# coding=utf-8
# Copyright 2023 Apple Inc. 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.
#
# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
"""PyTorch MobileViTV2 model."""

from typing import Optional, Tuple, Union

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

from ...activations import ACT2FN
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
    SemanticSegmenterOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_mobilevitv2 import MobileViTV2Config


logger = logging.get_logger(__name__)


# General docstring
_CONFIG_FOR_DOC = "MobileViTV2Config"

# Base docstring
_CHECKPOINT_FOR_DOC = "apple/mobilevitv2-1.0-imagenet1k-256"
_EXPECTED_OUTPUT_SHAPE = [1, 512, 8, 8]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "apple/mobilevitv2-1.0-imagenet1k-256"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"


# Copied from transformers.models.mobilevit.modeling_mobilevit.make_divisible
def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
    """
    Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
    original TensorFlow repo. It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    """
    if min_value is None:
        min_value = divisor
    new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_value < 0.9 * value:
        new_value += divisor
    return int(new_value)


def clip(value: float, min_val: float = float("-inf"), max_val: float = float("inf")) -> float:
    return max(min_val, min(max_val, value))


# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTConvLayer with MobileViT->MobileViTV2
class MobileViTV2ConvLayer(nn.Module):
    def __init__(
        self,
        config: MobileViTV2Config,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        groups: int = 1,
        bias: bool = False,
        dilation: int = 1,
        use_normalization: bool = True,
        use_activation: Union[bool, str] = True,
    ) -> None:
        super().__init__()
        padding = int((kernel_size - 1) / 2) * dilation

        if in_channels % groups != 0:
            raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
        if out_channels % groups != 0:
            raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")

        self.convolution = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
            padding_mode="zeros",
        )

        if use_normalization:
            self.normalization = nn.BatchNorm2d(
                num_features=out_channels,
                eps=1e-5,
                momentum=0.1,
                affine=True,
                track_running_stats=True,
            )
        else:
            self.normalization = None

        if use_activation:
            if isinstance(use_activation, str):
                self.activation = ACT2FN[use_activation]
            elif isinstance(config.hidden_act, str):
                self.activation = ACT2FN[config.hidden_act]
            else:
                self.activation = config.hidden_act
        else:
            self.activation = None

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        features = self.convolution(features)
        if self.normalization is not None:
            features = self.normalization(features)
        if self.activation is not None:
            features = self.activation(features)
        return features


# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTInvertedResidual with MobileViT->MobileViTV2
class MobileViTV2InvertedResidual(nn.Module):
    """
    Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381
    """

    def __init__(
        self, config: MobileViTV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1
    ) -> None:
        super().__init__()
        expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)

        if stride not in [1, 2]:
            raise ValueError(f"Invalid stride {stride}.")

        self.use_residual = (stride == 1) and (in_channels == out_channels)

        self.expand_1x1 = MobileViTV2ConvLayer(
            config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
        )

        self.conv_3x3 = MobileViTV2ConvLayer(
            config,
            in_channels=expanded_channels,
            out_channels=expanded_channels,
            kernel_size=3,
            stride=stride,
            groups=expanded_channels,
            dilation=dilation,
        )

        self.reduce_1x1 = MobileViTV2ConvLayer(
            config,
            in_channels=expanded_channels,
            out_channels=out_channels,
            kernel_size=1,
            use_activation=False,
        )

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        residual = features

        features = self.expand_1x1(features)
        features = self.conv_3x3(features)
        features = self.reduce_1x1(features)

        return residual + features if self.use_residual else features


# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTMobileNetLayer with MobileViT->MobileViTV2
class MobileViTV2MobileNetLayer(nn.Module):
    def __init__(
        self, config: MobileViTV2Config, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
    ) -> None:
        super().__init__()

        self.layer = nn.ModuleList()
        for i in range(num_stages):
            layer = MobileViTV2InvertedResidual(
                config,
                in_channels=in_channels,
                out_channels=out_channels,
                stride=stride if i == 0 else 1,
            )
            self.layer.append(layer)
            in_channels = out_channels

