MinerU/mineru/model/utils/pytorchocr/modeling/backbones/rec_donut_swin.py

import collections.abc
from collections import OrderedDict
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

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


class DonutSwinConfig(object):
    model_type = "donut-swin"

    attribute_map = {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }

    def __init__(
        self,
        image_size=224,
        patch_size=4,
        num_channels=3,
        embed_dim=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        mlp_ratio=4.0,
        qkv_bias=True,
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        drop_path_rate=0.1,
        hidden_act="gelu",
        use_absolute_embeddings=False,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        **kwargs,
    ):
        super().__init__()

        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.depths = depths
        self.num_layers = len(depths)
        self.num_heads = num_heads
        self.window_size = window_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.drop_path_rate = drop_path_rate
        self.hidden_act = hidden_act
        self.use_absolute_embeddings = use_absolute_embeddings
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))

        for key, value in kwargs.items():
            try:
                setattr(self, key, value)
            except AttributeError as err:
                print(f"Can't set {key} with value {value} for {self}")
                raise err


@dataclass
# Copied from transformers.models.swin.modeling_swin.SwinEncoderOutput with Swin->DonutSwin
class DonutSwinEncoderOutput(OrderedDict):
    last_hidden_state = None
    hidden_states = None
    attentions = None
    reshaped_hidden_states = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def __getitem__(self, k):
        if isinstance(k, str):
            inner_dict = dict(self.items())
            return inner_dict[k]
        else:
            return self.to_tuple()[k]

    def __setattr__(self, name, value):
        if name in self.keys() and value is not None:
            super().__setitem__(name, value)
        super().__setattr__(name, value)

    def __setitem__(self, key, value):
        super().__setitem__(key, value)
        super().__setattr__(key, value)

    def to_tuple(self):
        """
        Convert self to a tuple containing all the attributes/keys that are not `None`.
        """
        return tuple(self[k] for k in self.keys())


@dataclass
# Copied from transformers.models.swin.modeling_swin.SwinModelOutput with Swin->DonutSwin
class DonutSwinModelOutput(OrderedDict):
    last_hidden_state = None
    pooler_output = None
    hidden_states = None
    attentions = None
    reshaped_hidden_states = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def __getitem__(self, k):
        if isinstance(k, str):
            inner_dict = dict(self.items())
            return inner_dict[k]
        else:
            return self.to_tuple()[k]

    def __setattr__(self, name, value):
        if name in self.keys() and value is not None:
            super().__setitem__(name, value)
        super().__setattr__(name, value)

    def __setitem__(self, key, value):
        super().__setitem__(key, value)
        super().__setattr__(key, value)

    def to_tuple(self):
        """
        Convert self to a tuple containing all the attributes/keys that are not `None`.
        """
        return tuple(self[k] for k in self.keys())


# Copied from transformers.models.swin.modeling_swin.window_partition
def window_partition(input_feature, window_size):
    """
    Partitions the given input into windows.
    """
    batch_size, height, width, num_channels = input_feature.shape
    input_feature = input_feature.reshape(
        [
            batch_size,
            height // window_size,
            window_size,
            width // window_size,
            window_size,
            num_channels,
        ]
    )
    windows = input_feature.transpose([0, 1, 3, 2, 4, 5]).reshape(
        [-1, window_size, window_size, num_channels]
    )
    return windows


# Copied from transformers.models.swin.modeling_swin.window_reverse
def window_reverse(windows, window_size, height, width):
    """
    Merges windows to produce higher resolution features.
    """
    num_channels = windows.shape[-1]
    windows = windows.reshape(
        [
            -1,
            height // window_size,
            width // window_size,
            window_size,
            window_size,
            num_channels,
        ]
    )
    windows = windows.transpose([0, 1, 3, 2, 4, 5]).reshape(
        [-1, height, width, num_channels]
    )
    return windows


