# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""MaskFormer Swin Transformer. The reason Swin Transformer is implemented here is because MaskFormer uses the hidden
states before downsampling, which is different from the default Swin Transformer."""

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

import torch
from torch import Tensor, nn

from ...activations import ACT2FN
from ...file_utils import ModelOutput
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, meshgrid, prune_linear_layer
from ...utils import torch_int
from ...utils.backbone_utils import BackboneMixin
from .configuration_maskformer_swin import MaskFormerSwinConfig


@dataclass
class MaskFormerSwinModelOutputWithPooling(ModelOutput):
    """
    Class for MaskFormerSwinModel's outputs that also contains the spatial dimensions of the hidden states.

    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.
        pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
            Last layer hidden-state after a mean pooling operation.
        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.
        hidden_states_spatial_dimensions (`tuple(tuple(int, int))`, *optional*):
            A tuple containing the spatial dimension of each `hidden_state` needed to reshape the `hidden_states` to
            `batch, channels, height, width`. Due to padding, their spatial size cannot be inferred before the
            `forward` method.
        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
    pooler_output: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    hidden_states_spatial_dimensions: Tuple[Tuple[int, int]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


@dataclass
class MaskFormerSwinBaseModelOutput(ModelOutput):
    """
    Class for SwinEncoder's outputs.

    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.
        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.
        hidden_states_spatial_dimensions (`tuple(tuple(int, int))`, *optional*):
            A tuple containing the spatial dimension of each `hidden_state` needed to reshape the `hidden_states` to
            `batch, channels, height, width`. Due to padding, their spatial size cannot inferred before the `forward`
            method.
        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
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    hidden_states_spatial_dimensions: Tuple[Tuple[int, int]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


# 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.view(
        batch_size, height // window_size, window_size, width // window_size, window_size, num_channels
    )
    windows = input_feature.permute(0, 1, 3, 2, 4, 5).contiguous().view(-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.view(-1, height // window_size, width // window_size, window_size, window_size, num_channels)
    windows = windows.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, height, width, num_channels)
    return windows


# Copied from transformers.models.swin.modeling_swin.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
    argument.
    """
    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, device=input.device)
    random_tensor.floor_()  # binarize
    output = input.div(keep_prob) * random_tensor
    return output


class MaskFormerSwinEmbeddings(nn.Module):
    """
    Construct the patch and position embeddings.
    """

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

        self.patch_embeddings = MaskFormerSwinPatchEmbeddings(config)
        num_patches = self.patch_embeddings.num_patches
        self.patch_grid = self.patch_embeddings.grid_size

        if config.use_absolute_embeddings:
            self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.embed_dim))
        else:
            self.position_embeddings = None

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

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

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

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1

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

        class_pos_embed = self.position_embeddings[:, :1]
        patch_pos_embed = self.position_embeddings[:, 1:]

        dim = embeddings.shape[-1]

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

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

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

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

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

    def forward(self, pixel_values, interpolate_pos_encoding):
        _, num_channels, height, width = pixel_values.shape
        embeddings, output_dimensions = self.patch_embeddings(pixel_values)
        embeddings = self.norm(embeddings)

        if self.position_embeddings is not None:
            if interpolate_pos_encoding:
                embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
            else:
                embeddings = embeddings + self.position_embeddings

        embeddings = self.dropout(embeddings)

        return embeddings, output_dimensions


# Copied from transformers.models.swin.modeling_swin.SwinPatchEmbeddings with Swin->MaskFormerSwin
class MaskFormerSwinPatchEmbeddings(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.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])
            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])
            pixel_values = nn.functional.pad(pixel_values, pad_values)
        return pixel_values

    def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
        _, num_channels, height, width = pixel_values.shape
        # pad the input to be divisible by self.patch_size, if needed
        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(1, 2)

        return embeddings, output_dimensions


# Copied from transformers.models.swin.modeling_swin.SwinPatchMerging
class MaskFormerSwinPatchMerging(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) -> None:
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    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)
            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
        # `dim` is height * width
        batch_size, dim, num_channels = input_feature.shape

        input_feature = input_feature.view(batch_size, height, width, num_channels)
        # pad input to be disible by width and height, if needed
        input_feature = self.maybe_pad(input_feature, height, width)
        # [batch_size, height/2, width/2, num_channels]
        input_feature_0 = input_feature[:, 0::2, 0::2, :]
        # [batch_size, height/2, width/2, num_channels]
        input_feature_1 = input_feature[:, 1::2, 0::2, :]
        # [batch_size, height/2, width/2, num_channels]
        input_feature_2 = input_feature[:, 0::2, 1::2, :]
        # [batch_size, height/2, width/2, num_channels]
        input_feature_3 = input_feature[:, 1::2, 1::2, :]
        # batch_size height/2 width/2 4*num_channels
        input_feature = torch.cat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1)
        input_feature = input_feature.view(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.swin.modeling_swin.SwinDropPath with Swin->MaskFormerSwin
class MaskFormerSwinDropPath(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)