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        for layer_module in self.layer:
            features = layer_module(features)
        return features


class MobileViTV2LinearSelfAttention(nn.Module):
    """
    This layer applies a self-attention with linear complexity, as described in MobileViTV2 paper:
    https://arxiv.org/abs/2206.02680

    Args:
        config (`MobileVitv2Config`):
             Model configuration object
        embed_dim (`int`):
            `input_channels` from an expected input of size :math:`(batch_size, input_channels, height, width)`
    """

    def __init__(self, config: MobileViTV2Config, embed_dim: int) -> None:
        super().__init__()

        self.qkv_proj = MobileViTV2ConvLayer(
            config=config,
            in_channels=embed_dim,
            out_channels=1 + (2 * embed_dim),
            bias=True,
            kernel_size=1,
            use_normalization=False,
            use_activation=False,
        )

        self.attn_dropout = nn.Dropout(p=config.attn_dropout)
        self.out_proj = MobileViTV2ConvLayer(
            config=config,
            in_channels=embed_dim,
            out_channels=embed_dim,
            bias=True,
            kernel_size=1,
            use_normalization=False,
            use_activation=False,
        )
        self.embed_dim = embed_dim

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # (batch_size, embed_dim, num_pixels_in_patch, num_patches) --> (batch_size, 1+2*embed_dim, num_pixels_in_patch, num_patches)
        qkv = self.qkv_proj(hidden_states)

        # Project hidden_states into query, key and value
        # Query --> [batch_size, 1, num_pixels_in_patch, num_patches]
        # value, key --> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
        query, key, value = torch.split(qkv, split_size_or_sections=[1, self.embed_dim, self.embed_dim], dim=1)

        # apply softmax along num_patches dimension
        context_scores = torch.nn.functional.softmax(query, dim=-1)
        context_scores = self.attn_dropout(context_scores)

        # Compute context vector
        # [batch_size, embed_dim, num_pixels_in_patch, num_patches] x [batch_size, 1, num_pixels_in_patch, num_patches] -> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
        context_vector = key * context_scores
        # [batch_size, embed_dim, num_pixels_in_patch, num_patches] --> [batch_size, embed_dim, num_pixels_in_patch, 1]
        context_vector = torch.sum(context_vector, dim=-1, keepdim=True)

        # combine context vector with values
        # [batch_size, embed_dim, num_pixels_in_patch, num_patches] * [batch_size, embed_dim, num_pixels_in_patch, 1] --> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
        out = torch.nn.functional.relu(value) * context_vector.expand_as(value)
        out = self.out_proj(out)
        return out


class MobileViTV2FFN(nn.Module):
    def __init__(
        self,
        config: MobileViTV2Config,
        embed_dim: int,
        ffn_latent_dim: int,
        ffn_dropout: float = 0.0,
    ) -> None:
        super().__init__()
        self.conv1 = MobileViTV2ConvLayer(
            config=config,
            in_channels=embed_dim,
            out_channels=ffn_latent_dim,
            kernel_size=1,
            stride=1,
            bias=True,
            use_normalization=False,
            use_activation=True,
        )
        self.dropout1 = nn.Dropout(ffn_dropout)

        self.conv2 = MobileViTV2ConvLayer(
            config=config,
            in_channels=ffn_latent_dim,
            out_channels=embed_dim,
            kernel_size=1,
            stride=1,
            bias=True,
            use_normalization=False,
            use_activation=False,
        )
        self.dropout2 = nn.Dropout(ffn_dropout)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.dropout1(hidden_states)
        hidden_states = self.conv2(hidden_states)
        hidden_states = self.dropout2(hidden_states)
        return hidden_states


