FM / maskformer

/usr/local/lib/python3.6/site-packages/transformers/models/maskformer/

/usr/local/lib/python3.6/site-packages/transformers/models/maskformer

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_maskformer.py	8731	0644	edit dl rm
convert_maskformer_original_pytorch_checkpoint_to_pytorch.py	32125	0644	edit dl rm
feature_extraction_maskformer.py	32336	0644	edit dl rm
modeling_maskformer.py	118878	0644	edit dl rm
__init__.py	1963	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/maskformer/modeling_maskformer.py (118878B)

# coding=utf-8
# Copyright 2022 Meta Platforms, Inc.s 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 MaskFormer model."""

import collections.abc
import math
import random
from dataclasses import dataclass
from numbers import Number
from typing import Dict, List, Optional, Tuple

import numpy as np
import torch
from torch import Tensor, nn

from transformers.utils import logging

from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWithCrossAttentions
from ...modeling_utils import ModuleUtilsMixin, PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_scipy_available,
    replace_return_docstrings,
    requires_backends,
)
from ..detr import DetrConfig
from ..swin import SwinConfig
from .configuration_maskformer import MaskFormerConfig

if is_scipy_available():
    from scipy.optimize import linear_sum_assignment

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "MaskFormerConfig"
_CHECKPOINT_FOR_DOC = "facebook/maskformer-swin-base-ade"
_FEAT_EXTRACTOR_FOR_DOC = "MaskFormerFeatureExtractor"

MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/maskformer-swin-base-ade",
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
]

@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.

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

@dataclass
# Copied from transformers.models.detr.modeling_detr.DetrDecoderOutput
class DetrDecoderOutput(BaseModelOutputWithCrossAttentions):
    """
    Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions,
    namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
    gone through a layernorm. This is useful when training the model with auxiliary decoding losses.

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.
        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.
        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=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 of the decoder's cross-attention layer, after the attention softmax,
            used to compute the weighted average in the cross-attention heads.
        intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`):
            Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
            layernorm.
    """

intermediate_hidden_states: Optional[torch.FloatTensor] = None

@dataclass
class MaskFormerPixelLevelModuleOutput(ModelOutput):
    """
    MaskFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the
    `encoder` and `decoder`. By default, the `encoder` is a MaskFormerSwin Transformer and the `decoder` is a Feature
    Pyramid Network (FPN).

The `encoder_last_hidden_state` are referred on the paper as **images features**, while `decoder_last_hidden_state`
    as **pixel embeddings**

Args:
        encoder_last_hidden_state (`torch.FloatTensor` of shape`(batch_size, num_channels, height, width)`):
            Last hidden states (final feature map) of the last stage of the encoder.
        encoder_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 stage) of
            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at
            the output of each stage.
        decoder_last_hidden_state (`torch.FloatTensor` of shape`(batch_size, num_channels, height, width)`):
            Last hidden states (final feature map) of the last stage of the decoder.
        decoder_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 stage) of
            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at
            the output of each stage.
    """

encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    decoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None

class MaskFormerPixelDecoderOutput(ModelOutput):
    """
    MaskFormer's pixel decoder module output, practically a Feature Pyramid Network. It returns the last hidden state
    and (optionally) the hidden states.

Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Last hidden states (final feature map) of the last stage 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, num_channels, height, width)`. 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 from Detr's decoder 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
    attentions: Optional[Tuple[torch.FloatTensor]] = None

@dataclass
class MaskFormerModelOutput(ModelOutput):
    """
    Class for outputs of [`MaskFormerModel`]. This class returns all the needed hidden states to compute the logits.

Args:
        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Last hidden states (final feature map) of the last stage of the encoder model (backbone).
        pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN).
        transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Last hidden states (final feature map) of the last stage of the transformer decoder model.
        encoder_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 stage) of
            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
            model at the output of each stage.
        pixel_decoder_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 stage) of
            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
            decoder model at the output of each stage.
        transformer_decoder_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 stage) of
            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
            transformer decoder at the output of each stage.
        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` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and
            `decoder_hidden_states`
        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 from Detr's decoder after the attention softmax, used to compute the
            weighted average in the self-attention heads.
    """

encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
    transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    pixel_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    transformer_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None

@dataclass
class MaskFormerForInstanceSegmentationOutput(ModelOutput):
    """
    Class for outputs of [`MaskFormerForInstanceSegmentation`].

This output can be directly passed to [`~MaskFormerFeatureExtractor.post_process_segmentation`] or
    [`~MaskFormerFeatureExtractor.post_process_panoptic_segmentation`] depending on the task. Please, see
    [`~MaskFormerFeatureExtractor] for details regarding usage.

Args:
        loss (`torch.Tensor`, *optional*):
            The computed loss, returned when labels are present.
        class_queries_logits (`torch.FloatTensor`):
            A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each
            query.
        masks_queries_logits (`torch.FloatTensor`):
            A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each
            query. Note the `+ 1` is needed because we incorporate the null class.
        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Last hidden states (final feature map) of the last stage of the encoder model (backbone).
        pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN).
        transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Last hidden states (final feature map) of the last stage of the transformer decoder model.
        encoder_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 stage) of
            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
            model at the output of each stage.
        pixel_decoder_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 stage) of
            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
            decoder model at the output of each stage.
        transformer_decoder_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 stage) of
            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the transformer decoder at the output
            of each stage.
        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` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and
            `decoder_hidden_states`.
        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 from Detr's decoder after the attention softmax, used to compute the
            weighted average in the self-attention heads.
    """

loss: Optional[torch.FloatTensor] = None
    class_queries_logits: torch.FloatTensor = None
    masks_queries_logits: torch.FloatTensor = None
    auxiliary_logits: torch.FloatTensor = None
    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
    transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    pixel_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    transformer_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None

def upsample_like(pixel_values: Tensor, like: Tensor, mode: str = "bilinear") -> Tensor:
    """
    An utility function that upsamples `pixel_values` to match the dimension of `like`.

Args:
        pixel_values (`torch.Tensor`):
            The tensor we wish to upsample.
        like (`torch.Tensor`):
            The tensor we wish to use as size target.
        mode (str, *optional*, defaults to `"bilinear"`):
            The interpolation mode.

Returns:
        `torch.Tensor`: The upsampled tensor
    """
    _, _, height, width = like.shape
    upsampled = nn.functional.interpolate(pixel_values, size=(height, width), mode=mode, align_corners=False)
    return upsampled

# refactored from original implementation
def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor:
    r"""
    Compute the DICE loss, similar to generalized IOU for masks as follows:

$$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$

In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow

$$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$

Args:
        inputs (`torch.Tensor`):
            A tensor representing a mask.
        labels (`torch.Tensor`):
            A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
            (0 for the negative class and 1 for the positive class).
        num_masks (`int`):
            The number of masks present in the current batch, used for normalization.