# Copied from transformers.models.swin.modeling_swin.SwinSelfAttention with Swin->MaskFormerSwin
class MaskFormerSwinSelfAttention(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 = nn.Parameter(
            torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), num_heads)
        )

        # 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(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.permute(1, 2, 0).contiguous()
        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=config.qkv_bias)
        self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
        self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)

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

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = 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(-1, -2))

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

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

        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
        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 MaskFormerSwinModel forward() function)
            mask_shape = attention_mask.shape[0]
            attention_scores = attention_scores.view(
                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.view(-1, self.num_attention_heads, dim, dim)

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

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

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

        context_layer = torch.matmul(attention_probs, value_layer)
        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.swin.modeling_swin.SwinSelfOutput with Swin->MaskFormerSwin
class MaskFormerSwinSelfOutput(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->MaskFormerSwin
class MaskFormerSwinAttention(nn.Module):
    def __init__(self, config, dim, num_heads, window_size):
        super().__init__()
        self.self = MaskFormerSwinSelfAttention(config, dim, num_heads, window_size)
        self.output = MaskFormerSwinSelfOutput(config, dim)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
        )

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

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = 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 with Swin->MaskFormerSwin
class MaskFormerSwinIntermediate(nn.Module):
    def __init__(self, config, dim):
        super().__init__()
        self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
        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.swin.modeling_swin.SwinOutput with Swin->MaskFormerSwin
class MaskFormerSwinOutput(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


class MaskFormerSwinLayer(nn.Module):
    def __init__(self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0):
        super().__init__()
        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 = MaskFormerSwinAttention(config, dim, num_heads, self.window_size)
        self.drop_path = MaskFormerSwinDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
        self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
        self.intermediate = MaskFormerSwinIntermediate(config, dim)
        self.output = MaskFormerSwinOutput(config, dim)

    def get_attn_mask(self, input_resolution):
        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            height, width = input_resolution
            img_mask = torch.zeros((1, height, width, 1))
            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.view(-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_left = pad_top = 0
        pad_rigth = (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, pad_left, pad_rigth, pad_top, pad_bottom)
        hidden_states = nn.functional.pad(hidden_states, pad_values)
        return hidden_states, pad_values

    def forward(self, hidden_states, input_dimensions, head_mask=None, output_attentions=False):
        height, width = input_dimensions
        batch_size, dim, channels = hidden_states.size()
        shortcut = hidden_states

        hidden_states = self.layernorm_before(hidden_states)
        hidden_states = hidden_states.view(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:
            shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), 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.view(-1, self.window_size * self.window_size, channels)
        attn_mask = self.get_attn_mask((height_pad, width_pad))
        if attn_mask is not None:
            attn_mask = attn_mask.to(hidden_states_windows.device)

        self_attention_outputs = self.attention(
            hidden_states_windows, attn_mask, 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

        attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels)
        shifted_windows = window_reverse(
            attention_windows, self.window_size, height_pad, width_pad
        )  # B height' width' C

        # reverse cyclic shift
        if self.shift_size > 0:
            attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), 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.view(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)

        outputs = (layer_output,) + outputs

        return outputs


class MaskFormerSwinStage(nn.Module):
    # Copied from transformers.models.swin.modeling_swin.SwinStage.__init__ with Swin->MaskFormerSwin
    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(
            [
                MaskFormerSwinLayer(
                    config=config,
                    dim=dim,
                    input_resolution=input_resolution,
                    num_heads=num_heads,
                    drop_path_rate=drop_path[i],
                    shift_size=0 if (i % 2 == 0) else config.window_size // 2,
                )
                for i in range(depth)
            ]
        )

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

        self.pointing = False

    def forward(
        self, hidden_states, input_dimensions, head_mask=None, output_attentions=False, output_hidden_states=False
    ):
        all_hidden_states = () if output_hidden_states else None

        height, width = input_dimensions
        for i, block_module in enumerate(self.blocks):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

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

            block_hidden_states = block_module(hidden_states, input_dimensions, layer_head_mask, output_attentions)

            hidden_states = block_hidden_states[0]

            if output_hidden_states:
                all_hidden_states += (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, input_dimensions)
        else:
            output_dimensions = (height, width, height, width)

        return hidden_states, output_dimensions, all_hidden_states


class MaskFormerSwinEncoder(nn.Module):
    # Copied from transformers.models.swin.modeling_swin.SwinEncoder.__init__ with Swin->MaskFormerSwin
    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(
            [
                MaskFormerSwinStage(
                    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=MaskFormerSwinPatchMerging 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,
        input_dimensions,
        head_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_input_dimensions = ()
        all_self_attentions = () if output_attentions else None

