text: import copy
  from typing import Optional, Any, Union, Callable
  
  import torch
  from torch import Tensor
  from .. import functional as F
  from .module import Module
  from .activation import MultiheadAttention
  from .container import ModuleList
  from ..init import xavier_uniform_
  from .dropout import Dropout
  from .linear import Linear
  from .normalization import LayerNorm
  
  
  class Transformer(Module):
      r"""A transformer model. User is able to modify the attributes as needed. The architecture
      is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer,
      Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and
      Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information
      Processing Systems, pages 6000-6010. Users can build the BERT(https://arxiv.org/abs/1810.04805)
      model with corresponding parameters.
  
      Args:
          d_model: the number of expected features in the encoder/decoder inputs (default=512).
          nhead: the number of heads in the multiheadattention models (default=8).
          num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).
          num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6).
          dim_feedforward: the dimension of the feedforward network model (default=2048).
          dropout: the dropout value (default=0.1).
          activation: the activation function of encoder/decoder intermediate layer, can be a string
              ("relu" or "gelu") or a unary callable. Default: relu
          custom_encoder: custom encoder (default=None).
          custom_decoder: custom decoder (default=None).
          layer_norm_eps: the eps value in layer nmasormalization components (default=1e-5).
          batch_first: If ``True``, then the input and output tensors are provided
              as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
          norm_first: if ``True``, encoder and decoder layers will perform LayerNorms before
              other attention and feedforward operations, otherwise after. Default: ``False`` (after).
  
      Examples::
          >>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
          >>> src = torch.rand((10, 32, 512))
          >>> tgt = torch.rand((20, 32, 512))
          >>> out = transformer_model(src, tgt)
  
      Note: A full example to apply nn.Transformer module for the word language model is available in
      https://github.com/pytorch/examples/tree/master/word_language_model
      """
  
      def __init__(self, d_model: int = 512, nhead: int = 8, num_encoder_layers: int = 6,
                   num_decoder_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1,
                   activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
                   custom_encoder: Optional[Any] = None, custom_decoder: Optional[Any] = None,
                   layer_norm_eps: float = 1e-5, batch_first: bool = False, norm_first: bool = False,
                   device=None, dtype=None) -> None:
          factory_kwargs = {'device': device, 'dtype': dtype}
          super(Transformer, self).__init__()
  
          if custom_encoder is not None:
              self.encoder = custom_encoder
          else:
              encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout,
                                                      activation, layer_norm_eps, batch_first, norm_first,
                                                      **factory_kwargs)
              encoder_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
              self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
  
          if custom_decoder is not None:
              self.decoder = custom_decoder
          else:
              decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout,
                                                      activation, layer_norm_eps, batch_first, norm_first,
                                                      **factory_kwargs)
              decoder_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
              self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm)
  
          self._reset_parameters()
  
          self.d_model = d_model
          self.nhead = nhead
  
          self.batch_first = batch_first
  
      def forward(self, src: Tensor, tgt: Tensor, src_mask: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None,
                  memory_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None,
                  tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
          r"""Take in and process masked source/target sequences.
  
          Args:
              src: the sequence to the encoder (required).
              tgt: the sequence to the decoder (required).
              src_mask: the additive mask for the src sequence (optional).
              tgt_mask: the additive mask for the tgt sequence (optional).
              memory_mask: the additive mask for the encoder output (optional).
              src_key_padding_mask: the ByteTensor mask for src keys per batch (optional).
              tgt_key_padding_mask: the ByteTensor mask for tgt keys per batch (optional).
              memory_key_padding_mask: the ByteTensor mask for memory keys per batch (optional).
  
          Shape:
              - src: :math:`(S, N, E)`, `(N, S, E)` if batch_first.
              - tgt: :math:`(T, N, E)`, `(N, T, E)` if batch_first.
              - src_mask: :math:`(S, S)`.
              - tgt_mask: :math:`(T, T)`.
              - memory_mask: :math:`(T, S)`.
              - src_key_padding_mask: :math:`(N, S)`.
              - tgt_key_padding_mask: :math:`(N, T)`.
              - memory_key_padding_mask: :math:`(N, S)`.
  