Returns:
        `torch.Tensor`: The computed loss.
    """
    probs = inputs.sigmoid().flatten(1)
    numerator = 2 * (probs * labels).sum(-1)
    denominator = probs.sum(-1) + labels.sum(-1)
    loss = 1 - (numerator + 1) / (denominator + 1)
    loss = loss.sum() / num_masks
    return loss

# refactored from original implementation
def sigmoid_focal_loss(
    inputs: Tensor, labels: Tensor, num_masks: int, alpha: float = 0.25, gamma: float = 2
) -> Tensor:
    r"""
    Focal loss proposed in [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) originally used in
    RetinaNet. The loss is computed as follows:

$$ \mathcal{L}_{\text{focal loss} = -(1 - p_t)^{\gamma}\log{(p_t)} $$

where \$CE(p_t) = -\log{(p_t)}}\$, CE is the standard Cross Entropy Loss

Please refer to equation (1,2,3) of the paper for a better understanding.

Args:
        inputs (`torch.Tensor`):
            A float tensor of arbitrary shape.
        labels (`torch.Tensor`):
            A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
            (0 for the negative class and 1 for the positive class).
        num_masks (`int`):
            The number of masks present in the current batch, used for normalization.
        alpha (float, *optional*, defaults to 0.25):
            Weighting factor in range (0,1) to balance positive vs negative examples.
        gamma (float, *optional*, defaults to 2.0):
            Exponent of the modulating factor \$1 - p_t\$ to balance easy vs hard examples.

Returns:
        `torch.Tensor`: The computed loss.
    """
    criterion = nn.BCEWithLogitsLoss(reduction="none")
    probs = inputs.sigmoid()
    cross_entropy_loss = criterion(inputs, labels)
    p_t = probs * labels + (1 - probs) * (1 - labels)
    loss = cross_entropy_loss * ((1 - p_t) ** gamma)

if alpha >= 0:
        alpha_t = alpha * labels + (1 - alpha) * (1 - labels)
        loss = alpha_t * loss

loss = loss.mean(1).sum() / num_masks
    return loss

# refactored from original implementation
def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor:
    """
    A pair wise version of the dice loss, see `dice_loss` for usage.

Args:
        inputs (`torch.Tensor`):
            A tensor representing a mask
        labels (`torch.Tensor`):
            A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
            (0 for the negative class and 1 for the positive class).

Returns:
        `torch.Tensor`: The computed loss between each pairs.
    """
    inputs = inputs.sigmoid().flatten(1)
    numerator = 2 * torch.einsum("nc,mc->nm", inputs, labels)
    # using broadcasting to get a [num_queries, NUM_CLASSES] matrix
    denominator = inputs.sum(-1)[:, None] + labels.sum(-1)[None, :]
    loss = 1 - (numerator + 1) / (denominator + 1)
    return loss

# refactored from original implementation
def pair_wise_sigmoid_focal_loss(inputs: Tensor, labels: Tensor, alpha: float = 0.25, gamma: float = 2.0) -> Tensor:
    r"""
    A pair wise version of the focal loss, see `sigmoid_focal_loss` for usage.

Args:
        inputs (`torch.Tensor`):
            A tensor representing a mask.
        labels (`torch.Tensor`):
            A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
            (0 for the negative class and 1 for the positive class).
        alpha (float, *optional*, defaults to 0.25):
            Weighting factor in range (0,1) to balance positive vs negative examples.
        gamma (float, *optional*, defaults to 2.0):
            Exponent of the modulating factor \$1 - p_t\$ to balance easy vs hard examples.

Returns:
        `torch.Tensor`: The computed loss between each pairs.
    """
    if alpha < 0:
        raise ValueError("alpha must be positive")

height_and_width = inputs.shape[1]

criterion = nn.BCEWithLogitsLoss(reduction="none")
    prob = inputs.sigmoid()
    cross_entropy_loss_pos = criterion(inputs, torch.ones_like(inputs))
    focal_pos = ((1 - prob) ** gamma) * cross_entropy_loss_pos
    focal_pos *= alpha

cross_entropy_loss_neg = criterion(inputs, torch.zeros_like(inputs))

focal_neg = (prob**gamma) * cross_entropy_loss_neg
    focal_neg *= 1 - alpha

loss = torch.einsum("nc,mc->nm", focal_pos, labels) + torch.einsum("nc,mc->nm", focal_neg, (1 - labels))

return loss / height_and_width

# Copied from transformers.models.vit.modeling_vit.to_2tuple
def to_2tuple(x):
    if isinstance(x, collections.abc.Iterable):
        return x
    return (x, x)

# 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.
    """
    batch_size = int(windows.shape[0] / (height * width / window_size / window_size))
    windows = windows.view(batch_size, height // window_size, width // window_size, window_size, window_size, -1)
    windows = windows.permute(0, 1, 3, 2, 4, 5).contiguous().view(batch_size, height, width, -1)
    return windows

# Copied from transformers.models.swin.modeling_swin.drop_path
def drop_path(input, drop_prob=0.0, training=False, scale_by_keep=True):
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    """
    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 = input.new_empty(shape).bernoulli_(keep_prob)
    if keep_prob > 0.0 and scale_by_keep:
        random_tensor.div_(keep_prob)
    return input * random_tensor

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

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

self.patch_embeddings = MaskFormerSwinPatchEmbeddings(
            image_size=config.image_size,
            patch_size=config.patch_size,
            num_channels=config.num_channels,
            embed_dim=config.embed_dim,
        )
        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)

def forward(self, pixel_values):
        embeddings, output_dimensions = self.patch_embeddings(pixel_values)
        embeddings = self.norm(embeddings)

if self.position_embeddings is not None:
            embeddings = embeddings + self.position_embeddings

embeddings = self.dropout(embeddings)

return embeddings, output_dimensions

class MaskFormerSwinPatchEmbeddings(nn.Module):
    """
    Image to Patch Embedding, including padding.
    """

def __init__(self, image_size=224, patch_size=16, num_channels=3, embed_dim=768):
        super().__init__()
        image_size = to_2tuple(image_size)
        patch_size = to_2tuple(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_patches = num_patches
        self.grid_size = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])

self.projection = nn.Conv2d(num_channels, embed_dim, 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):
        _, _, 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_flat = embeddings.flatten(2).transpose(1, 2)

return embeddings_flat, output_dimensions

class MaskFormerSwinPatchMerging(nn.Module):
    """
    Patch Merging Layer for maskformer model.