# Copied from transformers.models.swin.modeling_swin.SwinEmbeddings with Swin->DonutSwin
class DonutSwinEmbeddings(nn.Module):
    """
    Construct the patch and position embeddings. Optionally, also the mask token.
    """

    def __init__(self, config, use_mask_token=False):
        super().__init__()

        self.patch_embeddings = DonutSwinPatchEmbeddings(config)
        num_patches = self.patch_embeddings.num_patches
        self.patch_grid = self.patch_embeddings.grid_size
        if use_mask_token:
            # self.mask_token = paddle.create_parameter(
            #     [1, 1, config.embed_dim], dtype="float32"
            # )
            self.mask_token = nn.Parameter(
                nn.init.xavier_uniform_(torch.zeros(1, 1, config.embed_dim).to(torch.float32))
            )
            nn.init.zeros_(self.mask_token)
        else:
            self.mask_token = None
        if config.use_absolute_embeddings:
            # self.position_embeddings = paddle.create_parameter(
            #     [1, num_patches + 1, config.embed_dim], dtype="float32"
            # )
            self.position_embeddings = nn.Parameter(
                nn.init.xavier_uniform_(torch.zeros(1, num_patches + 1, config.embed_dim).to(torch.float32))
            )
            nn.init.zeros_(self.position_embedding)
        else:
            self.position_embeddings = None

        self.norm = nn.LayerNorm(config.embed_dim)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, pixel_values, bool_masked_pos=None):

        embeddings, output_dimensions = self.patch_embeddings(pixel_values)
        embeddings = self.norm(embeddings)

        batch_size, seq_len, _ = embeddings.shape

        if bool_masked_pos is not None:
            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask

        if self.position_embeddings is not None:
            embeddings = embeddings + self.position_embeddings
        embeddings = self.dropout(embeddings)
        return embeddings, output_dimensions


class MyConv2d(nn.Conv2d):
    def __init__(
        self,
        in_channel,
        out_channels,
        kernel_size,
        stride=1,
        padding="SAME",
        dilation=1,
        groups=1,
        bias_attr=False,
        eps=1e-6,
    ):
        super().__init__(
            in_channel,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias_attr=bias_attr,
        )
        # self.weight = paddle.create_parameter(
        #     [out_channels, in_channel, kernel_size[0], kernel_size[1]], dtype="float32"
        # )
        self.weight = torch.Parameter(
            nn.init.xavier_uniform_(
                torch.zeros(out_channels, in_channel, kernel_size[0], kernel_size[1]).to(torch.float32)
            )
        )
        # self.bias = paddle.create_parameter([out_channels], dtype="float32")
        self.bias = torch.Parameter(
            nn.init.xavier_uniform_(
                torch.zeros(out_channels).to(torch.float32)
            )
        )
        nn.init.ones_(self.weight)
        nn.init.zeros_(self.bias)

    def forward(self, x):
        x = F.conv2d(
            x,
            self.weight,
            self.bias,
            self._stride,
            self._padding,
            self._dilation,
            self._groups,
        )
        return x


# Copied from transformers.models.swin.modeling_swin.SwinPatchEmbeddings
class DonutSwinPatchEmbeddings(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.embed_dim
        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.is_export = config.is_export
        self.grid_size = (
            image_size[0] // patch_size[0],
            image_size[1] // patch_size[1],
        )
        self.projection = nn.Conv2D(
            num_channels, hidden_size, kernel_size=patch_size, stride=patch_size
        )

    def maybe_pad(self, pixel_values, height, width):
        if width % self.patch_size[1] != 0:
            pad_values = (0, self.patch_size[1] - width % self.patch_size[1])
            if self.is_export:
                pad_values = torch.tensor(pad_values, dtype=torch.int32)
            pixel_values = nn.functional.pad(pixel_values, pad_values)
        if height % self.patch_size[0] != 0:
            pad_values = (0, 0, 0, self.patch_size[0] - height % self.patch_size[0])
            if self.is_export:
                pad_values = torch.tensor(pad_values, dtype=torch.int32)
            pixel_values = nn.functional.pad(pixel_values, pad_values)
        return pixel_values

    def forward(self, pixel_values) -> Tuple[torch.Tensor, Tuple[int]]:
        _, 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."
            )
        pixel_values = self.maybe_pad(pixel_values, height, width)
        embeddings = self.projection(pixel_values)