class MobileViTV2TransformerLayer(nn.Module):
    def __init__(
        self,
        config: MobileViTV2Config,
        embed_dim: int,
        ffn_latent_dim: int,
        dropout: float = 0.0,
    ) -> None:
        super().__init__()
        self.layernorm_before = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=config.layer_norm_eps)
        self.attention = MobileViTV2LinearSelfAttention(config, embed_dim)
        self.dropout1 = nn.Dropout(p=dropout)
        self.layernorm_after = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=config.layer_norm_eps)
        self.ffn = MobileViTV2FFN(config, embed_dim, ffn_latent_dim, config.ffn_dropout)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        layernorm_1_out = self.layernorm_before(hidden_states)
        attention_output = self.attention(layernorm_1_out)
        hidden_states = attention_output + hidden_states

        layer_output = self.layernorm_after(hidden_states)
        layer_output = self.ffn(layer_output)

        layer_output = layer_output + hidden_states
        return layer_output


class MobileViTV2Transformer(nn.Module):
    def __init__(self, config: MobileViTV2Config, n_layers: int, d_model: int) -> None:
        super().__init__()

        ffn_multiplier = config.ffn_multiplier

        ffn_dims = [ffn_multiplier * d_model] * n_layers

        # ensure that dims are multiple of 16
        ffn_dims = [int((d // 16) * 16) for d in ffn_dims]

        self.layer = nn.ModuleList()
        for block_idx in range(n_layers):
            transformer_layer = MobileViTV2TransformerLayer(
                config, embed_dim=d_model, ffn_latent_dim=ffn_dims[block_idx]
            )
            self.layer.append(transformer_layer)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        for layer_module in self.layer:
            hidden_states = layer_module(hidden_states)
        return hidden_states


class MobileViTV2Layer(nn.Module):
    """
    MobileViTV2 layer: https://arxiv.org/abs/2206.02680
    """

    def __init__(
        self,
        config: MobileViTV2Config,
        in_channels: int,
        out_channels: int,
        attn_unit_dim: int,
        n_attn_blocks: int = 2,
        dilation: int = 1,
        stride: int = 2,
    ) -> None:
        super().__init__()
        self.patch_width = config.patch_size
        self.patch_height = config.patch_size

        cnn_out_dim = attn_unit_dim

        if stride == 2:
            self.downsampling_layer = MobileViTV2InvertedResidual(
                config,
                in_channels=in_channels,
                out_channels=out_channels,
                stride=stride if dilation == 1 else 1,
                dilation=dilation // 2 if dilation > 1 else 1,
            )
            in_channels = out_channels
        else:
            self.downsampling_layer = None

        # Local representations
        self.conv_kxk = MobileViTV2ConvLayer(
            config,
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=config.conv_kernel_size,
            groups=in_channels,
        )
        self.conv_1x1 = MobileViTV2ConvLayer(
            config,
            in_channels=in_channels,
            out_channels=cnn_out_dim,
            kernel_size=1,
            use_normalization=False,
            use_activation=False,
        )

        # Global representations
        self.transformer = MobileViTV2Transformer(config, d_model=attn_unit_dim, n_layers=n_attn_blocks)

        # self.layernorm = MobileViTV2LayerNorm2D(attn_unit_dim, eps=config.layer_norm_eps)
        self.layernorm = nn.GroupNorm(num_groups=1, num_channels=attn_unit_dim, eps=config.layer_norm_eps)

