# coding=utf-8
# Copyright 2021 NVIDIA 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 SegFormer model."""


import collections
import math
import warnings

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...file_utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    replace_return_docstrings,
)
from ...modeling_outputs import BaseModelOutput, SemanticSegmentationModelOutput, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import logging
from .configuration_segformer import SegformerConfig


logger = logging.get_logger(__name__)


# General docstring
_CONFIG_FOR_DOC = "SegformerConfig"
_FEAT_EXTRACTOR_FOR_DOC = "SegformerFeatureExtractor"

# Base docstring
_CHECKPOINT_FOR_DOC = "nvidia/mit-b0"
_EXPECTED_OUTPUT_SHAPE = [1, 256, 256]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "nvidia/mit-b0"
_IMAGE_CLASS_EXPECTED_OUTPUT = "'tabby, tabby cat'"

SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "nvidia/segformer-b0-finetuned-ade-512-512",
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
]


# Inspired by
# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
# From PyTorch internals
def to_2tuple(x):
    if isinstance(x, collections.abc.Iterable):
        return x
    return (x, x)


# Stochastic depth implementation
# Taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the
    DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop
    Connect' is a different form of dropout in a separate paper... See discussion:
    https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and
    argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument.
    """
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob=None):
        super().__init__()
        self.drop_prob = drop_prob

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


class SegformerOverlapPatchEmbeddings(nn.Module):
    """Construct the patch embeddings from an image."""

    def __init__(self, image_size, patch_size, stride, num_channels, hidden_size):
        super().__init__()
        image_size = to_2tuple(image_size)
        patch_size = to_2tuple(patch_size)
        self.height, self.width = image_size[0] // patch_size[0], image_size[1] // patch_size[1]
        self.num_patches = self.height * self.width
        self.proj = nn.Conv2d(
            num_channels,
            hidden_size,
            kernel_size=patch_size,
            stride=stride,
            padding=(patch_size[0] // 2, patch_size[1] // 2),
        )

        self.layer_norm = nn.LayerNorm(hidden_size)

    def forward(self, pixel_values):
        x = self.proj(pixel_values)
        _, _, height, width = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.layer_norm(x)
        return x, height, width


class SegformerEfficientSelfAttention(nn.Module):
    def __init__(self, config, hidden_size, num_attention_heads, sr_ratio):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads

        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
                f"heads ({self.num_attention_heads})"
            )

        self.attention_head_size = int(self.hidden_size / self.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(self.hidden_size, self.all_head_size)
        self.key = nn.Linear(self.hidden_size, self.all_head_size)
        self.value = nn.Linear(self.hidden_size, self.all_head_size)

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

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(hidden_size, hidden_size, kernel_size=sr_ratio, stride=sr_ratio)
            self.layer_norm = nn.LayerNorm(hidden_size)

    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,
        height,
        width,
        output_attentions=False,
    ):
        query_layer = self.transpose_for_scores(self.query(hidden_states))

        if self.sr_ratio > 1:
            batch_size, seq_len, num_channels = hidden_states.shape
            hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
            hidden_states = self.sr(hidden_states)
            hidden_states = hidden_states.reshape(batch_size, num_channels, -1).permute(0, 2, 1)
            hidden_states = self.layer_norm(hidden_states)

        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))

        # 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)

        # 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)

        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


class SegformerSelfOutput(nn.Module):
    def __init__(self, config, hidden_size):
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


class SegformerAttention(nn.Module):
    def __init__(self, config, hidden_size, num_attention_heads, sr_ratio):
        super().__init__()
        self.self = SegformerEfficientSelfAttention(
            config=config, hidden_size=hidden_size, num_attention_heads=num_attention_heads, sr_ratio=sr_ratio
        )
        self.output = SegformerSelfOutput(config, hidden_size=hidden_size)
        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, height, width, output_attentions=False):
        self_outputs = self.self(hidden_states, height, width, 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


class SegformerDWConv(nn.Module):
    def __init__(self, dim=768):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, hidden_states, height, width):
        batch_size, seq_len, num_channels = hidden_states.shape
        hidden_states = hidden_states.transpose(1, 2).view(batch_size, num_channels, height, width)
        hidden_states = self.dwconv(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

        return hidden_states


class SegformerMixFFN(nn.Module):
    def __init__(self, config, in_features, hidden_features=None, out_features=None):
        super().__init__()
        out_features = out_features or in_features
        self.dense1 = nn.Linear(in_features, hidden_features)
        self.dwconv = SegformerDWConv(hidden_features)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act
        self.dense2 = nn.Linear(hidden_features, out_features)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, height, width):
        hidden_states = self.dense1(hidden_states)
        hidden_states = self.dwconv(hidden_states, height, width)
        hidden_states = self.intermediate_act_fn(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.dense2(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


class SegformerLayer(nn.Module):
    """This corresponds to the Block class in the original implementation."""