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, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        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, width, height):
        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, input_dimensions):
        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=None, scale_by_keep=True):
        super(MaskFormerSwinDropPath, self).__init__()
        self.drop_prob = drop_prob
        self.scale_by_keep = scale_by_keep

def forward(self, input):
        return drop_path(input, self.drop_prob, self.training, self.scale_by_keep)

# Copied from transformers.models.swin.modeling_swin.SwinSelfAttention with Swin->MaskFormerSwin
class MaskFormerSwinSelfAttention(nn.Module):
    def __init__(self, config, dim, num_heads):
        super().__init__()
        if dim % num_heads != 0:
            raise ValueError(
                f"The hidden size ({dim}) is not a multiple of the number of attention " f"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 = to_2tuple(config.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(torch.meshgrid([coords_h, coords_w]))
        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,
        attention_mask=None,
        head_mask=None,
        output_attentions=False,
    ):
        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, input_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):
        super().__init__()
        self.self = MaskFormerSwinSelfAttention(config, dim, num_heads)
        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, attention_mask=None, head_mask=None, output_attentions=False):
        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):
        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):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states

class MaskFormerSwinBlock(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 = MaskFormerSwinAttention(config, dim, num_heads)
        self.drop_path = (
            MaskFormerSwinDropPath(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 = 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 MaskFormerSwinLayer(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(
            [
                MaskFormerSwinBlock(
                    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)
            ]
        )

# 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):
    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(
            [
                MaskFormerSwinLayer(
                    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:

def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, output_attentions)

return custom_forward

layer_hidden_states, output_dimensions, layer_all_hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module), hidden_states, layer_head_mask
                )
            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,
        )

class MaskFormerSwinModel(nn.Module, ModuleUtilsMixin):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__()
        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,
        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)

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
        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,
        )

# Copied from transformers.models.detr.modeling_detr.DetrAttention
class DetrAttention(nn.Module):
    """
    Multi-headed attention from 'Attention Is All You Need' paper.

Here, we add position embeddings to the queries and keys (as explained in the DETR paper).
    """

def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
        bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert (
            self.head_dim * num_heads == self.embed_dim
        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads})."
        self.scaling = self.head_dim**-0.5

self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
        return tensor if position_embeddings is None else tensor + position_embeddings

def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_embeddings: Optional[torch.Tensor] = None,
        key_value_states: Optional[torch.Tensor] = None,
        key_value_position_embeddings: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

# if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, embed_dim = hidden_states.size()

# add position embeddings to the hidden states before projecting to queries and keys
        if position_embeddings is not None:
            hidden_states_original = hidden_states
            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)

# add key-value position embeddings to the key value states
        if key_value_position_embeddings is not None:
            key_value_states_original = key_value_states
            key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings)

# get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        # get key, value proj
        if is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states_original), -1, bsz)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states_original), -1, bsz)

proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)

src_len = key_states.size(1)

attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
            )

if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

attn_weights = nn.functional.softmax(attn_weights, dim=-1)

if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
        else:
            attn_weights_reshaped = None

attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

attn_output = torch.bmm(attn_probs, value_states)

if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
            )

attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)

attn_output = self.out_proj(attn_output)

return attn_output, attn_weights_reshaped

# Copied from transformers.models.detr.modeling_detr.DetrDecoderLayer
class DetrDecoderLayer(nn.Module):
    def __init__(self, config: DetrConfig):
        super().__init__()
        self.embed_dim = config.d_model

self.self_attn = DetrAttention(
            embed_dim=self.embed_dim,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.encoder_attn = DetrAttention(
            self.embed_dim,
            config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_embeddings: Optional[torch.Tensor] = None,
        query_position_embeddings: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            position_embeddings (`torch.FloatTensor`, *optional*):
                position embeddings that are added to the queries and keys
            in the cross-attention layer.
            query_position_embeddings (`torch.FloatTensor`, *optional*):
                position embeddings that are added to the queries and keys
            in the self-attention layer.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

# Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=query_position_embeddings,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
        )

hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

# Cross-Attention Block
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states

hidden_states, cross_attn_weights = self.encoder_attn(
                hidden_states=hidden_states,
                position_embeddings=query_position_embeddings,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                key_value_position_embeddings=position_embeddings,
                output_attentions=output_attentions,
            )

hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
            hidden_states = residual + hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)

# Fully Connected
        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

outputs = (hidden_states,)

if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

return outputs

# Copied from transformers.models.detr.modeling_detr._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

inverted_mask = 1.0 - expanded_mask

return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)

class DetrDecoder(nn.Module):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`].

The decoder updates the query embeddings through multiple self-attention and cross-attention layers.

Some small tweaks for DETR:

- position_embeddings and query_position_embeddings are added to the forward pass.
    - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers.

Args:
        config: DetrConfig
    """

def __init__(self, config: DetrConfig):
        super().__init__()
        self.config = config
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop

self.layers = nn.ModuleList([DetrDecoderLayer(config) for _ in range(config.decoder_layers)])
        # in DETR, the decoder uses layernorm after the last decoder layer output
        self.layernorm = nn.LayerNorm(config.d_model)

self.gradient_checkpointing = False

def forward(
        self,
        inputs_embeds=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        position_embeddings=None,
        query_position_embeddings=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                The query embeddings that are passed into the decoder.

attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`:

- 1 for queries that are **not masked**,
                - 0 for queries that are **masked**.

[What are attention masks?](../glossary#attention-mask)
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                of the decoder.
            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
                Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected
                in `[0, 1]`:

- 1 for pixels that are real (i.e. **not masked**),
                - 0 for pixels that are padding (i.e. **masked**).

position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Position embeddings that are added to the queries and keys in each cross-attention layer.
            query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
                , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        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 inputs_embeds is not None:
            hidden_states = inputs_embeds
            input_shape = inputs_embeds.size()[:-1]

combined_attention_mask = None

if attention_mask is not None and combined_attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            combined_attention_mask = combined_attention_mask + _expand_mask(
                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
            )

# expand encoder attention mask
        if encoder_hidden_states is not None and encoder_attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])

# optional intermediate hidden states
        intermediate = () if self.config.auxiliary_loss else None

# decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):
                continue

if self.gradient_checkpointing and self.training:

def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, output_attentions)

return custom_forward

layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    combined_attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=combined_attention_mask,
                    position_embeddings=position_embeddings,
                    query_position_embeddings=query_position_embeddings,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    output_attentions=output_attentions,
                )

hidden_states = layer_outputs[0]

if self.config.auxiliary_loss:
                hidden_states = self.layernorm(hidden_states)
                intermediate += (hidden_states,)

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

if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

# finally, apply layernorm
        hidden_states = self.layernorm(hidden_states)

# add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

# stack intermediate decoder activations
        if self.config.auxiliary_loss:
            intermediate = torch.stack(intermediate)

if not return_dict:
            return tuple(
                v
                for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate]
                if v is not None
            )
        return DetrDecoderOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
            intermediate_hidden_states=intermediate,
        )

# refactored from original implementation
class MaskFormerHungarianMatcher(nn.Module):
    """This class computes an assignment between the labels and the predictions of the network.