        _, _, height, width = embeddings.shape
        output_dimensions = (height, width)
        embeddings = embeddings.flatten(2).transpose([0, 2, 1])

        return embeddings, output_dimensions


# Copied from transformers.models.swin.modeling_swin.SwinPatchMerging
class DonutSwinPatchMerging(nn.Module):
    """
    Patch Merging Layer.

    Args:
        input_resolution (`Tuple[int]`):
            Resolution of input feature.
        dim (`int`):
            Number of input channels.
        norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
            Normalization layer class.
    """

    def __init__(
        self,
        input_resolution: Tuple[int],
        dim: int,
        norm_layer: nn.Module = nn.LayerNorm,
        is_export=False,
    ):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias_attr=False)
        self.norm = norm_layer(4 * dim)
        self.is_export = is_export

    def maybe_pad(self, input_feature, height, width):
        should_pad = (height % 2 == 1) or (width % 2 == 1)
        if should_pad:
            pad_values = (0, 0, 0, width % 2, 0, height % 2)
            if self.is_export:
                pad_values = torch.tensor(pad_values, dtype=torch.int32)
            input_feature = nn.functional.pad(input_feature, pad_values)

        return input_feature

    def forward(
        self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]
    ) -> torch.Tensor:
        height, width = input_dimensions
        batch_size, dim, num_channels = input_feature.shape

        input_feature = input_feature.reshape([batch_size, height, width, num_channels])

        input_feature = self.maybe_pad(input_feature, height, width)
        input_feature_0 = input_feature[:, 0::2, 0::2, :]
        input_feature_1 = input_feature[:, 1::2, 0::2, :]
        input_feature_2 = input_feature[:, 0::2, 1::2, :]
        input_feature_3 = input_feature[:, 1::2, 1::2, :]
        input_feature = torch.cat(
            [input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1
        )
        input_feature = input_feature.reshape(
            [batch_size, -1, 4 * num_channels]
        )  # batch_size height/2*width/2 4*C

        input_feature = self.norm(input_feature)
        input_feature = self.reduction(input_feature)

        return input_feature


# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(
    input: torch.Tensor, drop_prob: float = 0.0, training: bool = False
) -> torch.Tensor:
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (
        input.ndim - 1
    )  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(
        shape,
        dtype=input.dtype,
    )
    random_tensor.floor_()  # binarize
    output = input / keep_prob * random_tensor
    return output


# Copied from transformers.models.swin.modeling_swin.SwinDropPath
class DonutSwinDropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: Optional[float] = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return drop_path(hidden_states, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return "p={}".format(self.drop_prob)


class DonutSwinSelfAttention(nn.Module):
    def __init__(self, config, dim, num_heads, window_size):
        super().__init__()
        if dim % num_heads != 0:
            raise ValueError(
                f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
            )

        self.num_attention_heads = num_heads
        self.attention_head_size = int(dim / num_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.window_size = (
            window_size
            if isinstance(window_size, collections.abc.Iterable)
            else (window_size, window_size)
        )
        # self.relative_position_bias_table = paddle.create_parameter(
        #     [(2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), num_heads],
        #     dtype="float32",
        # )
        self.relative_position_bias_table = torch.Parameter(
            nn.init.xavier_normal_(
                torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), num_heads).to(torch.float32)
            )
        )

        nn.init.zeros_(self.relative_position_bias_table)