              Note: [src/tgt/memory]_mask ensures that position i is allowed to attend the unmasked
              positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
              while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
              are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
              is provided, it will be added to the attention weight.
              [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by
              the attention. If a ByteTensor is provided, the non-zero positions will be ignored while the zero
              positions will be unchanged. If a BoolTensor is provided, the positions with the
              value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
  
              - output: :math:`(T, N, E)`, `(N, T, E)` if batch_first.
  
              Note: Due to the multi-head attention architecture in the transformer model,
              the output sequence length of a transformer is same as the input sequence
              (i.e. target) length of the decode.
  
              where S is the source sequence length, T is the target sequence length, N is the
              batch size, E is the feature number
  
          Examples:
              >>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)
          """
  
          if not self.batch_first and src.size(1) != tgt.size(1):
              raise RuntimeError("the batch number of src and tgt must be equal")
          elif self.batch_first and src.size(0) != tgt.size(0):
              raise RuntimeError("the batch number of src and tgt must be equal")
  
          if src.size(2) != self.d_model or tgt.size(2) != self.d_model:
              raise RuntimeError("the feature number of src and tgt must be equal to d_model")
  
          memory = self.encoder(src, mask=src_mask, src_key_padding_mask=src_key_padding_mask)
          output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=memory_mask,
                                tgt_key_padding_mask=tgt_key_padding_mask,
                                memory_key_padding_mask=memory_key_padding_mask)
          return output
  
  
      @staticmethod
      def generate_square_subsequent_mask(sz: int) -> Tensor:
          r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
              Unmasked positions are filled with float(0.0).
          """
          return torch.triu(torch.full((sz, sz), float('-inf')), diagonal=1)
  
  
      def _reset_parameters(self):
          r"""Initiate parameters in the transformer model."""
  
          for p in self.parameters():
              if p.dim() > 1:
                  xavier_uniform_(p)
  
  
  
  class TransformerEncoder(Module):
      r"""TransformerEncoder is a stack of N encoder layers
  
      Args:
          encoder_layer: an instance of the TransformerEncoderLayer() class (required).
          num_layers: the number of sub-encoder-layers in the encoder (required).
          norm: the layer normalization component (optional).
  
      Examples::
          >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
          >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)
          >>> src = torch.rand(10, 32, 512)
          >>> out = transformer_encoder(src)
      """
      __constants__ = ['norm']
  
      def __init__(self, encoder_layer, num_layers, norm=None):
          super(TransformerEncoder, self).__init__()
          self.layers = _get_clones(encoder_layer, num_layers)
          self.num_layers = num_layers
          self.norm = norm
  
      def forward(self, src: Tensor, mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None) -> Tensor:
          r"""Pass the input through the encoder layers in turn.
  
          Args:
              src: the sequence to the encoder (required).
              mask: the mask for the src sequence (optional).
              src_key_padding_mask: the mask for the src keys per batch (optional).
  
          Shape:
              see the docs in Transformer class.
          """
          output = src
  
          for mod in self.layers:
              output = mod(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask)
  
          if self.norm is not None:
              output = self.norm(output)
  
          return output
  
  
  
  class TransformerDecoder(Module):
      r"""TransformerDecoder is a stack of N decoder layers
  
      Args:
          decoder_layer: an instance of the TransformerDecoderLayer() class (required).
          num_layers: the number of sub-decoder-layers in the decoder (required).
          norm: the layer normalization component (optional).
  
      Examples::
          >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
          >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)
          >>> memory = torch.rand(10, 32, 512)
          >>> tgt = torch.rand(20, 32, 512)
          >>> out = transformer_decoder(tgt, memory)
      """
      __constants__ = ['norm']
  
      def __init__(self, decoder_layer, num_layers, norm=None):
          super(TransformerDecoder, self).__init__()
          self.layers = _get_clones(decoder_layer, num_layers)
          self.num_layers = num_layers
          self.norm = norm
  
      def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
                  memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None,
                  memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
          r"""Pass the inputs (and mask) through the decoder layer in turn.
  
          Args:
              tgt: the sequence to the decoder (required).
              memory: the sequence from the last layer of the encoder (required).
              tgt_mask: the mask for the tgt sequence (optional).
              memory_mask: the mask for the memory sequence (optional).
              tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
              memory_key_padding_mask: the mask for the memory keys per batch (optional).
  