For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
    predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are
    un-matched (and thus treated as non-objects).
    """

def __init__(self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0):
        """Creates the matcher

Params:
            cost_class (float, *optional*, defaults to 1.0):
                This is the relative weight of the classification error in the matching cost.
            cost_mask (float, *optional*,  defaults to 1.0):
                This is the relative weight of the focal loss of the binary mask in the matching cost.
            cost_dice (float, *optional*, defaults to 1.0):
                This is the relative weight of the dice loss of the binary mask in the matching cost
        """
        super().__init__()
        if cost_class == 0 and cost_mask == 0 and cost_dice == 0:
            raise ValueError("All costs cant be 0")
        self.cost_class = cost_class
        self.cost_mask = cost_mask
        self.cost_dice = cost_dice

@torch.no_grad()
    def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> List[Tuple[Tensor]]:
        """Performs the matching

Params:
            masks_queries_logits (`torch.Tensor`):
                A tensor` of dim `batch_size, num_queries, num_labels` with the
                  classification logits.
            class_queries_logits (`torch.Tensor`):
                A tensor` of dim `batch_size, num_queries, height, width` with the
                  predicted masks.

class_labels (`torch.Tensor`):
                A tensor` of dim `num_target_boxes` (where num_target_boxes is the number
                  of ground-truth objects in the target) containing the class labels.
            mask_labels (`torch.Tensor`):
                A tensor` of dim `num_target_boxes, height, width` containing the target
                  masks.

Returns:
            `List[Tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected labels (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes).
        """
        indices: List[Tuple[np.array]] = []

preds_masks = masks_queries_logits
        preds_probs = class_queries_logits
        # iterate through batch size
        for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels):
            # downsample the target mask, save memory
            target_mask = nn.functional.interpolate(target_mask[:, None], size=pred_mask.shape[-2:], mode="nearest")
            pred_probs = pred_probs.softmax(-1)
            # Compute the classification cost. Contrary to the loss, we don't use the NLL,
            # but approximate it in 1 - proba[target class].
            # The 1 is a constant that doesn't change the matching, it can be ommitted.
            cost_class = -pred_probs[:, labels]
            # flatten spatial dimension "q h w -> q (h w)"
            pred_mask_flat = pred_mask.flatten(1)  # [num_queries, height*width]
            # same for target_mask "c h w -> c (h w)"
            target_mask_flat = target_mask[:, 0].flatten(1)  # [num_total_labels, height*width]
            # compute the focal loss between each mask pairs -> shape (num_queries, num_labels)
            cost_mask = pair_wise_sigmoid_focal_loss(pred_mask_flat, target_mask_flat)
            # Compute the dice loss betwen each mask pairs -> shape (num_queries, num_labels)
            cost_dice = pair_wise_dice_loss(pred_mask_flat, target_mask_flat)
            # final cost matrix
            cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
            # do the assigmented using the hungarian algorithm in scipy
            assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.cpu())
            indices.append(assigned_indices)

# It could be stacked in one tensor
        matched_indices = [
            (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
        ]
        return matched_indices

def __repr__(self):
        head = "Matcher " + self.__class__.__name__
        body = [
            f"cost_class: {self.cost_class}",
            f"cost_mask: {self.cost_mask}",
            f"cost_dice: {self.cost_dice}",
        ]
        _repr_indent = 4
        lines = [head] + [" " * _repr_indent + line for line in body]
        return "\n".join(lines)

# copied and adapted from original implementation
class MaskFormerLoss(nn.Module):
    def __init__(
        self,
        num_labels: int,
        matcher: MaskFormerHungarianMatcher,
        weight_dict: Dict[str, float],
        eos_coef: float,
    ):
        """
        The MaskFormer Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we compute
        hungarian assignment between ground truth masks and the outputs of the model 2) we supervise each pair of
        matched ground-truth / prediction (supervise class and mask)

Args:
            num_labels (`int`):
                The number of classes.
            matcher (`MaskFormerHungarianMatcher`):
                A torch module that computes the assigments between the predictions and labels.
            weight_dict (`Dict[str, float]`):
                A dictionary of weights to be applied to the different losses.
            eos_coef (`float`):
                Weight to apply to the null class.
        """

super().__init__()
        requires_backends(self, ["scipy"])
        self.num_labels = num_labels
        self.matcher = matcher
        self.weight_dict = weight_dict
        self.eos_coef = eos_coef
        empty_weight = torch.ones(self.num_labels + 1)
        empty_weight[-1] = self.eos_coef
        self.register_buffer("empty_weight", empty_weight)

def _max_by_axis(self, the_list: List[List[int]]) -> List[int]:
        maxes = the_list[0]
        for sublist in the_list[1:]:
            for index, item in enumerate(sublist):
                maxes[index] = max(maxes[index], item)
        return maxes

def _pad_images_to_max_in_batch(self, tensors: List[Tensor]) -> Tuple[Tensor, Tensor]:
        # get the maximum size in the batch
        max_size = self._max_by_axis([list(tensor.shape) for tensor in tensors])
        batch_size = len(tensors)
        # compute finel size
        batch_shape = [batch_size] + max_size
        b, _, h, w = batch_shape
        # get metadata
        dtype = tensors[0].dtype
        device = tensors[0].device
        padded_tensors = torch.zeros(batch_shape, dtype=dtype, device=device)
        padding_masks = torch.ones((b, h, w), dtype=torch.bool, device=device)
        # pad the tensors to the size of the biggest one
        for tensor, padded_tensor, padding_mask in zip(tensors, padded_tensors, padding_masks):
            padded_tensor[: tensor.shape[0], : tensor.shape[1], : tensor.shape[2]].copy_(tensor)
            padding_mask[: tensor.shape[1], : tensor.shape[2]] = False

return padded_tensors, padding_masks

def loss_labels(
        self, class_queries_logits: Tensor, class_labels: List[Tensor], indices: Tuple[np.array]
    ) -> Dict[str, Tensor]:
        """Compute the losses related to the labels using cross entropy.