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij"))
        coords_flatten = torch.flatten(coords, 1)
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
        relative_coords = relative_coords.transpose([1, 2, 0])
        relative_coords[:, :, 0] += self.window_size[0] - 1
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)
        self.register_buffer("relative_position_index", relative_position_index)

        self.query = nn.Linear(
            self.all_head_size, self.all_head_size, bias_attr=config.qkv_bias
        )
        self.key = nn.Linear(
            self.all_head_size, self.all_head_size, bias_attr=config.qkv_bias
        )
        self.value = nn.Linear(
            self.all_head_size, self.all_head_size, bias_attr=config.qkv_bias
        )

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        new_x_shape = x.shape[:-1] + [
            self.num_attention_heads,
            self.attention_head_size,
        ]
        x = x.reshape(new_x_shape)
        return x.transpose([0, 2, 1, 3])

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask=None,
        head_mask=None,
        output_attentions=False,
    ) -> Tuple[torch.Tensor]:
        batch_size, dim, num_channels = hidden_states.shape
        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([0, 1, 3, 2]))

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

        relative_position_bias = self.relative_position_bias_table[
            self.relative_position_index.reshape([-1])
        ]
        relative_position_bias = relative_position_bias.reshape(
            [
                self.window_size[0] * self.window_size[1],
                self.window_size[0] * self.window_size[1],
                -1,
            ]
        )

        relative_position_bias = relative_position_bias.transpose([2, 0, 1])
        attention_scores = attention_scores + relative_position_bias.unsqueeze(0)

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in DonutSwinModel forward() function)
            mask_shape = attention_mask.shape[0]
            attention_scores = attention_scores.reshape(
                [
                    batch_size // mask_shape,
                    mask_shape,
                    self.num_attention_heads,
                    dim,
                    dim,
                ]
            )
            attention_scores = attention_scores + attention_mask.unsqueeze(1).unsqueeze(
                0
            )
            attention_scores = attention_scores.reshape(
                [-1, self.num_attention_heads, dim, dim]
            )

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, axis=-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)
        context_layer = context_layer.transpose([0, 2, 1, 3])
        new_context_layer_shape = tuple(context_layer.shape[:-2]) + (
            self.all_head_size,
        )
        context_layer = context_layer.reshape(new_context_layer_shape)
        outputs = (
            (context_layer, attention_probs) if output_attentions else (context_layer,)
        )
        return outputs


# Copied from transformers.models.swin.modeling_swin.SwinSelfOutput
class DonutSwinSelfOutput(nn.Module):
    def __init__(self, config, dim):
        super().__init__()
        self.dense = nn.Linear(dim, dim)
        self.dropout = nn.Dropout(config.attention_probs_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.swin.modeling_swin.SwinAttention with Swin->DonutSwin
class DonutSwinAttention(nn.Module):
    def __init__(self, config, dim, num_heads, window_size):
        super().__init__()
        self.self = DonutSwinSelfAttention(config, dim, num_heads, window_size)
        self.output = DonutSwinSelfOutput(config, dim)
        self.pruned_heads = set()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask=None,
        head_mask=None,
        output_attentions=False,
    ) -> Tuple[torch.Tensor]:
        self_outputs = self.self(
            hidden_states, attention_mask, 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.swin.modeling_swin.SwinIntermediate
class DonutSwinIntermediate(nn.Module):
    def __init__(self, config, dim):
        super().__init__()
        self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
        self.intermediate_act_fn = F.gelu

    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.swin.modeling_swin.SwinOutput
class DonutSwinOutput(nn.Module):
    def __init__(self, config, dim):
        super().__init__()
        self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

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


# Copied from transformers.models.swin.modeling_swin.SwinLayer with Swin->DonutSwin
class DonutSwinLayer(nn.Module):
    def __init__(self, config, dim, input_resolution, num_heads, shift_size=0):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.shift_size = shift_size
        self.window_size = config.window_size
        self.input_resolution = input_resolution
        self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
        self.attention = DonutSwinAttention(
            config, dim, num_heads, window_size=self.window_size
        )
        self.drop_path = (
            DonutSwinDropPath(config.drop_path_rate)
            if config.drop_path_rate > 0.0
            else nn.Identity()
        )
        self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
        self.intermediate = DonutSwinIntermediate(config, dim)
        self.output = DonutSwinOutput(config, dim)
        self.is_export = config.is_export