          Shape:
              see the docs in Transformer class.
          """
          output = tgt
  
          for mod in self.layers:
              output = mod(output, memory, tgt_mask=tgt_mask,
                           memory_mask=memory_mask,
                           tgt_key_padding_mask=tgt_key_padding_mask,
                           memory_key_padding_mask=memory_key_padding_mask)
  
          if self.norm is not None:
              output = self.norm(output)
  
          return output
  
  
  class TransformerEncoderLayer(Module):
      r"""TransformerEncoderLayer is made up of self-attn and feedforward network.
      This standard encoder layer is based on the paper "Attention Is All You Need".
      Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
      Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
      Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
      in a different way during application.
  
      Args:
          d_model: the number of expected features in the input (required).
          nhead: the number of heads in the multiheadattention models (required).
          dim_feedforward: the dimension of the feedforward network model (default=2048).
          dropout: the dropout value (default=0.1).
          activation: the activation function of the intermediate layer, can be a string
              ("relu" or "gelu") or a unary callable. Default: relu
          layer_norm_eps: the eps value in layer normalization components (default=1e-5).
          batch_first: If ``True``, then the input and output tensors are provided
              as (batch, seq, feature). Default: ``False``.
          norm_first: if ``True``, layer norm is done prior to attention and feedforward
              operations, respectivaly. Otherwise it's done after. Default: ``False`` (after).
  
      Examples::
          >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
          >>> src = torch.rand(10, 32, 512)
          >>> out = encoder_layer(src)
  
      Alternatively, when ``batch_first`` is ``True``:
          >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True)
          >>> src = torch.rand(32, 10, 512)
          >>> out = encoder_layer(src)
      """
      __constants__ = ['batch_first', 'norm_first']
  
      def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation=F.relu,
                   layer_norm_eps=1e-5, batch_first=False, norm_first=False,
                   device=None, dtype=None) -> None:
          factory_kwargs = {'device': device, 'dtype': dtype}
          super(TransformerEncoderLayer, self).__init__()
          self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first,
                                              **factory_kwargs)
          # Implementation of Feedforward model
          self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
          self.dropout = Dropout(dropout)
          self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)
  
          self.norm_first = norm_first
          self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
          self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
          self.dropout1 = Dropout(dropout)
          self.dropout2 = Dropout(dropout)
  
          # Legacy string support for activation function.
          if isinstance(activation, str):
              self.activation = _get_activation_fn(activation)
          else:
              self.activation = activation
  
      def __setstate__(self, state):
          if 'activation' not in state:
              state['activation'] = F.relu
          super(TransformerEncoderLayer, self).__setstate__(state)
  
      def forward(self, src: Tensor, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None) -> Tensor:
          r"""Pass the input through the encoder layer.
  
          Args:
              src: the sequence to the encoder layer (required).
              src_mask: the mask for the src sequence (optional).
              src_key_padding_mask: the mask for the src keys per batch (optional).
  
          Shape:
              see the docs in Transformer class.
          """
  
          # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf
  
          x = src
          if self.norm_first:
              x = x + self._sa_block(self.norm1(x), src_mask, src_key_padding_mask)
              x = x + self._ff_block(self.norm2(x))
          else:
              x = self.norm1(x + self._sa_block(x, src_mask, src_key_padding_mask))
              x = self.norm2(x + self._ff_block(x))
  
          return x
  
  
      # self-attention block
      def _sa_block(self, x: Tensor,
                    attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor]) -> Tensor:
          x = self.self_attn(x, x, x,
                             attn_mask=attn_mask,
                             key_padding_mask=key_padding_mask,
                             need_weights=False)[0]
          return self.dropout1(x)
  
      # feed forward block
      def _ff_block(self, x: Tensor) -> Tensor:
          x = self.linear2(self.dropout(self.activation(self.linear1(x))))
          return self.dropout2(x)
  
  
  
  class TransformerDecoderLayer(Module):
      r"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
      This standard decoder layer is based on the paper "Attention Is All You Need".
      Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
      Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
      Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
      in a different way during application.
  