Args:
            class_queries_logits (`torch.Tensor`):
                A tensor of shape `batch_size, num_queries, num_labels`
            class_labels (`List[torch.Tensor]`):
                List of class labels of shape `(labels)`.
            indices (`Tuple[np.array])`:
                The indices computed by the Hungarian matcher.

Returns:
            `Dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key:
            - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
        """

pred_logits = class_queries_logits
        batch_size, num_queries, _ = pred_logits.shape
        criterion = nn.CrossEntropyLoss(weight=self.empty_weight)
        idx = self._get_predictions_permutation_indices(indices)
        # shape = (batch_size, num_queries)
        target_classes_o = torch.cat([target[j] for target, (_, j) in zip(class_labels, indices)])
        # shape = (batch_size, num_queries)
        target_classes = torch.full(
            (batch_size, num_queries), fill_value=self.num_labels, dtype=torch.int64, device=pred_logits.device
        )
        target_classes[idx] = target_classes_o
        # target_classes is a (batch_size, num_labels, num_queries), we need to permute pred_logits "b q c -> b c q"
        pred_logits_transposed = pred_logits.transpose(1, 2)
        loss_ce = criterion(pred_logits_transposed, target_classes)
        losses = {"loss_cross_entropy": loss_ce}
        return losses

def loss_masks(
        self, masks_queries_logits: Tensor, mask_labels: List[Tensor], indices: Tuple[np.array], num_masks: int
    ) -> Dict[str, Tensor]:
        """Compute the losses related to the masks using focal and dice loss.

Args:
            masks_queries_logits (`torch.Tensor`):
                A tensor of shape `batch_size, num_queries, height, width`
            mask_labels (`torch.Tensor`):
                List of mask labels of shape `(labels, height, width)`.
            indices (`Tuple[np.array])`:
                The indices computed by the Hungarian matcher.
            num_masks (`int)`:
                The number of masks, used for normalization.

Returns:
            `Dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys:
            - **loss_mask** -- The loss computed using sigmoid focal loss on the predicted and ground truth masks.
            - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth
              masks.
        """
        src_idx = self._get_predictions_permutation_indices(indices)
        tgt_idx = self._get_targets_permutation_indices(indices)
        # shape (batch_size * num_queries, height, width)
        pred_masks = masks_queries_logits[src_idx]
        # shape (batch_size, num_queries, height, width)
        # pad all and stack the targets to the num_labels dimension
        target_masks, _ = self._pad_images_to_max_in_batch(mask_labels)
        target_masks = target_masks[tgt_idx]
        # upsample predictions to the target size, we have to add one dim to use interpolate
        pred_masks = nn.functional.interpolate(
            pred_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
        )
        pred_masks = pred_masks[:, 0].flatten(1)

target_masks = target_masks.flatten(1)
        losses = {
            "loss_mask": sigmoid_focal_loss(pred_masks, target_masks, num_masks),
            "loss_dice": dice_loss(pred_masks, target_masks, num_masks),
        }
        return losses

def _get_predictions_permutation_indices(self, indices):
        # permute predictions following indices
        batch_indices = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
        predictions_indices = torch.cat([src for (src, _) in indices])
        return batch_indices, predictions_indices

def _get_targets_permutation_indices(self, indices):
        # permute labels following indices
        batch_indices = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
        target_indices = torch.cat([tgt for (_, tgt) in indices])
        return batch_indices, target_indices

def forward(
        self,
        masks_queries_logits: Tensor,
        class_queries_logits: Tensor,
        mask_labels: List[Tensor],
        class_labels: List[Tensor],
        auxiliary_predictions: Optional[Dict[str, Tensor]] = None,
    ) -> Dict[str, Tensor]:
        """
        This performs the loss computation.

Args:
            masks_queries_logits (`torch.Tensor`):
                A tensor of shape `batch_size, num_queries, height, width`
            class_queries_logits (`torch.Tensor`):
                A tensor of shape `batch_size, num_queries, num_labels`
            mask_labels (`torch.Tensor`):
                List of mask labels of shape `(labels, height, width)`.
            class_labels (`List[torch.Tensor]`):
                List of class labels of shape `(labels)`.
            auxiliary_predictions (`Dict[str, torch.Tensor]`, *optional*):
                if `use_auxiliary_loss` was set to `true` in [`MaskFormerConfig`], then it contains the logits from the
                inner layers of the Detr's Decoder.

Returns:
            `Dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys:
            - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
            - **loss_mask** -- The loss computed using sigmoid focal loss on the predicted and ground truth masks.
            - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth
              masks.
            if `use_auxiliary_loss` was set to `true` in [`MaskFormerConfig`], the dictionary contains addional losses
            for each auxiliary predictions.
        """

# retrieve the matching between the outputs of the last layer and the labels
        indices = self.matcher(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
        # compute the average number of target masks for normalization purposes
        num_masks: Number = self.get_num_masks(class_labels, device=class_labels[0].device)
        # get all the losses
        losses: Dict[str, Tensor] = {
            **self.loss_masks(masks_queries_logits, mask_labels, indices, num_masks),
            **self.loss_labels(class_queries_logits, class_labels, indices),
        }
        # in case of auxiliary losses, we repeat this process with the output of each intermediate layer.
        if auxiliary_predictions is not None:
            for idx, aux_outputs in enumerate(auxiliary_predictions):
                masks_queries_logits = aux_outputs["masks_queries_logits"]
                class_queries_logits = aux_outputs["class_queries_logits"]
                loss_dict = self.forward(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
                loss_dict = {f"{key}_{idx}": value for key, value in loss_dict.items()}
                losses.update(loss_dict)

return losses

def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor:
        """
        Computes the average number of target masks accross the batch, for normalization purposes.
        """
        num_masks = sum([len(classes) for classes in class_labels])
        num_masks_pt = torch.as_tensor([num_masks], dtype=torch.float, device=device)
        return num_masks_pt

class MaskFormerSwinTransformerBackbone(nn.Module):
    """
    This class uses [`MaskFormerSwinModel`] to reshape its `hidden_states` from (`batch_size, sequence_length,
    hidden_size)` to (`batch_size, num_channels, height, width)`).

