# import logging
import torch
from torch import nn
import numpy as np
import collections.abc
import math
from copy import deepcopy

# logger = logging.get_logger(__name__)
ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu}


def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False): #embed_dim=768, grid_size=14
    """
    Create 2D sin/cos positional embeddings.
    Args:
        embed_dim (`int`):
            Embedding dimension.
        grid_size (`int`):
            The grid height and width.
        add_cls_token (`bool`, *optional*, defaults to `False`):
            Whether or not to add a classification (CLS) token.
    Returns:
        (`torch.FloatTensor` of shape (grid_size*grid_size, embed_dim) or (1+grid_size*grid_size, embed_dim): the
        position embeddings (with or without classification token)
    """
    grid_h = np.arange(grid_size, dtype=np.float32)
    grid_w = np.arange(grid_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)  #[2, grid_size, grid_size]

    grid = grid.reshape([2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) #[num_tokens, hidden_size]

    if add_cls_token:
        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
    return pos_embed

def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be even")

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb

def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
    """
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be even")
    
    omega = np.arange(embed_dim // 2, dtype=float)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,) #(192,)

    pos = pos.reshape(-1)  # (M,) 196
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb



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

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

        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.patch_embeddings = ViTMAEPatchEmbeddings(config)
        self.num_patches = self.patch_embeddings.num_patches
        # fixed sin-cos embedding
        self.position_embeddings = nn.Parameter(
            torch.zeros(1, self.num_patches + 1, config.hidden_size), requires_grad=False
        )
        self.config = config
        self.initialize_weights()

    def initialize_weights(self):
        # initialize (and freeze) position embeddings by sin-cos embedding
        pos_embed = get_2d_sincos_pos_embed(
            self.position_embeddings.shape[-1], int(self.patch_embeddings.num_patches**0.5), add_cls_token=True
        )
        self.position_embeddings.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        # initialize patch_embeddings like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embeddings.projection.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=self.config.initializer_range)

    def random_masking(self, sequence, noise=None, random_seed=None, preset_mask=False):
        """
        Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random
        noise.
        Args:
            sequence (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`)
            noise (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) which is
                mainly used for testing purposes to control randomness and maintain the reproducibility
        """
        batch_size, seq_length, dim = sequence.shape # seq_length is #tokens
        len_keep = int(seq_length * (1 - self.config.mask_ratio))

        if noise is None:
            if random_seed is not None:
                torch.manual_seed(random_seed)
            noise = torch.rand(batch_size, seq_length, device=sequence.device)  # noise in [0, 1]

        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)


        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep] #[N, #tokens*ratio]
        sequence_unmasked = torch.gather(sequence, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, dim)) #index: [7,49,768]

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([batch_size, seq_length], device=sequence.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        if preset_mask:
            mask = 1-mask
        
        return sequence_unmasked, mask, ids_restore
        # mask: [N,  #tokens]
        # ids_restore: [N,  #tokens]
        # sequence_unmasked: [N, #tokens*0.75, hidden_size]

    def salient_masking(self, sequence, noise=None, random_seed=None, preset_mask=False):
        batch_size, seq_length, dim = sequence.shape # seq_length is #tokens
        row_indices, col_indices = np.where(preset_mask == 0)
        row_indices_1, col_indices_1 = np.where(preset_mask == 1)
        mask = torch.from_numpy(preset_mask).to(sequence.device)
        len_keep = int(seq_length * (1 - self.config.mask_ratio))
        len_remove = int(seq_length * self.config.mask_ratio)
    
        positions = np.zeros((batch_size, len_keep), dtype=int)
        tracker = 0
        for i in range(batch_size):
            positions[row_indices[tracker]] = col_indices[tracker:tracker+len_keep]
            tracker += len_keep

        positions_1 = np.zeros((batch_size, len_remove), dtype=int)
        tracker = 0
        for i in range(batch_size):
            positions_1[row_indices_1[tracker]] = col_indices_1[tracker:tracker+len_remove]
            tracker += len_remove
    
        sequence_unmasked = sequence[torch.arange(batch_size).unsqueeze(1), positions, :]
        ids_shuffle = np.concatenate((positions, positions_1), axis=1)
        ids_shuffle = torch.from_numpy(ids_shuffle)
    
        ids_restore = torch.argsort(ids_shuffle, dim=1)
        ids_restore = ids_restore.to(sequence.device)
    
        return sequence_unmasked, mask, ids_restore


    def forward(self, pixel_values, noise=None, random_seed=None, preset_mask=False):
        batch_size, num_channels, height, width = pixel_values.shape
        embeddings = self.patch_embeddings(pixel_values)