    def __init__(self, config, hidden_size, num_attention_heads, drop_path, sr_ratio, mlp_ratio):
        super().__init__()
        self.layer_norm_1 = nn.LayerNorm(hidden_size)
        self.attention = SegformerAttention(
            config, hidden_size=hidden_size, num_attention_heads=num_attention_heads, sr_ratio=sr_ratio
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.layer_norm_2 = nn.LayerNorm(hidden_size)
        mlp_hidden_size = int(hidden_size * mlp_ratio)
        self.mlp = SegformerMixFFN(config, in_features=hidden_size, hidden_features=mlp_hidden_size)

    def forward(self, hidden_states, height, width, output_attentions=False):
        self_attention_outputs = self.attention(
            self.layer_norm_1(hidden_states),  # in Segformer, layernorm is applied before self-attention
            height,
            width,
            output_attentions=output_attentions,
        )

        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        # first residual connection (with stochastic depth)
        attention_output = self.drop_path(attention_output)
        hidden_states = attention_output + hidden_states

        mlp_output = self.mlp(self.layer_norm_2(hidden_states), height, width)

        # second residual connection (with stochastic depth)
        mlp_output = self.drop_path(mlp_output)
        layer_output = mlp_output + hidden_states

        outputs = (layer_output,) + outputs

        return outputs


class SegformerEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        # stochastic depth decay rule
        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]

        # patch embeddings
        embeddings = []
        for i in range(config.num_encoder_blocks):
            embeddings.append(
                SegformerOverlapPatchEmbeddings(
                    image_size=config.image_size // config.downsampling_rates[i],
                    patch_size=config.patch_sizes[i],
                    stride=config.strides[i],
                    num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1],
                    hidden_size=config.hidden_sizes[i],
                )
            )
        self.patch_embeddings = nn.ModuleList(embeddings)

        # Transformer blocks
        blocks = []
        cur = 0
        for i in range(config.num_encoder_blocks):
            # each block consists of layers
            layers = []
            if i != 0:
                cur += config.depths[i - 1]
            for j in range(config.depths[i]):
                layers.append(
                    SegformerLayer(
                        config,
                        hidden_size=config.hidden_sizes[i],
                        num_attention_heads=config.num_attention_heads[i],
                        drop_path=dpr[cur + j],
                        sr_ratio=config.sr_ratios[i],
                        mlp_ratio=config.mlp_ratios[i],
                    )
                )
            blocks.append(nn.ModuleList(layers))

        self.block = nn.ModuleList(blocks)

        # Layer norms
        self.layer_norm = nn.ModuleList(
            [nn.LayerNorm(config.hidden_sizes[i]) for i in range(config.num_encoder_blocks)]
        )

    def forward(
        self,
        pixel_values,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        batch_size = pixel_values.shape[0]

        hidden_states = pixel_values
        for idx, x in enumerate(zip(self.patch_embeddings, self.block, self.layer_norm)):
            embedding_layer, block_layer, norm_layer = x
            # first, obtain patch embeddings
            hidden_states, height, width = embedding_layer(hidden_states)
            # second, send embeddings through blocks
            for i, blk in enumerate(block_layer):
                layer_outputs = blk(hidden_states, height, width, output_attentions)
                hidden_states = layer_outputs[0]
                if output_attentions:
                    all_self_attentions = all_self_attentions + (layer_outputs[1],)
            # third, apply layer norm
            hidden_states = norm_layer(hidden_states)
            # fourth, optionally reshape back to (batch_size, num_channels, height, width)
            if idx != len(self.patch_embeddings) - 1 or (
                idx == len(self.patch_embeddings) - 1 and self.config.reshape_last_stage
            ):
                hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous()
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

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


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

    config_class = SegformerConfig
    base_model_prefix = "segformer"
    main_input_name = "pixel_values"

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


SEGFORMER_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 ([`SegformerConfig`]): 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.
"""

SEGFORMER_INPUTS_DOCSTRING = r"""

    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`SegformerFeatureExtractor`]. See [`SegformerFeatureExtractor.__call__`] for details.

        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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare SegFormer encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.",
    SEGFORMER_START_DOCSTRING,
)
class SegformerModel(SegformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config

        # hierarchical Transformer encoder
        self.encoder = SegformerEncoder(config)

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

    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)

    @add_start_docstrings_to_model_forward(SEGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def forward(self, pixel_values, 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

        encoder_outputs = self.encoder(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]

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

        return BaseModelOutput(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


@add_start_docstrings(
    """
    SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden
    states) e.g. for ImageNet.
    """,
    SEGFORMER_START_DOCSTRING,
)
class SegformerForImageClassification(SegformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.segformer = SegformerModel(config)

        # Classifier head
        self.classifier = nn.Linear(config.hidden_sizes[-1], config.num_labels)

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

    @add_start_docstrings_to_model_forward(SEGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    def forward(
        self,
        pixel_values=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.segformer(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        # reshape last hidden states to (batch_size, height*width, hidden_size)
        batch_size = sequence_output.shape[0]
        sequence_output = sequence_output.reshape(batch_size, -1, self.config.hidden_sizes[-1])

