from typing import Optional, Tuple, Union

import torch
import torch.nn as nn


class PatchDropout(nn.Module):
    """
    https://arxiv.org/abs/2212.00794
    """
    return_indices: torch.jit.Final[bool]

    def __init__(
            self,
            prob: float = 0.5,
            num_prefix_tokens: int = 1,
            ordered: bool = False,
            return_indices: bool = False,
    ):
        super().__init__()
        assert 0 <= prob < 1.
        self.prob = prob
        self.num_prefix_tokens = num_prefix_tokens  # exclude CLS token (or other prefix tokens)
        self.ordered = ordered
        self.return_indices = return_indices

    def forward(self, x) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
        if not self.training or self.prob == 0.:
            if self.return_indices:
                return x, None
            return x

        if self.num_prefix_tokens:
            prefix_tokens, x = x[:, :self.num_prefix_tokens], x[:, self.num_prefix_tokens:]
        else:
            prefix_tokens = None

        B = x.shape[0]
        L = x.shape[1]
        num_keep = max(1, int(L * (1. - self.prob)))
        keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1)[:, :num_keep]
        if self.ordered:
            # NOTE does not need to maintain patch order in typical transformer use,
            # but possibly useful for debug / visualization
            keep_indices = keep_indices.sort(dim=-1)[0]
        x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.shape[2:]))

        if prefix_tokens is not None:
            x = torch.cat((prefix_tokens, x), dim=1)

        if self.return_indices:
            return x, keep_indices
        return x

9861e85d11d4a6952037c6df5db6dee70c1c505d
<mask token>


class PatchDropout(nn.Module):
    <mask token>
    return_indices: torch.jit.Final[bool]

    def __init__(self, prob: float=0.5, num_prefix_tokens: int=1, ordered:
        bool=False, return_indices: bool=False):
        super().__init__()
        assert 0 <= prob < 1.0
        self.prob = prob
        self.num_prefix_tokens = num_prefix_tokens
        self.ordered = ordered
        self.return_indices = return_indices
    <mask token>

<mask token>


class PatchDropout(nn.Module):
    <mask token>
    return_indices: torch.jit.Final[bool]

    def __init__(self, prob: float=0.5, num_prefix_tokens: int=1, ordered:
        bool=False, return_indices: bool=False):
        super().__init__()
        assert 0 <= prob < 1.0
        self.prob = prob
        self.num_prefix_tokens = num_prefix_tokens
        self.ordered = ordered
        self.return_indices = return_indices

    def forward(self, x) ->Union[torch.Tensor, Tuple[torch.Tensor, Optional
        [torch.Tensor]]]:
        if not self.training or self.prob == 0.0:
            if self.return_indices:
                return x, None
            return x
        if self.num_prefix_tokens:
            prefix_tokens, x = x[:, :self.num_prefix_tokens], x[:, self.
                num_prefix_tokens:]
        else:
            prefix_tokens = None
        B = x.shape[0]
        L = x.shape[1]
        num_keep = max(1, int(L * (1.0 - self.prob)))
        keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1
            )[:, :num_keep]
        if self.ordered:
            keep_indices = keep_indices.sort(dim=-1)[0]
        x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.
            shape[2:]))
        if prefix_tokens is not None:
            x = torch.cat((prefix_tokens, x), dim=1)
        if self.return_indices:
            return x, keep_indices
        return x

<mask token>


class PatchDropout(nn.Module):
    """
    https://arxiv.org/abs/2212.00794
    """
    return_indices: torch.jit.Final[bool]

    def __init__(self, prob: float=0.5, num_prefix_tokens: int=1, ordered:
        bool=False, return_indices: bool=False):
        super().__init__()
        assert 0 <= prob < 1.0
        self.prob = prob
        self.num_prefix_tokens = num_prefix_tokens
        self.ordered = ordered
        self.return_indices = return_indices

    def forward(self, x) ->Union[torch.Tensor, Tuple[torch.Tensor, Optional
        [torch.Tensor]]]:
        if not self.training or self.prob == 0.0:
            if self.return_indices:
                return x, None
            return x
        if self.num_prefix_tokens:
            prefix_tokens, x = x[:, :self.num_prefix_tokens], x[:, self.
                num_prefix_tokens:]
        else:
            prefix_tokens = None
        B = x.shape[0]
        L = x.shape[1]
        num_keep = max(1, int(L * (1.0 - self.prob)))
        keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1
            )[:, :num_keep]
        if self.ordered:
            keep_indices = keep_indices.sort(dim=-1)[0]
        x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.
            shape[2:]))
        if prefix_tokens is not None:
            x = torch.cat((prefix_tokens, x), dim=1)
        if self.return_indices:
            return x, keep_indices
        return x

from typing import Optional, Tuple, Union
import torch
import torch.nn as nn


class PatchDropout(nn.Module):
    """
    https://arxiv.org/abs/2212.00794
    """
    return_indices: torch.jit.Final[bool]

    def __init__(self, prob: float=0.5, num_prefix_tokens: int=1, ordered:
        bool=False, return_indices: bool=False):
        super().__init__()
        assert 0 <= prob < 1.0
        self.prob = prob
        self.num_prefix_tokens = num_prefix_tokens
        self.ordered = ordered
        self.return_indices = return_indices

    def forward(self, x) ->Union[torch.Tensor, Tuple[torch.Tensor, Optional
        [torch.Tensor]]]:
        if not self.training or self.prob == 0.0:
            if self.return_indices:
                return x, None
            return x
        if self.num_prefix_tokens:
            prefix_tokens, x = x[:, :self.num_prefix_tokens], x[:, self.
                num_prefix_tokens:]
        else:
            prefix_tokens = None
        B = x.shape[0]
        L = x.shape[1]
        num_keep = max(1, int(L * (1.0 - self.prob)))
        keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1
            )[:, :num_keep]
        if self.ordered:
            keep_indices = keep_indices.sort(dim=-1)[0]
        x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.
            shape[2:]))
        if prefix_tokens is not None:
            x = torch.cat((prefix_tokens, x), dim=1)
        if self.return_indices:
            return x, keep_indices
        return x

from typing import Optional, Tuple, Union

import torch
import torch.nn as nn


class PatchDropout(nn.Module):
    """
    https://arxiv.org/abs/2212.00794
    """
    return_indices: torch.jit.Final[bool]

    def __init__(
            self,
            prob: float = 0.5,
            num_prefix_tokens: int = 1,
            ordered: bool = False,
            return_indices: bool = False,
    ):
        super().__init__()
        assert 0 <= prob < 1.
        self.prob = prob
        self.num_prefix_tokens = num_prefix_tokens  # exclude CLS token (or other prefix tokens)
        self.ordered = ordered
        self.return_indices = return_indices

    def forward(self, x) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
        if not self.training or self.prob == 0.:
            if self.return_indices:
                return x, None
            return x

        if self.num_prefix_tokens:
            prefix_tokens, x = x[:, :self.num_prefix_tokens], x[:, self.num_prefix_tokens:]
        else:
            prefix_tokens = None

        B = x.shape[0]
        L = x.shape[1]
        num_keep = max(1, int(L * (1. - self.prob)))
        keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1)[:, :num_keep]
        if self.ordered:
            # NOTE does not need to maintain patch order in typical transformer use,
            # but possibly useful for debug / visualization
            keep_indices = keep_indices.sort(dim=-1)[0]
        x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.shape[2:]))

        if prefix_tokens is not None:
            x = torch.cat((prefix_tokens, x), dim=1)

        if self.return_indices:
            return x, keep_indices
        return x
