from torch import Tensor, nn
from torch.nn import LayerNorm

from transformer.attention import MultiHeadedAttention
from transformer.utils import PositionwiseFeedForward, SublayerConnection, clones


class EncoderLayer(nn.Module):
    self_attn: MultiHeadedAttention
    feed_forward: PositionwiseFeedForward
    sublayer: nn.ModuleList
    size: int

    def __init__(
        self, size: int, self_attn: MultiHeadedAttention, feed_forward: PositionwiseFeedForward, dropout: float
    ) -> None:
        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x: Tensor, mask: Tensor) -> Tensor:  # TODO mask type
        x = self.sublayer[0](x, lambda t: self.self_attn(t, t, t, mask))
        return self.sublayer[1](x, self.feed_forward)


class Encoder(nn.Module):
    layers: nn.ModuleList
    norm: LayerNorm

    def __init__(self, layer: EncoderLayer, n: int) -> None:
        super().__init__()
        self.layers = clones(layer, n)
        self.norm = LayerNorm(layer.size)

    def forward(self, x: Tensor, mask: Tensor) -> Tensor:  # TODO mask type
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

4270b6c9dd719061f4c4e0b971951fc596704f0d
<mask token>


class EncoderLayer(nn.Module):
    self_attn: MultiHeadedAttention
    feed_forward: PositionwiseFeedForward
    sublayer: nn.ModuleList
    size: int
    <mask token>
    <mask token>


class Encoder(nn.Module):
    layers: nn.ModuleList
    norm: LayerNorm

    def __init__(self, layer: EncoderLayer, n: int) ->None:
        super().__init__()
        self.layers = clones(layer, n)
        self.norm = LayerNorm(layer.size)

    def forward(self, x: Tensor, mask: Tensor) ->Tensor:
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

<mask token>


class EncoderLayer(nn.Module):
    self_attn: MultiHeadedAttention
    feed_forward: PositionwiseFeedForward
    sublayer: nn.ModuleList
    size: int
    <mask token>

    def forward(self, x: Tensor, mask: Tensor) ->Tensor:
        x = self.sublayer[0](x, lambda t: self.self_attn(t, t, t, mask))
        return self.sublayer[1](x, self.feed_forward)


class Encoder(nn.Module):
    layers: nn.ModuleList
    norm: LayerNorm

    def __init__(self, layer: EncoderLayer, n: int) ->None:
        super().__init__()
        self.layers = clones(layer, n)
        self.norm = LayerNorm(layer.size)

    def forward(self, x: Tensor, mask: Tensor) ->Tensor:
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

<mask token>


class EncoderLayer(nn.Module):
    self_attn: MultiHeadedAttention
    feed_forward: PositionwiseFeedForward
    sublayer: nn.ModuleList
    size: int

    def __init__(self, size: int, self_attn: MultiHeadedAttention,
        feed_forward: PositionwiseFeedForward, dropout: float) ->None:
        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x: Tensor, mask: Tensor) ->Tensor:
        x = self.sublayer[0](x, lambda t: self.self_attn(t, t, t, mask))
        return self.sublayer[1](x, self.feed_forward)


class Encoder(nn.Module):
    layers: nn.ModuleList
    norm: LayerNorm

    def __init__(self, layer: EncoderLayer, n: int) ->None:
        super().__init__()
        self.layers = clones(layer, n)
        self.norm = LayerNorm(layer.size)

    def forward(self, x: Tensor, mask: Tensor) ->Tensor:
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

from torch import Tensor, nn
from torch.nn import LayerNorm
from transformer.attention import MultiHeadedAttention
from transformer.utils import PositionwiseFeedForward, SublayerConnection, clones


class EncoderLayer(nn.Module):
    self_attn: MultiHeadedAttention
    feed_forward: PositionwiseFeedForward
    sublayer: nn.ModuleList
    size: int

    def __init__(self, size: int, self_attn: MultiHeadedAttention,
        feed_forward: PositionwiseFeedForward, dropout: float) ->None:
        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x: Tensor, mask: Tensor) ->Tensor:
        x = self.sublayer[0](x, lambda t: self.self_attn(t, t, t, mask))
        return self.sublayer[1](x, self.feed_forward)


class Encoder(nn.Module):
    layers: nn.ModuleList
    norm: LayerNorm

    def __init__(self, layer: EncoderLayer, n: int) ->None:
        super().__init__()
        self.layers = clones(layer, n)
        self.norm = LayerNorm(layer.size)

    def forward(self, x: Tensor, mask: Tensor) ->Tensor:
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

from torch import Tensor, nn
from torch.nn import LayerNorm

from transformer.attention import MultiHeadedAttention
from transformer.utils import PositionwiseFeedForward, SublayerConnection, clones


class EncoderLayer(nn.Module):
    self_attn: MultiHeadedAttention
    feed_forward: PositionwiseFeedForward
    sublayer: nn.ModuleList
    size: int

    def __init__(
        self, size: int, self_attn: MultiHeadedAttention, feed_forward: PositionwiseFeedForward, dropout: float
    ) -> None:
        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x: Tensor, mask: Tensor) -> Tensor:  # TODO mask type
        x = self.sublayer[0](x, lambda t: self.self_attn(t, t, t, mask))
        return self.sublayer[1](x, self.feed_forward)


class Encoder(nn.Module):
    layers: nn.ModuleList
    norm: LayerNorm

    def __init__(self, layer: EncoderLayer, n: int) -> None:
        super().__init__()
        self.layers = clones(layer, n)
        self.norm = LayerNorm(layer.size)

    def forward(self, x: Tensor, mask: Tensor) -> Tensor:  # TODO mask type
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)