    def set_shift_and_window_size(self, input_resolution):
        if min(input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(input_resolution)

    def get_attn_mask_export(self, height, width, dtype):

        attn_mask = None
        height_slices = (
            slice(0, -self.window_size),
            slice(-self.window_size, -self.shift_size),
            slice(-self.shift_size, None),
        )
        width_slices = (
            slice(0, -self.window_size),
            slice(-self.window_size, -self.shift_size),
            slice(-self.shift_size, None),
        )
        img_mask = torch.zeros((1, height, width, 1), dtype=dtype)
        count = 0
        for height_slice in height_slices:
            for width_slice in width_slices:
                if self.shift_size > 0:
                    img_mask[:, height_slice, width_slice, :] = count
                    count += 1
        if torch.Tensor(self.shift_size > 0).to(torch.bool):
            # calculate attention mask for SW-MSA
            mask_windows = window_partition(img_mask, self.window_size)
            mask_windows = mask_windows.reshape(
                [-1, self.window_size * self.window_size]
            )
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(
                attn_mask != 0, float(-100.0)
            ).masked_fill(attn_mask == 0, float(0.0))

        return attn_mask

    def get_attn_mask(self, height, width, dtype):
        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            img_mask = torch.zeros((1, height, width, 1), dtype=dtype)
            height_slices = (
                slice(0, -self.window_size),
                slice(-self.window_size, -self.shift_size),
                slice(-self.shift_size, None),
            )
            width_slices = (
                slice(0, -self.window_size),
                slice(-self.window_size, -self.shift_size),
                slice(-self.shift_size, None),
            )

            count = 0
            for height_slice in height_slices:
                for width_slice in width_slices:
                    img_mask[:, height_slice, width_slice, :] = count
                    count += 1

            mask_windows = window_partition(img_mask, self.window_size)
            mask_windows = mask_windows.reshape(
                [-1, self.window_size * self.window_size]
            )
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(
                attn_mask != 0, float(-100.0)
            ).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None
        return attn_mask

    def maybe_pad(self, hidden_states, height, width):
        pad_right = (self.window_size - width % self.window_size) % self.window_size
        pad_bottom = (self.window_size - height % self.window_size) % self.window_size
        pad_values = (0, 0, 0, pad_bottom, 0, pad_right, 0, 0)
        hidden_states = nn.functional.pad(hidden_states, pad_values)
        return hidden_states, pad_values

    def forward(
        self,
        hidden_states: torch.Tensor,
        input_dimensions: Tuple[int, int],
        head_mask=None,
        output_attentions=False,
        always_partition=False,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        if not always_partition:
            self.set_shift_and_window_size(input_dimensions)
        else:
            pass
        height, width = input_dimensions
        batch_size, _, channels = hidden_states.shape
        shortcut = hidden_states

        hidden_states = self.layernorm_before(hidden_states)

        hidden_states = hidden_states.reshape([batch_size, height, width, channels])

        # pad hidden_states to multiples of window size
        hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)

        _, height_pad, width_pad, _ = hidden_states.shape

        # cyclic shift
        if self.shift_size > 0:
            shift_value = (-self.shift_size, -self.shift_size)
            if self.is_export:
                shift_value = torch.tensor(shift_value, dtype=torch.int32)
            shifted_hidden_states = torch.roll(
                hidden_states, shifts=shift_value, dims=(1, 2)
            )
        else:
            shifted_hidden_states = hidden_states