      Args:
          d_model: the number of expected features in the input (required).
          nhead: the number of heads in the multiheadattention models (required).
          dim_feedforward: the dimension of the feedforward network model (default=2048).
          dropout: the dropout value (default=0.1).
          activation: the activation function of the intermediate layer, can be a string
              ("relu" or "gelu") or a unary callable. Default: relu
          layer_norm_eps: the eps value in layer normalization components (default=1e-5).
          batch_first: If ``True``, then the input and output tensors are provided
              as (batch, seq, feature). Default: ``False``.
          norm_first: if ``True``, layer norm is done prior to self attention, multihead
              attention and feedforward operations, respectivaly. Otherwise it's done after.
              Default: ``False`` (after).
  
      Examples::
          >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
          >>> memory = torch.rand(10, 32, 512)
          >>> tgt = torch.rand(20, 32, 512)
          >>> out = decoder_layer(tgt, memory)
  
      Alternatively, when ``batch_first`` is ``True``:
          >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8, batch_first=True)
          >>> memory = torch.rand(32, 10, 512)
          >>> tgt = torch.rand(32, 20, 512)
          >>> out = decoder_layer(tgt, memory)
      """
      __constants__ = ['batch_first', 'norm_first']
  
      def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation=F.relu,
                   layer_norm_eps=1e-5, batch_first=False, norm_first=False,
                   device=None, dtype=None) -> None:
          factory_kwargs = {'device': device, 'dtype': dtype}
          super(TransformerDecoderLayer, self).__init__()
          self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first,
                                              **factory_kwargs)
          self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first,
                                                   **factory_kwargs)
          # Implementation of Feedforward model
          self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
          self.dropout = Dropout(dropout)
          self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)
  
          self.norm_first = norm_first
          self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
          self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
          self.norm3 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
          self.dropout1 = Dropout(dropout)
          self.dropout2 = Dropout(dropout)
          self.dropout3 = Dropout(dropout)
  
          # Legacy string support for activation function.
          if isinstance(activation, str):
              self.activation = _get_activation_fn(activation)
          else:
              self.activation = activation
  
      def __setstate__(self, state):
          if 'activation' not in state:
              state['activation'] = F.relu
          super(TransformerDecoderLayer, self).__setstate__(state)
  
      def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None,
                  tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
          r"""Pass the inputs (and mask) through the decoder layer.
  
          Args:
              tgt: the sequence to the decoder layer (required).
              memory: the sequence from the last layer of the encoder (required).
              tgt_mask: the mask for the tgt sequence (optional).
              memory_mask: the mask for the memory sequence (optional).
              tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
              memory_key_padding_mask: the mask for the memory keys per batch (optional).
  
          Shape:
              see the docs in Transformer class.
          """
          # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf
  
          x = tgt
          if self.norm_first:
              x = x + self._sa_block(self.norm1(x), tgt_mask, tgt_key_padding_mask)
              x = x + self._mha_block(self.norm2(x), memory, memory_mask, memory_key_padding_mask)
              x = x + self._ff_block(self.norm3(x))
          else:
              x = self.norm1(x + self._sa_block(x, tgt_mask, tgt_key_padding_mask))
              x = self.norm2(x + self._mha_block(x, memory, memory_mask, memory_key_padding_mask))
              x = self.norm3(x + self._ff_block(x))
  
          return x
  
  
      # self-attention block
      def _sa_block(self, x: Tensor,
                    attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor]) -> Tensor:
          x = self.self_attn(x, x, x,
                             attn_mask=attn_mask,
                             key_padding_mask=key_padding_mask,
                             need_weights=False)[0]
          return self.dropout1(x)
  
      # multihead attention block
      def _mha_block(self, x: Tensor, mem: Tensor,
                     attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor]) -> Tensor:
          x = self.multihead_attn(x, mem, mem,
                                  attn_mask=attn_mask,
                                  key_padding_mask=key_padding_mask,
                                  need_weights=False)[0]
          return self.dropout2(x)
  
      # feed forward block
      def _ff_block(self, x: Tensor) -> Tensor:
          x = self.linear2(self.dropout(self.activation(self.linear1(x))))
          return self.dropout3(x)
  
  
  
  def _get_clones(module, N):
      return ModuleList([copy.deepcopy(module) for i in range(N)])
  
  
  def _get_activation_fn(activation):
      if activation == "relu":
          return F.relu
      elif activation == "gelu":
          return F.gelu
  
      raise RuntimeError("activation should be relu/gelu, not {}".format(activation))