        # Fusion
        self.conv_projection = MobileViTV2ConvLayer(
            config,
            in_channels=cnn_out_dim,
            out_channels=in_channels,
            kernel_size=1,
            use_normalization=True,
            use_activation=False,
        )

    def unfolding(self, feature_map: torch.Tensor) -> Tuple[torch.Tensor, Tuple[int, int]]:
        batch_size, in_channels, img_height, img_width = feature_map.shape
        patches = nn.functional.unfold(
            feature_map,
            kernel_size=(self.patch_height, self.patch_width),
            stride=(self.patch_height, self.patch_width),
        )
        patches = patches.reshape(batch_size, in_channels, self.patch_height * self.patch_width, -1)

        return patches, (img_height, img_width)

    def folding(self, patches: torch.Tensor, output_size: Tuple[int, int]) -> torch.Tensor:
        batch_size, in_dim, patch_size, n_patches = patches.shape
        patches = patches.reshape(batch_size, in_dim * patch_size, n_patches)

        feature_map = nn.functional.fold(
            patches,
            output_size=output_size,
            kernel_size=(self.patch_height, self.patch_width),
            stride=(self.patch_height, self.patch_width),
        )

        return feature_map

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        # reduce spatial dimensions if needed
        if self.downsampling_layer:
            features = self.downsampling_layer(features)

        # local representation
        features = self.conv_kxk(features)
        features = self.conv_1x1(features)

        # convert feature map to patches
        patches, output_size = self.unfolding(features)

        # learn global representations
        patches = self.transformer(patches)
        patches = self.layernorm(patches)

        # convert patches back to feature maps
        # [batch_size, patch_height, patch_width, input_dim] --> [batch_size, input_dim, patch_height, patch_width]
        features = self.folding(patches, output_size)

        features = self.conv_projection(features)
        return features


class MobileViTV2Encoder(nn.Module):
    def __init__(self, config: MobileViTV2Config) -> None:
        super().__init__()
        self.config = config

        self.layer = nn.ModuleList()
        self.gradient_checkpointing = False

        # segmentation architectures like DeepLab and PSPNet modify the strides
        # of the classification backbones
        dilate_layer_4 = dilate_layer_5 = False
        if config.output_stride == 8:
            dilate_layer_4 = True
            dilate_layer_5 = True
        elif config.output_stride == 16:
            dilate_layer_5 = True

        dilation = 1

        layer_0_dim = make_divisible(
            clip(value=32 * config.width_multiplier, min_val=16, max_val=64), divisor=8, min_value=16
        )

        layer_1_dim = make_divisible(64 * config.width_multiplier, divisor=16)
        layer_2_dim = make_divisible(128 * config.width_multiplier, divisor=8)
        layer_3_dim = make_divisible(256 * config.width_multiplier, divisor=8)
        layer_4_dim = make_divisible(384 * config.width_multiplier, divisor=8)
        layer_5_dim = make_divisible(512 * config.width_multiplier, divisor=8)

        layer_1 = MobileViTV2MobileNetLayer(
            config,
            in_channels=layer_0_dim,
            out_channels=layer_1_dim,
            stride=1,
            num_stages=1,
        )
        self.layer.append(layer_1)

        layer_2 = MobileViTV2MobileNetLayer(
            config,
            in_channels=layer_1_dim,
            out_channels=layer_2_dim,
            stride=2,
            num_stages=2,
        )
        self.layer.append(layer_2)

        layer_3 = MobileViTV2Layer(
            config,
            in_channels=layer_2_dim,
            out_channels=layer_3_dim,
            attn_unit_dim=make_divisible(config.base_attn_unit_dims[0] * config.width_multiplier, divisor=8),
            n_attn_blocks=config.n_attn_blocks[0],
        )
        self.layer.append(layer_3)

        if dilate_layer_4:
            dilation *= 2

        layer_4 = MobileViTV2Layer(
            config,
            in_channels=layer_3_dim,
            out_channels=layer_4_dim,
            attn_unit_dim=make_divisible(config.base_attn_unit_dims[1] * config.width_multiplier, divisor=8),
            n_attn_blocks=config.n_attn_blocks[1],
            dilation=dilation,
        )
        self.layer.append(layer_4)

        if dilate_layer_5:
            dilation *= 2

        layer_5 = MobileViTV2Layer(
            config,
            in_channels=layer_4_dim,
            out_channels=layer_5_dim,
            attn_unit_dim=make_divisible(config.base_attn_unit_dims[2] * config.width_multiplier, divisor=8),
            n_attn_blocks=config.n_attn_blocks[2],
            dilation=dilation,
        )
        self.layer.append(layer_5)