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

def __init__(self, config: SwinConfig):
        super().__init__()
        self.model = MaskFormerSwinModel(config)
        self.hidden_states_norms = nn.ModuleList([nn.LayerNorm(out_shape) for out_shape in self.outputs_shapes])

def forward(self, *args, **kwargs) -> List[Tensor]:
        output = self.model(*args, **kwargs, output_hidden_states=True)
        hidden_states_permuted: List[Tensor] = []
        # we need to reshape the hidden state to their original spatial dimensions
        # skipping the embeddings
        hidden_states: Tuple[Tuple[Tensor]] = output.hidden_states[1:]
        # spatial dimensions contains all the heights and widths of each stage, including after the embeddings
        spatial_dimensions: Tuple[Tuple[int, int]] = output.hidden_states_spatial_dimensions
        for i, (hidden_state, (height, width)) in enumerate(zip(hidden_states, 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)
            # our pixel decoder (FPN) expect 3D tensors (features)
            batch_size, _, hidden_size = hidden_state_norm.shape
            # reshape our tensor "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()
            )
            hidden_states_permuted.append(hidden_state_permuted)
        return hidden_states_permuted

@property
    def input_resolutions(self) -> List[int]:
        return [layer.input_resolution for layer in self.model.encoder.layers]

@property
    def outputs_shapes(self) -> List[int]:
        return [layer.dim for layer in self.model.encoder.layers]

class MaskFormerFPNConvLayer(nn.Sequential):
    def __init__(self, in_features: int, out_features: int, kernel_size: int = 3, padding: int = 1):
        """
        A basic module that executes conv - norm - in sequence used in MaskFormer.

Args:
            in_features (`int`):
                The number of input features (channels).
            out_features (`int`):
                The number of outputs features (channels).
        """
        super().__init__(
            nn.Conv2d(in_features, out_features, kernel_size=kernel_size, padding=padding, bias=False),
            nn.GroupNorm(32, out_features),
            nn.ReLU(inplace=True),
        )

class MaskFormerFPNLayer(nn.Module):
    def __init__(self, in_features: int, lateral_features: int):
        """
        A Feature Pyramid Network Layer (FPN) layer. It creates a feature map by aggregating features from the previous
        and backbone layer. Due to the spatial mismatch, the tensor coming from the previous layer is upsampled.

Args:
            in_features (`int`):
                The number of input features (channels).
            lateral_features (`int`):
                The number of lateral features (channels).
        """
        super().__init__()
        self.proj = nn.Sequential(
            nn.Conv2d(lateral_features, in_features, kernel_size=1, padding=0, bias=False),
            nn.GroupNorm(32, in_features),
        )

self.block = MaskFormerFPNConvLayer(in_features, in_features)

def forward(self, down: Tensor, left: Tensor) -> Tensor:
        left = self.proj(left)
        down = nn.functional.interpolate(down, size=left.shape[-2:], mode="nearest")
        down += left
        down = self.block(down)
        return down

class MaskFormerFPNModel(nn.Module):
    def __init__(self, in_features: int, lateral_widths: List[int], feature_size: int = 256):
        """
        Feature Pyramid Network, given an input tensor and a set of feature map of different feature/spatial size, it
        creates a list of feature maps with the same feature size.

Args:
            in_features (`int`):
                The number of input features (channels).
            lateral_widths (`List[int]`):
                A list with the features (channels) size of each lateral connection.
            feature_size (int, *optional*, defaults to 256):
                The features (channels) of the resulting feature maps.
        """
        super().__init__()
        self.stem = MaskFormerFPNConvLayer(in_features, feature_size)
        self.layers = nn.Sequential(
            *[MaskFormerFPNLayer(feature_size, lateral_width) for lateral_width in lateral_widths[::-1]]
        )

def forward(self, features: List[Tensor]) -> List[Tensor]:
        fpn_features = []
        last_feature = features[-1]
        other_features = features[:-1]
        output = self.stem(last_feature)
        for layer, left in zip(self.layers, other_features[::-1]):
            output = layer(output, left)
            fpn_features.append(output)
        return fpn_features

class MaskFormerPixelDecoder(nn.Module):
    def __init__(self, *args, feature_size: int = 256, mask_feature_size: int = 256, **kwargs):
        """
        Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
        Segmentation](https://arxiv.org/abs/2107.06278). It first runs the backbone's feature into a Feature Pyramid
        Network creating a list of feature maps. Then, it projects the last one to the correct `mask_size`.

Args:
            feature_size (`int`, *optional*, defaults to 256):
                The feature size (channel dimension) of the FPN feature maps.
            mask_feature_size (`int`, *optional*, defaults to 256):
                The features (channels) of the target masks size \\C_{\epsilon}\\ in the paper.
        """
        super().__init__()
        self.fpn = MaskFormerFPNModel(*args, feature_size=feature_size, **kwargs)
        self.mask_projection = nn.Conv2d(feature_size, mask_feature_size, kernel_size=3, padding=1)

def forward(self, features: List[Tensor], output_hidden_states: bool = False) -> MaskFormerPixelDecoderOutput:
        fpn_features: List[Tensor] = self.fpn(features)
        # we use the last feature map
        last_feature_projected = self.mask_projection(fpn_features[-1])
        return MaskFormerPixelDecoderOutput(
            last_hidden_state=last_feature_projected, hidden_states=tuple(fpn_features) if output_hidden_states else ()
        )

# copied and adapted from original implementation, also practically equal to DetrSinePositionEmbedding
class MaskFormerSinePositionEmbedding(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
    need paper, generalized to work on images.
    """

def __init__(
        self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: Optional[float] = None
    ):
        super().__init__()
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        self.scale = 2 * torch.pi if scale is None else scale

def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor:
        if mask is None:
            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
        not_mask = ~mask
        y_embed = not_mask.cumsum(1, dtype=torch.float32)
        x_embed = not_mask.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale

dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats)

pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos

class MaskformerMLPPredictionHead(nn.Sequential):
    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
        """
        A classic Multi Layer Perceptron (MLP).

Args:
            input_dim (`int`):
                The input dimensions.
            hidden_dim (`int`):
                The hidden dimensions.
            output_dim (`int`):
                The output dimensions.
            num_layers (int, *optional*, defaults to 3):
                The number of layers.
        """
        in_dims = [input_dim] + [hidden_dim] * (num_layers - 1)
        out_dims = [hidden_dim] * (num_layers - 1) + [output_dim]

layers = []
        for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)):

layer = nn.Sequential(
                nn.Linear(in_dim, out_dim), nn.ReLU(inplace=True) if i < num_layers - 1 else nn.Identity()
            )
            layers.append(layer)

super().__init__(*layers)

class MaskFormerPixelLevelModule(nn.Module):
    def __init__(self, config: MaskFormerConfig):
        """
        Pixel Level Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
        Segmentation](https://arxiv.org/abs/2107.06278). It runs the input image through a backbone and a pixel
        decoder, generating an image feature map and pixel embeddings.