        # partition windows
        hidden_states_windows = window_partition(
            shifted_hidden_states, self.window_size
        )
        hidden_states_windows = hidden_states_windows.reshape(
            [-1, self.window_size * self.window_size, channels]
        )
        attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype)

        attention_outputs = self.attention(
            hidden_states_windows,
            attn_mask,
            head_mask,
            output_attentions=output_attentions,
        )
        attention_output = attention_outputs[0]

        attention_windows = attention_output.reshape(
            [-1, self.window_size, self.window_size, channels]
        )
        shifted_windows = window_reverse(
            attention_windows, self.window_size, height_pad, width_pad
        )
        # reverse cyclic shift
        if self.shift_size > 0:
            shift_value = (self.shift_size, self.shift_size)
            if self.is_export:
                shift_value = torch.tensor(shift_value, dtype=torch.int32)
            attention_windows = torch.roll(
                shifted_windows, shifts=shift_value, dims=(1, 2)
            )
        else:
            attention_windows = shifted_windows

        was_padded = pad_values[3] > 0 or pad_values[5] > 0
        if was_padded:
            attention_windows = attention_windows[:, :height, :width, :].contiguous()

        attention_windows = attention_windows.reshape(
            [batch_size, height * width, channels]
        )
        hidden_states = shortcut + self.drop_path(attention_windows)
        layer_output = self.layernorm_after(hidden_states)
        layer_output = self.intermediate(layer_output)
        layer_output = hidden_states + self.output(layer_output)
        layer_outputs = (
            (layer_output, attention_outputs[1])
            if output_attentions
            else (layer_output,)
        )
        return layer_outputs


# Copied from transformers.models.swin.modeling_swin.SwinStage with Swin->DonutSwin
class DonutSwinStage(nn.Module):
    def __init__(
        self, config, dim, input_resolution, depth, num_heads, drop_path, downsample
    ):
        super().__init__()
        self.config = config
        self.dim = dim
        self.blocks = nn.ModuleList(
            [
                DonutSwinLayer(
                    config=config,
                    dim=dim,
                    input_resolution=input_resolution,
                    num_heads=num_heads,
                    shift_size=0 if (i % 2 == 0) else config.window_size // 2,
                )
                for i in range(depth)
            ]
        )
        self.is_export = config.is_export

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(
                input_resolution,
                dim=dim,
                norm_layer=nn.LayerNorm,
                is_export=self.is_export,
            )
        else:
            self.downsample = None

        self.pointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        input_dimensions: Tuple[int, int],
        head_mask=None,
        output_attentions=False,
        always_partition=False,
    ) -> Tuple[torch.Tensor]:
        height, width = input_dimensions

        for i, layer_module in enumerate(self.blocks):
            layer_head_mask = head_mask[i] if head_mask is not None else None

            layer_outputs = layer_module(
                hidden_states,
                input_dimensions,
                layer_head_mask,
                output_attentions,
                always_partition,
            )

            hidden_states = layer_outputs[0]

        hidden_states_before_downsampling = hidden_states
        if self.downsample is not None:
            height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
            output_dimensions = (height, width, height_downsampled, width_downsampled)
            hidden_states = self.downsample(
                hidden_states_before_downsampling, input_dimensions
            )
        else:
            output_dimensions = (height, width, height, width)

        stage_outputs = (
            hidden_states,
            hidden_states_before_downsampling,
            output_dimensions,
        )

        if output_attentions:
            stage_outputs += layer_outputs[1:]
        return stage_outputs


# Copied from transformers.models.swin.modeling_swin.SwinEncoder with Swin->DonutSwin
class DonutSwinEncoder(nn.Module):
    def __init__(self, config, grid_size):
        super().__init__()
        self.num_layers = len(config.depths)
        self.config = config
        dpr = [
            x.item()
            for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))
        ]
        self.layers = nn.ModuleList(
            [
                DonutSwinStage(
                    config=config,
                    dim=int(config.embed_dim * 2**i_layer),
                    input_resolution=(
                        grid_size[0] // (2**i_layer),
                        grid_size[1] // (2**i_layer),
                    ),
                    depth=config.depths[i_layer],
                    num_heads=config.num_heads[i_layer],
                    drop_path=dpr[
                        sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])
                    ],
                    downsample=(
                        DonutSwinPatchMerging
                        if (i_layer < self.num_layers - 1)
                        else None
                    ),
                )
                for i_layer in range(self.num_layers)
            ]
        )