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ) -> Union[tuple, BaseModelOutputWithNoAttention]:
        all_hidden_states = () if output_hidden_states else None

        for i, layer_module in enumerate(self.layer):
            if self.gradient_checkpointing and self.training:
                hidden_states = self._gradient_checkpointing_func(
                    layer_module.__call__,
                    hidden_states,
                )
            else:
                hidden_states = layer_module(hidden_states)

            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] if v is not None)

        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)


# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTPreTrainedModel with MobileViT->MobileViTV2,mobilevit->mobilevitv2
class MobileViTV2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = MobileViTV2Config
    base_model_prefix = "mobilevitv2"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MobileViTV2Layer"]

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


MOBILEVITV2_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileViTV2Config`]): 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.
"""

MOBILEVITV2_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileViTImageProcessor.__call__`] for details.
        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.
"""


@add_start_docstrings(
    "The bare MobileViTV2 model outputting raw hidden-states without any specific head on top.",
    MOBILEVITV2_START_DOCSTRING,
)
class MobileViTV2Model(MobileViTV2PreTrainedModel):
    def __init__(self, config: MobileViTV2Config, expand_output: bool = True):
        super().__init__(config)
        self.config = config
        self.expand_output = expand_output

        layer_0_dim = make_divisible(
            clip(value=32 * config.width_multiplier, min_val=16, max_val=64), divisor=8, min_value=16
        )

        self.conv_stem = MobileViTV2ConvLayer(
            config,
            in_channels=config.num_channels,
            out_channels=layer_0_dim,
            kernel_size=3,
            stride=2,
            use_normalization=True,
            use_activation=True,
        )
        self.encoder = MobileViTV2Encoder(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_index, heads in heads_to_prune.items():
            mobilevitv2_layer = self.encoder.layer[layer_index]
            if isinstance(mobilevitv2_layer, MobileViTV2Layer):
                for transformer_layer in mobilevitv2_layer.transformer.layer:
                    transformer_layer.attention.prune_heads(heads)

    @add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPoolingAndNoAttention,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
        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")

        embedding_output = self.conv_stem(pixel_values)

        encoder_outputs = self.encoder(
            embedding_output,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.expand_output:
            last_hidden_state = encoder_outputs[0]

            # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
            pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False)
        else:
            last_hidden_state = encoder_outputs[0]
            pooled_output = None

        if not return_dict:
            output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
            return output + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
        )


@add_start_docstrings(
    """
    MobileViTV2 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """,
    MOBILEVITV2_START_DOCSTRING,
)
class MobileViTV2ForImageClassification(MobileViTV2PreTrainedModel):
    def __init__(self, config: MobileViTV2Config) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.mobilevitv2 = MobileViTV2Model(config)

        out_channels = make_divisible(512 * config.width_multiplier, divisor=8)  # layer 5 output dimension
        # Classifier head
        self.classifier = (
            nn.Linear(in_features=out_channels, out_features=config.num_labels)
            if config.num_labels > 0
            else nn.Identity()
        )

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

    @add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=ImageClassifierOutputWithNoAttention,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
        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.mobilevitv2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)

        pooled_output = outputs.pooler_output if return_dict else outputs[1]

        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

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

        return ImageClassifierOutputWithNoAttention(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
        )


# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTASPPPooling with MobileViT->MobileViTV2
class MobileViTV2ASPPPooling(nn.Module):
    def __init__(self, config: MobileViTV2Config, in_channels: int, out_channels: int) -> None:
        super().__init__()

        self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)

        self.conv_1x1 = MobileViTV2ConvLayer(
            config,
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            stride=1,
            use_normalization=True,
            use_activation="relu",
        )

