from dataclasses import dataclass
from typing import Optional
import collections.abc
import torch.nn as nn
import torch

@dataclass
class DinoConfig():

    hidden_size: int = 1024
    use_mask_token: bool = True
    patch_size: int = 14
    image_size: int = 518
    num_channels: int = 3
    num_attention_heads: int = 16
    attention_probs_dropout_prob: float = 0.0
    hidden_dropout_prob: float = 0.0
    mlp_ratio: int = 4
    num_hidden_layers: int = 24
    layer_norm_eps: float = 1e-6
    qkv_bias: bool = True
    layerscale_value: float =  1.0
    drop_path_rate: float =  0.0

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

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

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

        if config.use_mask_token:
            self.mask_token = nn.Parameter(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.patch_size = config.patch_size
        self.use_mask_token = config.use_mask_token

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:

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

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

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

        dim = embeddings.shape[-1]

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

        sqrt_num_positions = int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)

        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        target_dtype = patch_pos_embed.dtype

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed.to(torch.float32),
            size=(new_height, new_width),
            mode="bicubic",
            align_corners=False,
        ).to(dtype=target_dtype)

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

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

    def forward(self, pixel_values: torch.Tensor, bool_masked_pos: torch.Tensor = None) -> torch.Tensor:

        batch_size, _, height, width = pixel_values.shape
        target_dtype = self.patch_embeddings.projection.weight.dtype
        embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))

        if bool_masked_pos is not None and self.use_mask_token:
            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 pixel_values.shape[1] != 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}."
            )
        return self.projection(pixel_values).flatten(2).transpose(1, 2)
    
def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    # Take the dot product between "query" and "key" to get the raw attention scores.
    attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling

    # Normalize the attention scores to probabilities.
    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)

    # This is actually dropping out entire tokens to attend to, which might
    # seem a bit unusual, but is taken from the original Transformer paper.
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)

    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights



# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Dinov2
class Dinov2SelfAttention(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()

        self.config = config
        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.dropout_prob = config.attention_probs_dropout_prob
        self.scaling = self.attention_head_size**-0.5

        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)

    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: torch.Tensor = None
    ):
        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(self.query(hidden_states))

        context_layer, _ = eager_attention_forward(
            self,
            query = query_layer,
            key = key_layer,
            value = value_layer,
            scaling = self.scaling,
            dropout = 0.0 if not self.training else self.dropout_prob,
        )

        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.reshape(new_context_layer_shape)

        outputs = (context_layer,)

        return outputs

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: DinoConfig) -> 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) -> 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: DinoConfig) -> None:
        super().__init__()
        self.attention = Dinov2SelfAttention(config)
        self.output = Dinov2SelfOutput(config)
        self.pruned_heads = set()

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: torch.Tensor = None,
    ):
        self_outputs = self.attention(hidden_states, head_mask)

        attention_output = self.output(self_outputs[0])

        outputs = (attention_output,)
        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:

    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: 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)


class Dinov2MLP(nn.Module):
    def __init__(self, config):
        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)

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

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

        self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attention = Dinov2Attention(config)
        self.layer_scale1 = Dinov2LayerScale(config)
        self.drop_path = 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.mlp = Dinov2MLP(config)
        self.layer_scale2 = Dinov2LayerScale(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: torch.Tensor = None,
    ):
        self_attention_outputs = self.attention(
            self.norm1(hidden_states),  # in Dinov2, layernorm is applied before self-attention
            head_mask,
        )
        attention_output = self_attention_outputs[0]

        attention_output = self.layer_scale1(attention_output)

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

        # in Dinov2, layernorm is also applied after self-attention
        layer_output = self.norm2(hidden_states)
        layer_output = self.mlp(layer_output)
        layer_output = self.layer_scale2(layer_output)

        # second residual connection
        layer_output = self.drop_path(layer_output) + hidden_states

        outputs = (layer_output,)

        return outputs
    
class Dinov2Encoder(nn.Module):
    def __init__(self, config: DinoConfig) -> None:
        super().__init__()
        self.layer = nn.ModuleList([Dinov2Layer(config) for _ in range(config.num_hidden_layers)])

    def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None):

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

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

            layer_outputs = layer_module(hidden_states, layer_head_mask)

            hidden_states = layer_outputs[0]

        return hidden_states


class Dinov2Model(nn.Module):
    def __init__(self, config: DinoConfig):
        super().__init__()
        self.config = config

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

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

    def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
        return self.embeddings.patch_embeddings
    
    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
    ):

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

        encoder_outputs = self.encoder(
            embedding_output,
            head_mask = head_mask,
        )
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)

        return sequence_output