Args:
            config ([`MaskFormerConfig`]):
                The configuration used to instantiate this model.
        """
        super().__init__()
        self.encoder = MaskFormerSwinTransformerBackbone(config.backbone_config)
        self.decoder = MaskFormerPixelDecoder(
            in_features=self.encoder.outputs_shapes[-1],
            feature_size=config.fpn_feature_size,
            mask_feature_size=config.mask_feature_size,
            lateral_widths=self.encoder.outputs_shapes[:-1],
        )

def forward(self, pixel_values: Tensor, output_hidden_states: bool = False) -> MaskFormerPixelLevelModuleOutput:
        features: List[Tensor] = self.encoder(pixel_values)
        decoder_output: MaskFormerPixelDecoderOutput = self.decoder(features, output_hidden_states)
        return MaskFormerPixelLevelModuleOutput(
            # the last feature is actually the output from the last layer
            encoder_last_hidden_state=features[-1],
            decoder_last_hidden_state=decoder_output.last_hidden_state,
            encoder_hidden_states=tuple(features) if output_hidden_states else (),
            decoder_hidden_states=decoder_output.hidden_states if output_hidden_states else (),
        )

class MaskFormerTransformerModule(nn.Module):
    """
    The MaskFormer's transformer module.
    """

def __init__(self, in_features: int, config: MaskFormerConfig):
        super().__init__()
        hidden_size = config.decoder_config.hidden_size
        should_project = in_features != hidden_size
        self.position_embedder = MaskFormerSinePositionEmbedding(num_pos_feats=hidden_size // 2, normalize=True)
        self.queries_embedder = nn.Embedding(config.decoder_config.num_queries, hidden_size)
        self.input_projection = nn.Conv2d(in_features, hidden_size, kernel_size=1) if should_project else None
        self.decoder = DetrDecoder(config=config.decoder_config)

def forward(
        self, image_features: Tensor, output_hidden_states: bool = False, output_attentions: bool = False
    ) -> DetrDecoderOutput:
        if self.input_projection is not None:
            image_features = self.input_projection(image_features)
        position_embeddings = self.position_embedder(image_features)
        # repeat the queries "q c -> b q c"
        batch_size = image_features.shape[0]
        queries_embeddings = self.queries_embedder.weight.unsqueeze(0).repeat(batch_size, 1, 1)
        inputs_embeds = torch.zeros_like(queries_embeddings, requires_grad=True)

batch_size, num_channels, height, width = image_features.shape
        # rearrange both image_features and position_embeddings "b c h w -> b (h w) c"
        image_features = image_features.view(batch_size, num_channels, height * width).permute(0, 2, 1)
        position_embeddings = position_embeddings.view(batch_size, num_channels, height * width).permute(0, 2, 1)

decoder_output: DetrDecoderOutput = self.decoder(
            inputs_embeds=inputs_embeds,
            attention_mask=None,
            encoder_hidden_states=image_features,
            encoder_attention_mask=None,
            position_embeddings=position_embeddings,
            query_position_embeddings=queries_embeddings,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=None,
        )
        return decoder_output

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

Parameters:
        config ([`MaskFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

MASKFORMER_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoFeatureExtractor`]. See
            [`AutoFeatureExtractor.__call__`] for details.
        pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:

- 1 for pixels that are real (i.e. **not masked**),
            - 0 for pixels that are padding (i.e. **masked**).

[What are attention masks?](../glossary#attention-mask)
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of Detr's decoder attention layers.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~MaskFormerModelOutput`] instead of a plain tuple.
"""

class MaskFormerPreTrainedModel(PreTrainedModel):
    config_class = MaskFormerConfig
    base_model_prefix = "model"
    main_input_name = "pixel_values"

def _init_weights(self, module: nn.Module):
        xavier_std = self.config.init_xavier_std
        std = self.config.init_std
        if isinstance(module, MaskFormerTransformerModule):
            if module.input_projection is not None:
                nn.init.xavier_uniform_(module.input_projection.weight, gain=xavier_std)
                nn.init.constant_(module.input_projection.bias, 0)
        # FPN
        elif isinstance(module, MaskFormerFPNModel):
            nn.init.xavier_uniform_(module.stem[0].weight, gain=xavier_std)

elif isinstance(module, MaskFormerFPNLayer):
            nn.init.xavier_uniform_(module.proj[0].weight, gain=xavier_std)

elif isinstance(module, MaskFormerFPNConvLayer):
            nn.init.xavier_uniform_(module[0].weight, gain=xavier_std)
        # The MLP head
        elif isinstance(module, MaskformerMLPPredictionHead):
            # I was not able to find the correct initializer in the original implementation
            # we'll use xavier
            for layer in module:
                nn.init.xavier_uniform_(layer[0].weight, gain=xavier_std)
                nn.init.constant_(layer[0].bias, 0)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        # copied from DETR
        if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
            # 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=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, MaskFormerSwinEncoder):
            module.gradient_checkpointing = value
        if isinstance(module, DetrDecoder):
            module.gradient_checkpointing = value

@add_start_docstrings(
    "The bare MaskFormer Model outputting raw hidden-states without any specific head on top.",
    MASKFORMER_START_DOCSTRING,
)
class MaskFormerModel(MaskFormerPreTrainedModel):
    def __init__(self, config: MaskFormerConfig):
        super().__init__(config)
        self.pixel_level_module = MaskFormerPixelLevelModule(config)
        self.transformer_module = MaskFormerTransformerModule(
            in_features=self.pixel_level_module.encoder.outputs_shapes[-1], config=config
        )

self.post_init()

@add_start_docstrings_to_model_forward(MASKFORMER_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskFormerModelOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
    )
    def forward(
        self,
        pixel_values: Tensor,
        pixel_mask: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> MaskFormerModelOutput:

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

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

batch_size, _, height, width = pixel_values.shape

if pixel_mask is None:
            pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)

pixel_level_module_output: MaskFormerPixelLevelModuleOutput = self.pixel_level_module(
            pixel_values, output_hidden_states
        )
        image_features = pixel_level_module_output.encoder_last_hidden_state
        pixel_embeddings = pixel_level_module_output.decoder_last_hidden_state

transformer_module_output: DetrDecoderOutput = self.transformer_module(
            image_features, output_hidden_states, output_attentions
        )
        queries = transformer_module_output.last_hidden_state

encoder_hidden_states = None
        pixel_decoder_hidden_states = None
        transformer_decoder_hidden_states = None
        hidden_states = None

if output_hidden_states:
            encoder_hidden_states = pixel_level_module_output.encoder_hidden_states
            pixel_decoder_hidden_states = pixel_level_module_output.decoder_hidden_states
            transformer_decoder_hidden_states = transformer_module_output.hidden_states
            hidden_states = encoder_hidden_states + pixel_decoder_hidden_states + transformer_decoder_hidden_states

