text: # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  import math
  from typing import Dict, Optional, Tuple
  
  import torch
  import torch.nn.functional as F
  from fairseq import utils
  from fairseq.incremental_decoding_utils import with_incremental_state
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from fairseq.modules.quant_noise import quant_noise
  from torch import Tensor, nn
  from torch.nn import Parameter
  
  
  @with_incremental_state
  class MultiheadAttention(nn.Module):
      """Multi-headed attention.
  
      See "Attention Is All You Need" for more details.
      """
  
      def __init__(
          self,
          embed_dim,
          num_heads,
          kdim=None,
          vdim=None,
          dropout=0.0,
          bias=True,
          add_bias_kv=False,
          add_zero_attn=False,
          self_attention=False,
          encoder_decoder_attention=False,
          q_noise=0.0,
          qn_block_size=8,
          in_attn=False,
      ):
          super().__init__()
          self.embed_dim = embed_dim
          self.kdim = kdim if kdim is not None else embed_dim
          self.vdim = vdim if vdim is not None else embed_dim
          self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
          
          self.in_attn=in_attn
          
          self.num_heads = num_heads
          self.dropout_module = FairseqDropout(
              dropout, module_name=self.__class__.__name__
          )
  
          self.head_dim = embed_dim // num_heads
          assert (
              self.head_dim * num_heads == self.embed_dim
          ), "embed_dim must be divisible by num_heads"
          self.scaling = self.head_dim ** -0.5
  
          self.self_attention = self_attention
          self.encoder_decoder_attention = encoder_decoder_attention
  
          assert not self.self_attention or self.qkv_same_dim, (
              "Self-attention requires query, key and " "value to be of the same size"
          )
  
          self.k_proj = quant_noise(
              nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size
          )
          if not in_attn:
              self.v_proj = quant_noise(
                  nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size
              )
          self.q_proj = quant_noise(
              nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
          )
  
          if not in_attn:
              self.out_proj = quant_noise(
                  nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
              )
  
          if add_bias_kv:
              self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
              self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
          else:
              self.bias_k = self.bias_v = None
  
          self.add_zero_attn = add_zero_attn
  
          self.reset_parameters()
  
          self.onnx_trace = False
          self.tpu = False
  
      def prepare_for_onnx_export_(self):
          self.onnx_trace = True
  
      def prepare_for_tpu_(self, **kwargs):
          self.tpu = True
  
      def reset_parameters(self):
         # if self.qkv_same_dim:
         #     # Empirically observed the convergence to be much better with
         #     # the scaled initialization
         #     nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
         #     nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
         #     nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
         # else:
         #     nn.init.xavier_uniform_(self.k_proj.weight)
         #     nn.init.xavier_uniform_(self.v_proj.weight)
         #     nn.init.xavier_uniform_(self.q_proj.weight)
          
          utils.truncated_normal_(self.k_proj.weight)
          if not self.in_attn:
              utils.truncated_normal_(self.v_proj.weight)
          utils.truncated_normal_(self.q_proj.weight)
  
          if not self.in_attn:
          #    nn.init.xavier_uniform_(self.out_proj.weight)
              utils.truncated_normal_(self.out_proj.weight)
  
          if not self.in_attn and self.out_proj.bias is not None:
              nn.init.constant_(self.out_proj.bias, 0.0)
          if self.bias_k is not None:
              nn.init.xavier_normal_(self.bias_k)
          if self.bias_v is not None:
              nn.init.xavier_normal_(self.bias_v)
  
      def forward(
          self,
          query,
          key: Optional[Tensor],
          value: Optional[Tensor],
          key_padding_mask: Optional[Tensor] = None,
          incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
          need_weights: bool = True,
          static_kv: bool = False,
          attn_mask: Optional[Tensor] = None,
          span_mask: Optional[Tensor] = None,
          before_softmax: bool = False,
          need_head_weights: bool = False,
      ) -> Tuple[Tensor, Optional[Tensor]]:
          """Input shape: Time x Batch x Channel
  
          Args:
              key_padding_mask (ByteTensor, optional): mask to exclude
                  keys that are pads, of shape `(batch, src_len)`, where
                  padding elements are indicated by 1s.
              need_weights (bool, optional): return the attention weights,
                  averaged over heads (default: False).
              attn_mask (ByteTensor, optional): typically used to
                  implement causal attention, where the mask prevents the
                  attention from looking forward in time (default: None).
              before_softmax (bool, optional): return the raw attention
                  weights and values before the attention softmax.
              need_head_weights (bool, optional): return the attention
                  weights for each head. Implies *need_weights*. Default:
                  return the average attention weights over all heads.
          """
          if need_head_weights:
              need_weights = True
  
          tgt_len, bsz, embed_dim = query.size()
          assert embed_dim == self.embed_dim
          assert list(query.size()) == [tgt_len, bsz, embed_dim]
  