        # add position embeddings w/o cls token
        embeddings = embeddings + self.position_embeddings[:, 1:, :] #[N, #tokens, hidden_size]
        # masking: length -> length * config.mask_ratio
        if preset_mask is False:
            embeddings, mask, ids_restore = self.random_masking(embeddings, noise, random_seed, preset_mask=preset_mask)
        else:
            embeddings, mask, ids_restore = self.salient_masking(embeddings, noise, random_seed, preset_mask=preset_mask)

        # append cls token
        cls_token = self.cls_token + self.position_embeddings[:, :1, :] #[1,1,hidden_size]
        cls_tokens = cls_token.expand(embeddings.shape[0], -1, -1)

        embeddings = torch.cat((cls_tokens, embeddings), dim=1) #[N, #tokens*0.75+1, hidden_size]

        return embeddings, mask, ids_restore
    # mask: [N,  #tokens]
    # ids_restore: [N,  #tokens]

class ViTMAEPatchEmbeddings(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):
        batch_size, num_channels, height, width = pixel_values.shape
        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."
            )
        if height != self.image_size[0] or width != self.image_size[1]:
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
            )
        x = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return x

# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention ViT->ViTMAE
class ViTMAESelfAttention(nn.Module):
    def __init__(self, config) -> 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.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 = None, output_attentions = False):
        mixed_query_layer = self.query(hidden_states)

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

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)

        # 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) #[N, #heads, #tokens, #tokens]

        # 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->ViTMAE
class ViTMAESelfOutput(nn.Module):
    """
    The residual connection is defined in ViTMAELayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config):
        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, input_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->ViTMAE
class ViTMAEAttention(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        self.attention = ViTMAESelfAttention(config)
        self.output = ViTMAESelfOutput(config)
        self.pruned_heads = set()

    def forward(self, hidden_states, head_mask, output_attentions):
        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
    #outputs[0]: [N, #tokens, hidden_size]
    #outputs[1]: [N, #heads, #tokens, #tokens]


# Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->ViTMAE
class ViTMAEIntermediate(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

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

        return hidden_states

# Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->ViTMAE
class ViTMAEOutput(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        self.dense = nn.Linear(config.intermediate_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)

        hidden_states = hidden_states + input_tensor

        return hidden_states

# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->ViTMAE
class ViTMAELayer(nn.Module):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = ViTMAEAttention(config)
        self.intermediate = ViTMAEIntermediate(config)
        self.output = ViTMAEOutput(config)
        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states, head_mask = None, output_attentions = False):
        self_attention_outputs = self.attention(
            self.layernorm_before(hidden_states),  # in ViTMAE, layernorm is applied before self-attention
            head_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in ViTMAE, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(hidden_states)
        layer_output = self.intermediate(layer_output)

        # second residual connection is done here
        layer_output = self.output(layer_output, hidden_states)

        outputs = (layer_output,) + outputs

        return outputs
    #outputs[0]: [N, #tokens, hidden_size]
    #outputs[1]: [N, #heads, #tokens, #tokens]

# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTMAE
class ViTMAEEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([ViTMAELayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

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

        for i, layer_module in enumerate(self.layer):
            layer_head_mask = head_mask[i] if head_mask is not None else None ##

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            else:
                layer_outputs = layer_module(hidden_states, layer_head_mask, 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,
        # )
        return (hidden_states, all_hidden_states, all_self_attentions)
    # hidden_states: [N, #tokens, #hidden_size]
    # all_self_attentions: [#layers, N, #heads, #tokens, #tokens]


