in search_roots:
        for probe in (root / candidate, root / candidate.name, root / "decoder_config" / "config.json"):
            if probe.is_file():
                return str(probe)
    return path


def _build_pixel_decoder_config(
    *,
    encoder_hidden_size: int,
    decoder_patch_size: int,
    base_patches: int,
    decoder_config_path: Optional[str] = None,
    overrides: Optional[Dict[str, Any]] = None,
) -> ViTMAEConfig:
    pixel_decoder = dict(_PIXEL_DECODER_DEFAULTS)
    if decoder_config_path is not None:
        resolved = _resolve_local_asset_path(decoder_config_path)
        if Path(resolved).is_file():
            with open(resolved, encoding="utf-8") as handle:
                pixel_decoder.update(json.load(handle))
    if overrides:
        pixel_decoder.update({key: value for key, value in overrides.items() if value is not None})

    return ViTMAEConfig(
        hidden_size=encoder_hidden_size,
        patch_size=decoder_patch_size,
        image_size=int(decoder_patch_size * sqrt(base_patches)),
        **pixel_decoder,
    )


def _load_image_normalization(encoder_config_path: str) -> tuple[list[float], list[float]]:
    try:
        proc = AutoImageProcessor.from_pretrained(encoder_config_path, use_fast=True, local_files_only=True)
        return proc.image_mean, proc.image_std
    except Exception:
        pass
    try:
        proc = AutoImageProcessor.from_pretrained(encoder_config_path, use_fast=True, local_files_only=False)
        return proc.image_mean, proc.image_std
    except Exception:
        if "siglip" in encoder_config_path.lower():
            return [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
        return [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]


class PAEDecoder(ModelMixin, ConfigMixin):
    """PAE decoder: latent decompressor + ViT-MAE pixel decoder."""

    @register_to_config
    def __init__(
        self,
        encoder_hidden_size: int = 1024,
        encoder_num_heads: int = 16,
        encoder_input_size: int = 224,
        encoder_patch_size: int = 14,
        encoder_config_path: str = "facebook/dinov2-with-registers-large",
        latent_dim: int = 32,
        decoder_patch_size: int = 16,
        decoder_config_path: Optional[str] = None,
        decoder_hidden_size: int = 1024,
        decoder_intermediate_size: int = 4096,
        decoder_num_attention_heads: int = 16,
        decoder_num_hidden_layers: int = 24,
        layer_norm_eps: float = 1e-12,
        num_channels: int = 3,
        hidden_act: str = "gelu",
        qkv_bias: bool = True,
        hidden_dropout_prob: float = 0.0,
        attention_probs_dropout_prob: float = 0.0,
        initializer_range: float = 0.02,
        image_mean: Optional[list[float]] = None,
        image_std: Optional[list[float]] = None,
        latent_mean: Optional[list[float]] = None,
        latent_std: Optional[list[float]] = None,
        latent_multiplier: float = 1.0,
        **kwargs,
    ) -> None:
        super().__init__()
        if image_mean is not None and image_std is not None:
            norm_mean, norm_std = image_mean, image_std
        else:
            norm_mean, norm_std = _load_image_normalization(encoder_config_path)
        self.register_buffer("encoder_mean", torch.tensor(norm_mean).view(1, 3, 1, 1), persistent=True)
        self.register_buffer("encoder_std", torch.tensor(norm_std).view(1, 3, 1, 1), persistent=True)

        self.encoder_input_size = encoder_input_size
        self.encoder_patch_size = encoder_patch_size
        self.base_patches = (self.encoder_input_size // self.encoder_patch_size) ** 2
        self.latent_dim = latent_dim

        self.latent_decompressor = nn.ModuleList(
            [
                nn.Conv2d(latent_dim, encoder_hidden_size, kernel_size=3, padding=1),
                Attention(dim=encoder_hidden_size, num_heads=encoder_num_heads),
            ]
        )

        pixel_decoder_config = _build_pixel_decoder_config(
            encoder_hidden_size=encoder_hidden_size,
            decoder_patch_size=decoder_patch_size,
            base_patches=self.base_patches,
            decoder_config_path=decoder_config_path,
            overrides={
                "decoder_hidden_size": decoder_hidden_size,
                "decoder_intermediate_size": decoder_intermediate_size,
                "decoder_num_attention_heads": decoder_num_attention_heads,
                "decoder_num_hidden_layers": decoder_num_hidden_layers,
                "layer_norm_eps": layer_norm_eps,
                "num_channels": num_channels,
                "hidden_act": hidden_act,
                "qkv_bias": qkv_bias,
                "hidden_dropout_prob": hidden_dropout_prob,
                "attention_probs_dropout_prob": attention_probs_dropout_prob,
                "initializer_range": initializer_range,
            },
        )
        self.decoder = GeneralDecoder(pixel_decoder_config, num_patches=self.base_patches)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        model_root = Path(pretrained_model_name_or_path)
        config_file = model_root / cls.config_name
        if config_file.is_file():
            with open(config_file, encoding="utf-8") as handle:
                config = json.load(handle)
            decoder_config_path = config.get("decoder_config_path")
            if decoder_config_path is not None:
                resolved = _resolve_local_asset_path(decoder_config_path, model_root=model_root)
                if resolved != decoder_config_path:
                    kwargs.setdefault("decoder_config_path", resolved)
        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

    def decode(self, latents: torch.Tensor) -> torch.Tensor:
        hidden_states = self.latent_decompressor[0](latents)
        batch_size, channels, height, width = hidden_states.shape
        num_tokens = height * width
        hidden_states = hidden_states.view(batch_size, channels, num_tokens).transpose(1, 2)
        hidden_states = self.latent_decompressor[1](hidden_states)

        output = self.decoder(hidden_states, drop_cls_token=False).logits
        x_rec = self.decoder.unpatchify(output)
        x_rec = x_rec * self.encoder_std.to(x_rec.device) + self.encoder_mean.to(x_rec.device)
        return x_rec

    def forward(self, latents: torch.Tensor) -> torch.Tensor:
        return self.decode(latents)