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        input_dimensions: Tuple[int, int],
        head_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        output_hidden_states_before_downsampling=False,
        always_partition=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_reshaped_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if output_hidden_states:
            batch_size, _, hidden_size = hidden_states.shape
            reshaped_hidden_state = hidden_states.view(
                batch_size, *input_dimensions, hidden_size
            )
            reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
            all_hidden_states += (hidden_states,)
            all_reshaped_hidden_states += (reshaped_hidden_state,)

        for i, layer_module in enumerate(self.layers):
            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,
                    input_dimensions,
                    layer_head_mask,
                    output_attentions,
                    always_partition,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    input_dimensions,
                    layer_head_mask,
                    output_attentions,
                    always_partition,
                )

            hidden_states = layer_outputs[0]

            hidden_states_before_downsampling = layer_outputs[1]
            output_dimensions = layer_outputs[2]

            input_dimensions = (output_dimensions[-2], output_dimensions[-1])

            if output_hidden_states and output_hidden_states_before_downsampling:
                batch_size, _, hidden_size = hidden_states_before_downsampling.shape
                reshaped_hidden_state = hidden_states_before_downsampling.reshape(
                    [
                        batch_size,
                        *(output_dimensions[0], output_dimensions[1]),
                        hidden_size,
                    ]
                )
                reshaped_hidden_state = reshaped_hidden_state.transpose([0, 3, 1, 2])
                all_hidden_states += (hidden_states_before_downsampling,)
                all_reshaped_hidden_states += (reshaped_hidden_state,)
            elif output_hidden_states and not output_hidden_states_before_downsampling:
                batch_size, _, hidden_size = hidden_states.shape
                reshaped_hidden_state = hidden_states.reshape(
                    [batch_size, *input_dimensions, hidden_size]
                )
                reshaped_hidden_state = reshaped_hidden_state.transpose([0, 3, 1, 2])
                all_hidden_states += (hidden_states,)
                all_reshaped_hidden_states += (reshaped_hidden_state,)

            if output_attentions:
                all_self_attentions += layer_outputs[3:]

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

        return DonutSwinEncoderOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            reshaped_hidden_states=all_reshaped_hidden_states,
        )


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

    config_class = DonutSwinConfig
    base_model_prefix = "swin"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2D)):
            # normal_ = Normal(mean=0.0, std=self.config.initializer_range)
            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.LayerNorm):
            nn.init.zeros_(module.bias)
            nn.init.ones_(module.weight)

    def _initialize_weights(self, module):
        """
        Initialize the weights if they are not already initialized.
        """
        if getattr(module, "_is_hf_initialized", False):
            return
        self._init_weights(module)

    def post_init(self):
        self.apply(self._initialize_weights)

    def get_head_mask(self, head_mask, num_hidden_layers, is_attention_chunked=False):
        if head_mask is not None:
            head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
            if is_attention_chunked is True:
                head_mask = head_mask.unsqueeze(-1)
        else:
            head_mask = [None] * num_hidden_layers