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

        self.embeddings = ViTMAEEmbeddings(config)
        self.encoder = ViTMAEEncoder(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):
        return self.embeddings.patch_embeddings

    def forward(self, pixel_values, noise = None, random_seed=None, head_mask = None, output_attentions = None, output_hidden_states = None, return_dict = None, preset_mask=False):
        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]
        if head_mask is not None:
            head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output, mask, ids_restore = self.embeddings(pixel_values, noise=noise, random_seed=random_seed, preset_mask=preset_mask)
        encoder_outputs = self.encoder(embedding_output, head_mask=head_mask, 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)

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

        return (sequence_output, mask, ids_restore, encoder_outputs[1], encoder_outputs[2])
    #sequence_output: [N, #tokens*0.75, hidden_size]
    #mask: [N, #tokens]  0/1
    #ids_restore: [N, #tokens]
    #encoder_outputs[1]: all_hidden_states
    #encoder_outputs[2]: all_self_attentions [#layers, N, #heads, #tokens*0.75, #tokens*0.75]

class ViTMAEDecoder_custom(nn.Module):
    def __init__(self, config, num_patches):
        super().__init__()
        self.decoder_embed = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=True)
        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size))
        self.decoder_pos_embed = nn.Parameter(
            torch.zeros(1, num_patches + 1, config.decoder_hidden_size), requires_grad=False
        )  # fixed sin-cos embedding

        decoder_config = deepcopy(config)
        decoder_config.hidden_size = config.decoder_hidden_size
        decoder_config.num_hidden_layers = config.decoder_num_hidden_layers
        decoder_config.num_attention_heads = config.decoder_num_attention_heads
        decoder_config.intermediate_size = config.decoder_intermediate_size
        self.decoder_layers = nn.ModuleList(
            [ViTMAELayer(decoder_config) for _ in range(config.decoder_num_hidden_layers)]
        )

        self.decoder_norm = nn.LayerNorm(config.decoder_hidden_size, eps=config.layer_norm_eps)
        self.decoder_pred = nn.Linear(
            config.decoder_hidden_size, config.patch_size**2 * config.num_channels, bias=True
        )  # encoder to decoder
        self.gradient_checkpointing = False
        self.config = config
        self.initialize_weights(num_patches)

    def initialize_weights(self, num_patches):
        # initialize (and freeze) position embeddings by sin-cos embedding
        decoder_pos_embed = get_2d_sincos_pos_embed(
            self.decoder_pos_embed.shape[-1], int(num_patches**0.5), add_cls_token=True
        )
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.mask_token, std=self.config.initializer_range)

    def forward(self, hidden_states, ids_restore, output_attentions=False, output_hidden_states=False, return_dict=True):
        # embed tokens
        x = self.decoder_embed(hidden_states) #[N, #tokens*0.75, decoder_hidden_size]

        # append mask tokens to sequence
        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1) #[N, #masked_tokens, decoder_hidden_size]
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token #[N, #tokens, decoer_hidden_size]
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token
        
        # add pos embed
        hidden_states = x + self.decoder_pos_embed

        # apply Transformer layers (blocks)
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.decoder_layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            else:
                layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=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,)

        hidden_states = self.decoder_norm(hidden_states)

        # predictor projection
        logits = self.decoder_pred(hidden_states)

        # remove cls token
        logits = logits[:, 1:, :]

        if not return_dict:
            return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None)
        return (logits, all_hidden_states, all_self_attentions)


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

        self.vit = ViTMAEModel_custom(config)
        self.decoder = ViTMAEDecoder_custom(config, num_patches=self.vit.embeddings.num_patches)

    def get_input_embeddings(self):
        return self.vit.embeddings.patch_embeddings

    def patchify(self, pixel_values):
        """
        Args:
            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
                Pixel values.