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        spatial_size = features.shape[-2:]
        features = self.global_pool(features)
        features = self.conv_1x1(features)
        features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False)
        return features


class MobileViTV2ASPP(nn.Module):
    """
    ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587
    """

    def __init__(self, config: MobileViTV2Config) -> None:
        super().__init__()

        encoder_out_channels = make_divisible(512 * config.width_multiplier, divisor=8)  # layer 5 output dimension
        in_channels = encoder_out_channels
        out_channels = config.aspp_out_channels

        if len(config.atrous_rates) != 3:
            raise ValueError("Expected 3 values for atrous_rates")

        self.convs = nn.ModuleList()

        in_projection = MobileViTV2ConvLayer(
            config,
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            use_activation="relu",
        )
        self.convs.append(in_projection)

        self.convs.extend(
            [
                MobileViTV2ConvLayer(
                    config,
                    in_channels=in_channels,
                    out_channels=out_channels,
                    kernel_size=3,
                    dilation=rate,
                    use_activation="relu",
                )
                for rate in config.atrous_rates
            ]
        )

        pool_layer = MobileViTV2ASPPPooling(config, in_channels, out_channels)
        self.convs.append(pool_layer)

        self.project = MobileViTV2ConvLayer(
            config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu"
        )

        self.dropout = nn.Dropout(p=config.aspp_dropout_prob)

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        pyramid = []
        for conv in self.convs:
            pyramid.append(conv(features))
        pyramid = torch.cat(pyramid, dim=1)

        pooled_features = self.project(pyramid)
        pooled_features = self.dropout(pooled_features)
        return pooled_features


# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTDeepLabV3 with MobileViT->MobileViTV2
class MobileViTV2DeepLabV3(nn.Module):
    """
    DeepLabv3 architecture: https://arxiv.org/abs/1706.05587
    """

    def __init__(self, config: MobileViTV2Config) -> None:
        super().__init__()
        self.aspp = MobileViTV2ASPP(config)

        self.dropout = nn.Dropout2d(config.classifier_dropout_prob)

        self.classifier = MobileViTV2ConvLayer(
            config,
            in_channels=config.aspp_out_channels,
            out_channels=config.num_labels,
            kernel_size=1,
            use_normalization=False,
            use_activation=False,
            bias=True,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        features = self.aspp(hidden_states[-1])
        features = self.dropout(features)
        features = self.classifier(features)
        return features


@add_start_docstrings(
    """
    MobileViTV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.
    """,
    MOBILEVITV2_START_DOCSTRING,
)
class MobileViTV2ForSemanticSegmentation(MobileViTV2PreTrainedModel):
    def __init__(self, config: MobileViTV2Config) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.mobilevitv2 = MobileViTV2Model(config, expand_output=False)
        self.segmentation_head = MobileViTV2DeepLabV3(config)

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

    @add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, SemanticSegmenterOutput]:
        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).

        Returns:

        Examples:

        ```python
        >>> import requests
        >>> import torch
        >>> from PIL import Image
        >>> from transformers import AutoImageProcessor, MobileViTV2ForSemanticSegmentation

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

        >>> image_processor = AutoImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
        >>> model = MobileViTV2ForSemanticSegmentation.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")

        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> with torch.no_grad():
        ...     outputs = model(**inputs)

        >>> # logits are of shape (batch_size, num_labels, height, width)
        >>> logits = outputs.logits
        ```"""
        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 labels is not None and self.config.num_labels == 1:
            raise ValueError("The number of labels should be greater than one")

        outputs = self.mobilevitv2(
            pixel_values,
            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.segmentation_head(encoder_hidden_states)

        loss = None
        if labels is not None:
            # upsample logits to the images' original size
            upsampled_logits = nn.functional.interpolate(
                logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
            )
            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,) + outputs[1:]
            else:
                output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SemanticSegmenterOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states if output_hidden_states else None,
            attentions=None,
        )