output = MaskFormerModelOutput(
            encoder_last_hidden_state=image_features,
            pixel_decoder_last_hidden_state=pixel_embeddings,
            transformer_decoder_last_hidden_state=queries,
            encoder_hidden_states=encoder_hidden_states,
            pixel_decoder_hidden_states=pixel_decoder_hidden_states,
            transformer_decoder_hidden_states=transformer_decoder_hidden_states,
            hidden_states=hidden_states,
            attentions=transformer_module_output.attentions,
        )

if not return_dict:
            output = tuple(v for v in output.values())

return output

class MaskFormerForInstanceSegmentation(MaskFormerPreTrainedModel):
    def __init__(self, config: MaskFormerConfig):
        super().__init__(config)
        self.model = MaskFormerModel(config)
        hidden_size = config.decoder_config.hidden_size
        # + 1 because we add the "null" class
        self.class_predictor = nn.Linear(hidden_size, config.num_labels + 1)
        self.mask_embedder = MaskformerMLPPredictionHead(hidden_size, hidden_size, config.mask_feature_size)

self.matcher = MaskFormerHungarianMatcher(
            cost_class=1.0, cost_dice=config.dice_weight, cost_mask=config.mask_weight
        )

self.weight_dict: Dict[str, float] = {
            "loss_cross_entropy": config.cross_entropy_weight,
            "loss_mask": config.mask_weight,
            "loss_dice": config.dice_weight,
        }

self.criterion = MaskFormerLoss(
            config.num_labels,
            matcher=self.matcher,
            weight_dict=self.weight_dict,
            eos_coef=config.no_object_weight,
        )

self.post_init()

def get_loss_dict(
        self,
        masks_queries_logits: Tensor,
        class_queries_logits: Tensor,
        mask_labels: Tensor,
        class_labels: Tensor,
        auxiliary_logits: Dict[str, Tensor],
    ) -> Dict[str, Tensor]:
        loss_dict: Dict[str, Tensor] = self.criterion(
            masks_queries_logits, class_queries_logits, mask_labels, class_labels, auxiliary_logits
        )
        # weight each loss by `self.weight_dict[<LOSS_NAME>]` including auxiliary losses
        for key, weight in self.weight_dict.items():
            for loss_key, loss in loss_dict.items():
                if key in loss_key:
                    loss *= weight

return loss_dict

def get_loss(self, loss_dict: Dict[str, Tensor]) -> Tensor:
        return sum(loss_dict.values())

def get_logits(self, outputs: MaskFormerModelOutput) -> Tuple[Tensor, Tensor, Dict[str, Tensor]]:
        pixel_embeddings = outputs.pixel_decoder_last_hidden_state
        # get the auxiliary predictions (one for each decoder's layer)
        auxiliary_logits: List[str, Tensor] = []
        # This code is a little bit cumbersome, an improvement can be to return a list of predictions. If we have auxiliary loss then we are going to return more than one element in the list
        if self.config.use_auxiliary_loss:
            stacked_transformer_decoder_outputs = torch.stack(outputs.transformer_decoder_hidden_states)
            classes = self.class_predictor(stacked_transformer_decoder_outputs)
            class_queries_logits = classes[-1]
            # get the masks
            mask_embeddings = self.mask_embedder(stacked_transformer_decoder_outputs)
            # sum up over the channels for each embedding
            binaries_masks = torch.einsum("lbqc,   bchw -> lbqhw", mask_embeddings, pixel_embeddings)
            masks_queries_logits = binaries_masks[-1]
            # go til [:-1] because the last one is always used
            for aux_binary_masks, aux_classes in zip(binaries_masks[:-1], classes[:-1]):
                auxiliary_logits.append(
                    {"masks_queries_logits": aux_binary_masks, "class_queries_logits": aux_classes}
                )

else:
            transformer_decoder_hidden_states = outputs.transformer_decoder_last_hidden_state
            classes = self.class_predictor(transformer_decoder_hidden_states)
            class_queries_logits = classes
            # get the masks
            mask_embeddings = self.mask_embedder(transformer_decoder_hidden_states)
            # sum up over the channels
            masks_queries_logits = torch.einsum("bqc,   bchw -> bqhw", mask_embeddings, pixel_embeddings)

return class_queries_logits, masks_queries_logits, auxiliary_logits

@add_start_docstrings_to_model_forward(MASKFORMER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=MaskFormerForInstanceSegmentationOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values: Tensor,
        mask_labels: Optional[List[Tensor]] = None,
        class_labels: Optional[List[Tensor]] = None,
        pixel_mask: Optional[Tensor] = None,
        output_auxiliary_logits: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> MaskFormerForInstanceSegmentationOutput:
        r"""
        mask_labels (`List[torch.Tensor]`, *optional*):
            List of mask labels of shape `(num_labels, height, width)` to be fed to a model
        class_labels (`List[torch.LongTensor]`, *optional*):
            list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They identify the
            labels of `mask_labels`, e.g. the label of `mask_labels[i][j]` if `class_labels[i][j]`.

Returns:

Examples:

```python
        >>> from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
        >>> from PIL import Image
        >>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-ade")
        >>> inputs = feature_extractor(images=image, return_tensors="pt")

>>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade")
        >>> outputs = model(**inputs)
        >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
        >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
        >>> class_queries_logits = outputs.class_queries_logits
        >>> masks_queries_logits = outputs.masks_queries_logits

>>> # you can pass them to feature_extractor for postprocessing
        >>> output = feature_extractor.post_process_segmentation(outputs)
        >>> output = feature_extractor.post_process_semantic_segmentation(outputs)
        >>> output = feature_extractor.post_process_panoptic_segmentation(outputs)
        ```
        """

outputs: MaskFormerModelOutput = self.model(
            pixel_values,
            pixel_mask,
            output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
            return_dict=True,
            output_attentions=output_attentions,
        )

loss, loss_dict, auxiliary_logits = None, None, None

class_queries_logits, masks_queries_logits, auxiliary_logits = self.get_logits(outputs)

if mask_labels is not None and class_labels is not None:
            loss_dict: Dict[str, Tensor] = self.get_loss_dict(
                masks_queries_logits, class_queries_logits, mask_labels, class_labels, auxiliary_logits
            )
            loss = self.get_loss(loss_dict)

output_auxiliary_logits = (
            self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits
        )
        if not output_auxiliary_logits:
            auxiliary_logits = None

output = MaskFormerForInstanceSegmentationOutput(
            loss=loss,
            **outputs,
            class_queries_logits=class_queries_logits,
            masks_queries_logits=masks_queries_logits,
            auxiliary_logits=auxiliary_logits,
        )

if not return_dict:
            output = tuple(v for v in output.values())
            if loss is not None:
                output = ((loss)) + output
        return output

cmd: