# coding=utf-8
# Copyright 2023 Meta AI 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
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"""PyTorch DINOv2 model."""

import collections.abc
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Union

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BackboneOutput,
    BaseModelOutput,
    BaseModelOutputWithPooling,
    ImageClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.models.dinov2.configuration_dinov2 import Dinov2Config
from transformers.pytorch_utils import (
    find_pruneable_heads_and_indices,
    prune_linear_layer,
)
from transformers.utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from transformers.utils.backbone_utils import BackboneMixin

logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "Dinov2Config"

# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/dinov2-base"
_EXPECTED_OUTPUT_SHAPE = [1, 257, 768]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/dinov2-base"


DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/dinov2-base",
    # See all DINOv2 models at https://huggingface.co/models?filter=dinov2
]


class Dinov2Embeddings(nn.Module):
    """
    Construct the CLS token, mask token, position and patch embeddings.
    """

    def __init__(self, config: Dinov2Config) -> None:
        super().__init__()

        self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
        # register as mask token as it's not used in optimization
        # to avoid the use of find_unused_parameters_true
        # self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size))
        self.register_buffer("mask_token", torch.zeros(1, config.hidden_size))
        self.patch_embeddings = Dinov2PatchEmbeddings(config)
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(
            torch.randn(1, num_patches + 1, config.hidden_size)
        )
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.config = config

    def interpolate_pos_encoding(
        self, embeddings: torch.Tensor, height: int, width: int
    ) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
        resolution images.

        Source:
        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
        """

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1
        if num_patches == num_positions and height == width:
            return self.position_embeddings
        class_pos_embed = self.position_embeddings[:, 0]
        patch_pos_embed = self.position_embeddings[:, 1:]
        dim = embeddings.shape[-1]
        height = height // self.config.patch_size
        width = width // self.config.patch_size
        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        height, width = height + 0.1, width + 0.1
        patch_pos_embed = patch_pos_embed.reshape(
            1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim
        )
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            scale_factor=(
                height / math.sqrt(num_positions),
                width / math.sqrt(num_positions),
            ),
            mode="bicubic",
            align_corners=False,
        )
        if (
            int(height) != patch_pos_embed.shape[-2]
            or int(width) != patch_pos_embed.shape[-1]
        ):
            raise ValueError(
                "Width or height does not match with the interpolated position embeddings"
            )
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)

    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        batch_size, _, height, width = pixel_values.shape
        patch_embeddings = self.patch_embeddings(pixel_values)
        embeddings = patch_embeddings

        if bool_masked_pos is not None:
            embeddings = torch.where(
                bool_masked_pos.unsqueeze(-1),
                self.mask_token.to(embeddings.dtype).unsqueeze(0),
                embeddings,
            )

        # add the [CLS] token to the embedded patch tokens
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)

        # add positional encoding to each token
        embeddings = embeddings + self.interpolate_pos_encoding(
            embeddings, height, width
        )

        embeddings = self.dropout(embeddings)

        return embeddings


class Dinov2PatchEmbeddings(nn.Module):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size

        image_size = (
            image_size
            if isinstance(image_size, collections.abc.Iterable)
            else (image_size, image_size)
        )
        patch_size = (
            patch_size
            if isinstance(patch_size, collections.abc.Iterable)
            else (patch_size, patch_size)
        )
        num_patches = (image_size[1] // patch_size[1]) * (
            image_size[0] // patch_size[0]
        )
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

        self.projection = nn.Conv2d(
            num_channels, hidden_size, kernel_size=patch_size, stride=patch_size
        )

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        """
        num_channels = pixel_values.shape[1]
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
                f" Expected {self.num_channels} but got {num_channels}."
            )
        """
        embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return embeddings


# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Dinov2
class Dinov2SelfAttention(nn.Module):
    def __init__(self, config: Dinov2Config) -> None:
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
            config, "embedding_size"
        ):
            raise ValueError(
                f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
                f"heads {config.num_attention_heads}."
            )

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

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

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

    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
        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,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        mixed_query_layer = self.query(hidden_states)

        if hasattr(F, "scaled_dot_product_attention"):
            assert head_mask is None and not output_attentions
            new_size = hidden_states.size()[:-1] + (
                self.num_attention_heads,
                self.attention_head_size,
            )
            key_layer = self.key(hidden_states).reshape(new_size).transpose(1, 2)
            value_layer = self.value(hidden_states).reshape(new_size).transpose(1, 2)
            query_layer = mixed_query_layer.reshape(new_size).transpose(1, 2)
            context_layer = F.scaled_dot_product_attention(
                query_layer,
                key_layer,
                value_layer,
                dropout_p=self.attention_probs_dropout_prob,
                is_causal=False,
            )
            context_layer = context_layer.transpose(1, 2).reshape(
                *hidden_states.size()[:-1], -1
            )
        else:
            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)

            # 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.vit.modeling_vit.ViTSelfOutput with ViT->Dinov2
class Dinov2SelfOutput(nn.Module):
    """
    The residual connection is defined in Dinov2Layer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: Dinov2Config) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

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

        return hidden_states


# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Dinov2
class Dinov2Attention(nn.Module):
    def __init__(self, config: Dinov2Config) -> None:
        super().__init__()
        self.attention = Dinov2SelfAttention(config)
        self.output = Dinov2SelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads: Set[int]) -> None:
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads,
            self.attention.num_attention_heads,
            self.attention.attention_head_size,
            self.pruned_heads,
        )

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

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        self_outputs = self.attention(hidden_states, 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


class Dinov2LayerScale(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        self.lambda1 = nn.Parameter(
            config.layerscale_value * torch.ones(config.hidden_size)
        )

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        return hidden_state * self.lambda1


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

    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
    argument.
    """
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (
        input.ndim - 1
    )  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(
        shape, dtype=input.dtype, device=input.device
    )
    random_tensor.floor_()  # binarize
    output = input.div(keep_prob) * random_tensor
    return output


# Copied from transformers.models.beit.modeling_beit.BeitDropPath
class Dinov2DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

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

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

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


class Dinov2MLP(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        in_features = out_features = config.hidden_size
        hidden_features = int(config.hidden_size * config.mlp_ratio)
        self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
        if isinstance(config.hidden_act, str):
            self.activation = ACT2FN[config.hidden_act]
        else:
            self.activation = config.hidden_act
        self.fc2 = nn.Linear(hidden_features, out_features, bias=True)

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        hidden_state = self.fc1(hidden_state)
        hidden_state = self.activation(hidden_state)
        hidden_state = self.fc2(hidden_state)
        return hidden_state


class Dinov2SwiGLUFFN(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        in_features = out_features = config.hidden_size
        hidden_features = int(config.hidden_size * config.mlp_ratio)
        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8

        self.weights_in = nn.Linear(in_features, 2 * hidden_features, bias=True)
        self.weights_out = nn.Linear(hidden_features, out_features, bias=True)

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        hidden_state = self.weights_in(hidden_state)
        x1, x2 = hidden_state.chunk(2, dim=-1)
        hidden = nn.functional.silu(x1) * x2
        return self.weights_out(hidden)


class Dinov2Layer(nn.Module):
    """This corresponds to the Block class in the original implementation."""

    def __init__(self, config: Dinov2Config) -> None:
        super().__init__()

        self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.norm1_modulation = None
        self.attention = Dinov2Attention(config)
        self.layer_scale1 = Dinov2LayerScale(config)
        self.drop_path1 = (
            Dinov2DropPath(config.drop_path_rate)
            if config.drop_path_rate > 0.0
            else nn.Identity()
        )

        self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.norm2_modulation = None

        if config.use_swiglu_ffn:
            self.mlp = Dinov2SwiGLUFFN(config)
        else:
            self.mlp = Dinov2MLP(config)
        self.layer_scale2 = Dinov2LayerScale(config)
        self.drop_path2 = (
            Dinov2DropPath(config.drop_path_rate)
            if config.drop_path_rate > 0.0
            else nn.Identity()
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        modulation_cond: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        hidden_states_norm = self.norm1(hidden_states)
        if self.norm1_modulation is not None:
            assert modulation_cond is not None
            hidden_states_norm = self.norm1_modulation(
                hidden_states_norm, modulation_cond
            )
        self_attention_outputs = self.attention(
            hidden_states_norm,  # in Dinov2, layernorm is applied before self-attention
            head_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]

        attention_output = self.layer_scale1(attention_output)
        outputs = self_attention_outputs[
            1:
        ]  # add self attentions if we output attention weights

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in Dinov2, layernorm is also applied after self-attention
        layer_output = self.norm2(hidden_states)
        if self.norm2_modulation is not None:
            assert modulation_cond is not None
            layer_output = self.norm2_modulation(layer_output, modulation_cond)
        layer_output = self.mlp(layer_output)
        layer_output = self.layer_scale2(layer_output)

        # second residual connection
        layer_output = layer_output + hidden_states

        outputs = (layer_output,) + outputs

        return outputs

    def register_ada_norm_modulation(self, norm1_mod: nn.Module, norm2_mod: nn.Module):
        self.norm1_modulation = norm1_mod
        self.norm2_modulation = norm2_mod


# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->Dinov2
class Dinov2Encoder(nn.Module):
    def __init__(self, config: Dinov2Config) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList(
            [Dinov2Layer(config) for _ in range(config.num_hidden_layers)]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        modulation_cond: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ) -> Union[tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        for i, layer_module in enumerate(self.layer):
            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

            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(layer_module),
                    hidden_states,
                    layer_head_mask,
                    modulation_cond,
                    use_reentrant=False,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states, layer_head_mask, modulation_cond, output_attentions
                )

            hidden_states = layer_outputs[0]

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

        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 Dinov2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = Dinov2Config
    base_model_prefix = "dinov2"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True

    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
            # `trunc_normal_cpu` not implemented in `half` issues
            module.weight.data = nn.init.trunc_normal_(
                module.weight.data.to(torch.float32),
                mean=0.0,
                std=self.config.initializer_range,
            ).to(module.weight.dtype)
            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)
        elif isinstance(module, Dinov2Embeddings):
            module.position_embeddings.data = nn.init.trunc_normal_(
                module.position_embeddings.data.to(torch.float32),
                mean=0.0,
                std=self.config.initializer_range,
            ).to(module.position_embeddings.dtype)

            module.cls_token.data = nn.init.trunc_normal_(
                module.cls_token.data.to(torch.float32),
                mean=0.0,
                std=self.config.initializer_range,
            ).to(module.cls_token.dtype)

    def _set_gradient_checkpointing(
        self, module: Dinov2Encoder, value: bool = False
    ) -> None:
        if isinstance(module, Dinov2Encoder):
            module.gradient_checkpointing = value


DINOV2_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 ([`Dinov2Config`]): 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.
"""

DINOV2_BASE_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
            [`BitImageProcessor.preprocess`] for details.

        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Only relevant for
            pre-training.

        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        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.
"""

DINOV2_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
            [`BitImageProcessor.preprocess`] for details.

        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        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.
"""


@dataclass
class CustomBaseModelOutputWithPooling(BaseModelOutputWithPooling):
    patch_embeddings: Optional[torch.FloatTensor] = None


@add_start_docstrings(
    "The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top.",
    DINOV2_START_DOCSTRING,
)
class Dinov2Model(Dinov2PreTrainedModel):
    def __init__(self, config: Dinov2Config):
        super().__init__(config)
        self.config = config

        self.embeddings = Dinov2Embeddings(config)
        self.encoder = Dinov2Encoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

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

    def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
        return self.embeddings.patch_embeddings

    def expand_input_channels(self, extra_input_channels: int) -> None:
        if extra_input_channels == 0:
            return
        conv_old = self.embeddings.patch_embeddings.projection
        conv_new = nn.Conv2d(
            self.config.num_channels + extra_input_channels,
            self.config.hidden_size,
            kernel_size=self.config.patch_size,
            stride=self.config.patch_size,
        ).to(self.device)
        with torch.no_grad():
            conv_new.weight[:, :3] = conv_old.weight
            conv_new.bias = conv_old.bias
        self.embeddings.patch_embeddings.projection = conv_new
        del conv_old

    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
        """
        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(DINOV2_BASE_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        bool_masked_pos: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        modulation_cond: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        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, self.config.num_hidden_layers)

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

        encoder_outputs = self.encoder(
            embedding_output,
            head_mask=head_mask,
            modulation_cond=modulation_cond,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)
        pooled_output = sequence_output[:, 0, :]

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

        return CustomBaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            patch_embeddings=embedding_output,
        )

    def set_gradient_checkpointing(self, value: bool = False) -> None:
        self._set_gradient_checkpointing(self.encoder, value)


@add_start_docstrings(
    """
    Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state
    of the [CLS] token) e.g. for ImageNet.
    """,
    DINOV2_START_DOCSTRING,
)
class Dinov2ForImageClassification(Dinov2PreTrainedModel):
    def __init__(self, config: Dinov2Config) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.dinov2 = Dinov2Model(config)

        # Classifier head
        self.classifier = (
            nn.Linear(config.hidden_size * 2, 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(DINOV2_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=ImageClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, ImageClassifierOutput]:
        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.dinov2(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]  # batch_size, sequence_length, hidden_size

        cls_token = sequence_output[:, 0]
        patch_tokens = sequence_output[:, 1:]

        linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1)

        logits = self.classifier(linear_input)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(logits.device)
            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 ImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    Dinov2 backbone, to be used with frameworks like DETR and MaskFormer.
    """,
    DINOV2_START_DOCSTRING,
)
class Dinov2Backbone(Dinov2PreTrainedModel, BackboneMixin):
    def __init__(self, config):
        super().__init__(config)
        super()._init_backbone(config)

        self.num_features = [
            config.hidden_size for _ in range(config.num_hidden_layers + 1)
        ]
        self.embeddings = Dinov2Embeddings(config)
        self.encoder = Dinov2Encoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

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

    def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
        return self.embeddings.patch_embeddings

    @add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values: torch.Tensor,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> BackboneOutput:
        """
        Returns:

        Examples:

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

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

        >>> processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
        >>> model = AutoBackbone.from_pretrained(
        ...     "facebook/dinov2-base", out_features=["stage2", "stage5", "stage8", "stage11"]
        ... )

        >>> inputs = processor(image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> feature_maps = outputs.feature_maps
        >>> list(feature_maps[-1].shape)
        [1, 768, 16, 16]
        ```"""
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )

        embedding_output = self.embeddings(pixel_values)

        outputs = self.encoder(
            embedding_output,
            output_hidden_states=True,
            output_attentions=output_attentions,
            return_dict=return_dict,
        )

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

        feature_maps = ()
        for stage, hidden_state in zip(self.stage_names, hidden_states):
            if stage in self.out_features:
                if self.config.apply_layernorm:
                    hidden_state = self.layernorm(hidden_state)
                if self.config.reshape_hidden_states:
                    batch_size, _, height, width = pixel_values.shape
                    patch_size = self.config.patch_size
                    hidden_state = hidden_state[:, 1:, :].reshape(
                        batch_size, width // patch_size, height // patch_size, -1
                    )
                    hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
                feature_maps += (hidden_state,)

        if not return_dict:
            if output_hidden_states:
                output = (feature_maps,) + outputs[1:]
            else:
                output = (feature_maps,) + outputs[2:]
            return output

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


class CustomPatchEmbeddings(nn.Module):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(
        self, image_size: int, patch_size: int, num_channels: int, hidden_size: int
    ):
        super().__init__()

        image_size = (
            image_size
            if isinstance(image_size, collections.abc.Iterable)
            else (image_size, image_size)
        )
        patch_size = (
            patch_size
            if isinstance(patch_size, collections.abc.Iterable)
            else (patch_size, patch_size)
        )
        num_patches = (image_size[1] // patch_size[1]) * (
            image_size[0] // patch_size[0]
        )
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

        self.projection = nn.Conv2d(
            num_channels, hidden_size, kernel_size=patch_size, stride=patch_size
        )

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        num_channels = pixel_values.shape[1]
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
                f" Expected {self.num_channels} but got {num_channels}."
            )
        embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return embeddings


class CustomEmbeddings(nn.Module):
    """
    Construct the CLS token, mask token, position and patch embeddings.
    """

    def __init__(
        self, image_size: int, patch_size: int, num_channels: int, hidden_size: int
    ) -> None:
        super().__init__()

        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.hidden_size = hidden_size

        self.cls_token = nn.Parameter(torch.randn(1, 1, self.hidden_size))

        self.patch_embeddings = CustomPatchEmbeddings(
            image_size, patch_size, num_channels, hidden_size
        )
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(
            torch.randn(1, num_patches + 1, self.hidden_size)
        )

    def interpolate_pos_encoding(
        self, embeddings: torch.Tensor, height: int, width: int
    ) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
        resolution images.

        Source:
        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
        """

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1
        if num_patches == num_positions and height == width:
            return self.position_embeddings
        class_pos_embed = self.position_embeddings[:, 0]
        patch_pos_embed = self.position_embeddings[:, 1:]
        dim = embeddings.shape[-1]
        height = height // self.patch_size
        width = width // self.patch_size
        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        height, width = height + 0.1, width + 0.1
        patch_pos_embed = patch_pos_embed.reshape(
            1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim
        )
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            scale_factor=(
                height / math.sqrt(num_positions),
                width / math.sqrt(num_positions),
            ),
            mode="bicubic",
            align_corners=False,
        )
        if (
            int(height) != patch_pos_embed.shape[-2]
            or int(width) != patch_pos_embed.shape[-1]
        ):
            raise ValueError(
                "Width or height does not match with the interpolated position embeddings"
            )
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)

    def forward(
        self,
        pixel_values: torch.Tensor,
    ) -> torch.Tensor:
        batch_size, _, height, width = pixel_values.shape
        patch_embeddings = self.patch_embeddings(pixel_values)
        embeddings = patch_embeddings

        # add the [CLS] token to the embedded patch tokens
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)

        # add positional encoding to each token
        embeddings = embeddings + self.interpolate_pos_encoding(
            embeddings, height, width
        )

        return embeddings