        return head_mask


class DonutSwinModel(DonutSwinPreTrainedModel):
    def __init__(
        self,
        in_channels=3,
        hidden_size=1024,
        num_layers=4,
        num_heads=[4, 8, 16, 32],
        add_pooling_layer=True,
        use_mask_token=False,
        is_export=False,
    ):
        super().__init__()
        donut_swin_config = {
            "return_dict": True,
            "output_hidden_states": False,
            "output_attentions": False,
            "use_bfloat16": False,
            "tf_legacy_loss": False,
            "pruned_heads": {},
            "tie_word_embeddings": True,
            "chunk_size_feed_forward": 0,
            "is_encoder_decoder": False,
            "is_decoder": False,
            "cross_attention_hidden_size": None,
            "add_cross_attention": False,
            "tie_encoder_decoder": False,
            "max_length": 20,
            "min_length": 0,
            "do_sample": False,
            "early_stopping": False,
            "num_beams": 1,
            "num_beam_groups": 1,
            "diversity_penalty": 0.0,
            "temperature": 1.0,
            "top_k": 50,
            "top_p": 1.0,
            "typical_p": 1.0,
            "repetition_penalty": 1.0,
            "length_penalty": 1.0,
            "no_repeat_ngram_size": 0,
            "encoder_no_repeat_ngram_size": 0,
            "bad_words_ids": None,
            "num_return_sequences": 1,
            "output_scores": False,
            "return_dict_in_generate": False,
            "forced_bos_token_id": None,
            "forced_eos_token_id": None,
            "remove_invalid_values": False,
            "exponential_decay_length_penalty": None,
            "suppress_tokens": None,
            "begin_suppress_tokens": None,
            "architectures": None,
            "finetuning_task": None,
            "id2label": {0: "LABEL_0", 1: "LABEL_1"},
            "label2id": {"LABEL_0": 0, "LABEL_1": 1},
            "tokenizer_class": None,
            "prefix": None,
            "bos_token_id": None,
            "pad_token_id": None,
            "eos_token_id": None,
            "sep_token_id": None,
            "decoder_start_token_id": None,
            "task_specific_params": None,
            "problem_type": None,
            "_name_or_path": "",
            "_commit_hash": None,
            "_attn_implementation_internal": None,
            "transformers_version": None,
            "hidden_size": hidden_size,
            "num_layers": num_layers,
            "path_norm": True,
            "use_2d_embeddings": False,
            "image_size": [420, 420],
            "patch_size": 4,
            "num_channels": in_channels,
            "embed_dim": 128,
            "depths": [2, 2, 14, 2],
            "num_heads": num_heads,
            "window_size": 5,
            "mlp_ratio": 4.0,
            "qkv_bias": True,
            "hidden_dropout_prob": 0.0,
            "attention_probs_dropout_prob": 0.0,
            "drop_path_rate": 0.1,
            "hidden_act": "gelu",
            "use_absolute_embeddings": False,
            "layer_norm_eps": 1e-05,
            "initializer_range": 0.02,
            "is_export": is_export,
        }

        config = DonutSwinConfig(**donut_swin_config)
        self.config = config
        self.num_layers = len(config.depths)
        self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))

        self.embeddings = DonutSwinEmbeddings(config, use_mask_token=use_mask_token)
        self.encoder = DonutSwinEncoder(config, self.embeddings.patch_grid)

        self.pooler = nn.AdaptiveAvgPool1D(1) if add_pooling_layer else None
        self.out_channels = hidden_size
        self.post_init()

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

    def forward(
        self,
        input_data=None,
        bool_masked_pos=None,
        head_mask=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ) -> Union[Tuple, DonutSwinModelOutput]:
        r"""
        bool_masked_pos (`paddle.BoolTensor` of shape `(batch_size, num_patches)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        """
        if self.training:
            pixel_values, label, attention_mask = input_data
        else:
            if isinstance(input_data, list):
                pixel_values = input_data[0]
            else:
                pixel_values = input_data
        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.return_dict
        )

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")
        num_channels = pixel_values.shape[1]
        if num_channels == 1:
            pixel_values = torch.repeat_interleave(pixel_values, repeats=3, dim=1)

        head_mask = self.get_head_mask(head_mask, len(self.config.depths))

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

        encoder_outputs = self.encoder(
            embedding_output,
            input_dimensions,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]

        pooled_output = None
        if self.pooler is not None:
            pooled_output = self.pooler(sequence_output.transpose([0, 2, 1]))
            pooled_output = torch.flatten(pooled_output, 1)

        if not return_dict:
            output = (sequence_output, pooled_output) + encoder_outputs[1:]
            return output

        donut_swin_output = DonutSwinModelOutput(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
        )
        if self.training:
            return donut_swin_output, label, attention_mask
        else:
            return donut_swin_output