        # global average pooling
        sequence_output = sequence_output.mean(dim=1)

        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1), labels.view(-1))
            else:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class SegformerMLP(nn.Module):
    """
    Linear Embedding.
    """

    def __init__(self, config: SegformerConfig, input_dim):
        super().__init__()
        self.proj = nn.Linear(input_dim, config.decoder_hidden_size)

    def forward(self, hidden_states: torch.Tensor):
        hidden_states = hidden_states.flatten(2).transpose(1, 2)
        hidden_states = self.proj(hidden_states)
        return hidden_states


class SegformerDecodeHead(SegformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        # linear layers which will unify the channel dimension of each of the encoder blocks to the same config.decoder_hidden_size
        mlps = []
        for i in range(config.num_encoder_blocks):
            mlp = SegformerMLP(config, input_dim=config.hidden_sizes[i])
            mlps.append(mlp)
        self.linear_c = nn.ModuleList(mlps)

        # the following 3 layers implement the ConvModule of the original implementation
        self.linear_fuse = nn.Conv2d(
            in_channels=config.decoder_hidden_size * config.num_encoder_blocks,
            out_channels=config.decoder_hidden_size,
            kernel_size=1,
            bias=False,
        )
        self.batch_norm = nn.BatchNorm2d(config.decoder_hidden_size)
        self.activation = nn.ReLU()

        self.dropout = nn.Dropout(config.classifier_dropout_prob)
        self.classifier = nn.Conv2d(config.decoder_hidden_size, config.num_labels, kernel_size=1)

    def forward(self, encoder_hidden_states):
        batch_size, _, _, _ = encoder_hidden_states[-1].shape
        all_hidden_states = ()
        for encoder_hidden_state, mlp in zip(encoder_hidden_states, self.linear_c):
            # unify channel dimension
            height, width = encoder_hidden_state.shape[2], encoder_hidden_state.shape[3]
            encoder_hidden_state = mlp(encoder_hidden_state)
            encoder_hidden_state = encoder_hidden_state.permute(0, 2, 1)
            encoder_hidden_state = encoder_hidden_state.reshape(batch_size, -1, height, width)
            # upsample
            encoder_hidden_state = nn.functional.interpolate(
                encoder_hidden_state, size=encoder_hidden_states[0].size()[2:], mode="bilinear", align_corners=False
            )
            all_hidden_states += (encoder_hidden_state,)

        hidden_states = self.linear_fuse(torch.cat(all_hidden_states[::-1], dim=1))
        hidden_states = self.batch_norm(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.dropout(hidden_states)

        # logits are of shape (batch_size, num_labels, height/4, width/4)
        logits = self.classifier(hidden_states)

        return logits


@add_start_docstrings(
    """SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.""",
    SEGFORMER_START_DOCSTRING,
)
class SegformerForSemanticSegmentation(SegformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.segformer = SegformerModel(config)
        self.decode_head = SegformerDecodeHead(config)

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

    @add_start_docstrings_to_model_forward(SEGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(output_type=SemanticSegmentationModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        legacy_output=None,
    ):
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
        legacy_output (`bool`, *optional*):
            Whether to return the legacy outputs or not (with logits of shape `height / 4 , width / 4`). Will default
            to `self.config.legacy_output`.

            This argument is only present for backward compatibility reasons and will be removed in v5 of Transformers.

        Returns:

        Examples:

        ```python
        >>> from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
        >>> from PIL import Image
        >>> import requests

        >>> feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
        >>> model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = feature_extractor(images=image, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits  # shape (batch_size, num_labels, height, width)
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        legacy_output = legacy_output if legacy_output is not None else self.config.legacy_output
        if not legacy_output:
            warnings.warn(
                "The output of this model has changed in v4.17.0 and the logits now have the same size as the inputs. "
                "You can activate the previous behavior by passing `legacy_output=True` to this call or the "
                "configuration of this model (only until v5, then that argument will be removed).",
                FutureWarning,
            )

        outputs = self.segformer(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=True,  # we need the intermediate hidden states
            return_dict=return_dict,
        )

        encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]

        logits = self.decode_head(encoder_hidden_states)

        upsampled_logits = nn.functional.interpolate(
            logits, size=pixel_values.shape[-2:], mode="bilinear", align_corners=False
        )

        loss = None
        if labels is not None:
            if self.config.num_labels == 1:
                raise ValueError("The number of labels should be greater than one")
            else:
                # upsample logits to the images' original size
                loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
                loss = loss_fct(upsampled_logits, labels)

        if not return_dict:
            if output_hidden_states:
                output = (logits if legacy_output else upsampled_logits,) + outputs[1:]
            else:
                output = (logits if legacy_output else upsampled_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        if legacy_output:
            return SequenceClassifierOutput(
                loss=loss,
                logits=logits,
                hidden_states=outputs.hidden_states if output_hidden_states else None,
                attentions=outputs.attentions,
            )
        else:
            return SemanticSegmentationModelOutput(
                loss=loss,
                logits=upsampled_logits,
                hidden_states=outputs.hidden_states if output_hidden_states else None,
                attentions=outputs.attentions,
            )