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        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_hidden_states, output_dimensions, layer_all_hidden_states = self._gradient_checkpointing_func(
                    layer_module.__call__,
                    hidden_states,
                    layer_head_mask,
                    output_attentions,
                )
            else:
                layer_hidden_states, output_dimensions, layer_all_hidden_states = layer_module(
                    hidden_states,
                    input_dimensions,
                    layer_head_mask,
                    output_attentions,
                    output_hidden_states,
                )

            input_dimensions = (output_dimensions[-2], output_dimensions[-1])
            all_input_dimensions += (input_dimensions,)
            if output_hidden_states:
                all_hidden_states += (layer_all_hidden_states,)

            hidden_states = layer_hidden_states

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

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

        return MaskFormerSwinBaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            hidden_states_spatial_dimensions=all_input_dimensions,
            attentions=all_self_attentions,
        )


# Copied from transformers.models.swin.modeling_swin.SwinPreTrainedModel with Swin->MaskFormerSwin, swin->model
class MaskFormerSwinPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = MaskFormerSwinConfig
    base_model_prefix = "model"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MaskFormerSwinStage"]

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


class MaskFormerSwinModel(MaskFormerSwinPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.num_layers = len(config.depths)
        self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))

        self.embeddings = MaskFormerSwinEmbeddings(config)
        self.encoder = MaskFormerSwinEncoder(config, self.embeddings.patch_grid)

        self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
        self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None

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

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def forward(
        self,
        pixel_values=None,
        head_mask=None,
        output_attentions=None,
        output_hidden_states=None,
        interpolate_pos_encoding=False,
        return_dict=None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

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

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

        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.last_hidden_state if return_dict else encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)

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

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

        hidden_states_spatial_dimensions = (input_dimensions,) + encoder_outputs.hidden_states_spatial_dimensions

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


class MaskFormerSwinBackbone(MaskFormerSwinPreTrainedModel, BackboneMixin):
    """
    MaskFormerSwin backbone, designed especially for the MaskFormer framework.

    This classes reshapes `hidden_states` from (`batch_size, sequence_length, hidden_size)` to (`batch_size,
    num_channels, height, width)`). It also adds additional layernorms after each stage.

    Args:
        config (`MaskFormerSwinConfig`):
            The configuration used by [`MaskFormerSwinModel`].
    """

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

        self.model = MaskFormerSwinModel(config)
        if "stem" in self.out_features:
            raise ValueError("This backbone does not support 'stem' in the `out_features`.")
        self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
        self.hidden_states_norms = nn.ModuleList(
            [nn.LayerNorm(num_channels) for num_channels in self.num_features[1:]]
        )

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

    def forward(
        self,
        pixel_values: Tensor,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> BackboneOutput:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        outputs = self.model(
            pixel_values, output_hidden_states=True, output_attentions=output_attentions, return_dict=True
        )

        # we skip the stem
        hidden_states = outputs.hidden_states[1:]

        # we need to reshape the hidden states to their original spatial dimensions
        # spatial dimensions contains all the heights and widths of each stage, including after the embeddings
        spatial_dimensions: Tuple[Tuple[int, int]] = outputs.hidden_states_spatial_dimensions
        feature_maps = ()
        for i, (hidden_state, stage, (height, width)) in enumerate(
            zip(hidden_states, self.stage_names[1:], spatial_dimensions)
        ):
            norm = self.hidden_states_norms[i]
            # the last element corespond to the layer's last block output but before patch merging
            hidden_state_unpolled = hidden_state[-1]
            hidden_state_norm = norm(hidden_state_unpolled)
            # the pixel decoder (FPN) expects 3D tensors (features)
            batch_size, _, hidden_size = hidden_state_norm.shape
            # reshape "b (h w) d -> b d h w"
            hidden_state_permuted = (
                hidden_state_norm.permute(0, 2, 1).view((batch_size, hidden_size, height, width)).contiguous()
            )
            if stage in self.out_features:
                feature_maps += (hidden_state_permuted,)

        if not return_dict:
            output = (feature_maps,)
            if output_hidden_states:
                output += (outputs.hidden_states,)
            if output_attentions:
                output += (outputs.attentions,)
            return output

        return BackboneOutput(
            feature_maps=feature_maps,
            hidden_states=outputs.hidden_states if output_hidden_states else None,
            attentions=outputs.attentions,
        )


__all__ = ["MaskFormerSwinBackbone", "MaskFormerSwinModel", "MaskFormerSwinPreTrainedModel"]