        Returns:
            `torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
                Patchified pixel values.
        """
        patch_size, num_channels = self.config.patch_size, self.config.num_channels
        # sanity checks
        if (pixel_values.shape[2] != pixel_values.shape[3]) or (pixel_values.shape[2] % patch_size != 0):
            raise ValueError("Make sure the pixel values have a squared size that is divisible by the patch size")
        if pixel_values.shape[1] != num_channels:
            raise ValueError(
                "Make sure the number of channels of the pixel values is equal to the one set in the configuration"
            )

        # patchify
        batch_size = pixel_values.shape[0]
        num_patches_one_direction = pixel_values.shape[2] // patch_size
        patchified_pixel_values = pixel_values.reshape(
            batch_size, num_channels, num_patches_one_direction, patch_size, num_patches_one_direction, patch_size
        )
        patchified_pixel_values = torch.einsum("nchpwq->nhwpqc", patchified_pixel_values)
        patchified_pixel_values = patchified_pixel_values.reshape(
            batch_size, num_patches_one_direction * num_patches_one_direction, patch_size**2 * num_channels
        )
        return patchified_pixel_values

    def unpatchify(self, patchified_pixel_values):
        """
        Args:
            patchified_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
                Patchified pixel values.

        Returns:
            `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`:
                Pixel values.
        """
        patch_size, num_channels = self.config.patch_size, self.config.num_channels
        num_patches_one_direction = int(patchified_pixel_values.shape[1] ** 0.5)
        # sanity check
        if num_patches_one_direction**2 != patchified_pixel_values.shape[1]:
            raise ValueError("Make sure that the number of patches can be squared")

        # unpatchify
        batch_size = patchified_pixel_values.shape[0]
        patchified_pixel_values = patchified_pixel_values.reshape(
            batch_size,
            num_patches_one_direction,
            num_patches_one_direction,
            patch_size,
            patch_size,
            num_channels,
        )
        patchified_pixel_values = torch.einsum("nhwpqc->nchpwq", patchified_pixel_values)
        pixel_values = patchified_pixel_values.reshape(
            batch_size,
            num_channels,
            num_patches_one_direction * patch_size,
            num_patches_one_direction * patch_size,
        )
        return pixel_values

    def forward_loss(self, pixel_values, pred, mask):
        """
        Args:
            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
                Pixel values.
            pred (`torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
                Predicted pixel values.
            mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
                Tensor indicating which patches are masked (1) and which are not (0).

        Returns:
            `torch.FloatTensor`: Pixel reconstruction loss.
        """
        target = self.patchify(pixel_values)
        if self.config.norm_pix_loss: #False
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.0e-6) ** 0.5

        loss = (pred - target) ** 2
        loss = loss.mean(dim=-1)  # [N, L], mean loss per patch

        loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches
        return loss


    def forward(self, pixel_values, noise = None, random_seed = None,
        head_mask = None, output_attentions = False,
        output_hidden_states = False, return_dict = True, preset_mask=False):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.vit(
            pixel_values,
            noise=noise,
            random_seed = random_seed,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            preset_mask=preset_mask
        )

        # latent = outputs.last_hidden_state
        # ids_restore = outputs.ids_restore
        # mask = outputs.mask
        latent = outputs[0]
        mask = outputs[1]
        ids_restore = outputs[2]

        decoder_outputs = self.decoder(latent, ids_restore)
        # logits = decoder_outputs.logits  # shape (batch_size, num_patches, patch_size*patch_size*num_channels)
        logits = decoder_outputs[0]

        loss = self.forward_loss(pixel_values, logits, mask)

        return (loss, logits, mask, ids_restore, outputs[1], outputs[2]) #wrong
    #logits.shape: [N, #tokens, hidden-size]

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

        self.vit = ViTMAEModel_custom(config)
        self.classifier = nn.Linear(768, 200) 

    def forward(self, pixel_values, noise = None, random_seed = None,
        head_mask = None, output_attentions = False,
        output_hidden_states = False, return_dict = True, preset_mask=False):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.vit(
            pixel_values,
            noise=noise,
            random_seed = random_seed,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            preset_mask=preset_mask
        )

        latent = outputs[0][:,0]
        decoder_outputs = self.classifier(latent)
        return decoder_outputs