         # if (
         #     not self.onnx_trace
         #     and not self.tpu  # don't use PyTorch version on TPUs
         #     and incremental_state is None
         #     and not static_kv
         #     # A workaround for quantization to work. Otherwise JIT compilation
         #     # treats bias in linear module as method.
         #     and not torch.jit.is_scripting()
         # ):
         #     assert key is not None and value is not None
         #     return F.multi_head_attention_forward(
         #         query,
         #         key,
         #         value,
         #         self.embed_dim,
         #         self.num_heads,
         #         torch.empty([0]),
         #         torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
         #         self.bias_k,
         #         self.bias_v,
         #         self.add_zero_attn,
         #         self.dropout_module.p,
         #         self.out_proj.weight,
         #         self.out_proj.bias,
         #         self.training or self.dropout_module.apply_during_inference,
         #         key_padding_mask,
         #         need_weights,
         #         attn_mask,
         #         use_separate_proj_weight=True,
         #         q_proj_weight=self.q_proj.weight,
         #         k_proj_weight=self.k_proj.weight,
         #         v_proj_weight=self.v_proj.weight,
         #     )
  
          if incremental_state is not None:
              saved_state = self._get_input_buffer(incremental_state)
              if saved_state is not None and "prev_key" in saved_state:
                  # previous time steps are cached - no need to recompute
                  # key and value if they are static
                  if static_kv:
                      assert self.encoder_decoder_attention and not self.self_attention
                      key = value = None
          else:
              saved_state = None
  
          if self.self_attention:
              q = self.q_proj(query)
              k = self.k_proj(query)
  
              if not self.in_attn:
                  v = self.v_proj(query)
              else:
                  v = query
          elif self.encoder_decoder_attention:
              # encoder-decoder attention
              q = self.q_proj(query)
              if key is None:
                  assert value is None
                  k = v = None
              else:
                  k = self.k_proj(key)
  
                  if not self.in_attn:
                      v = self.v_proj(key)
                  else:
                      v = key
          else:
              assert key is not None and value is not None
              q = self.q_proj(query)
              k = self.k_proj(key)
              
              if not self.in_attn:
                  v = self.v_proj(value)
              else:
                  v = value
  
          q *= self.scaling
  
          if self.bias_k is not None:
              assert self.bias_v is not None
              k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
              v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
              if attn_mask is not None:
                  attn_mask = torch.cat(
                      [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
                  )
              if key_padding_mask is not None:
                  key_padding_mask = torch.cat(
                      [
                          key_padding_mask,
                          key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
                      ],
                      dim=1,
                  )
  
          q = (
              q.contiguous()
              .view(tgt_len, bsz * self.num_heads, self.head_dim)
              .transpose(0, 1)
          )
          if k is not None:
              k = (
                  k.contiguous()
                  .view(-1, bsz * self.num_heads, self.head_dim)
                  .transpose(0, 1)
              )
          if v is not None:
              v = (
                  v.contiguous()
                  .view(-1, bsz * self.num_heads, self.head_dim)
                  .transpose(0, 1)
              )
  
          if saved_state is not None:
              # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
              if "prev_key" in saved_state:
                  _prev_key = saved_state["prev_key"]
                  assert _prev_key is not None
                  prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
                  if static_kv:
                      k = prev_key
                  else:
                      assert k is not None
                      k = torch.cat([prev_key, k], dim=1)
              if "prev_value" in saved_state:
                  _prev_value = saved_state["prev_value"]
                  assert _prev_value is not None
                  prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
                  if static_kv:
                      v = prev_value
                  else:
                      assert v is not None
                      v = torch.cat([prev_value, v], dim=1)
              prev_key_padding_mask: Optional[Tensor] = None
              if "prev_key_padding_mask" in saved_state:
                  prev_key_padding_mask = saved_state["prev_key_padding_mask"]
              assert k is not None and v is not None
              key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
                  key_padding_mask=key_padding_mask,
                  prev_key_padding_mask=prev_key_padding_mask,
                  batch_size=bsz,
                  src_len=k.size(1),
                  static_kv=static_kv,
              )
  
              saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
              saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
              saved_state["prev_key_padding_mask"] = key_padding_mask
              # In this branch incremental_state is never None
              assert incremental_state is not None
              incremental_state = self._set_input_buffer(incremental_state, saved_state)
          assert k is not None
          src_len = k.size(1)
  
          #print("q",q.size())
          #print("k",k.shape)
          # This is part of a workaround to get around fork/join parallelism
          # not supporting Optional types.
          if key_padding_mask is not None and key_padding_mask.dim() == 0:
              key_padding_mask = None
  
          if key_padding_mask is not None:
              assert key_padding_mask.size(0) == bsz
              assert key_padding_mask.size(1) == src_len
  
          if self.add_zero_attn:
              assert v is not None
              src_len += 1
              k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
              v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
              if attn_mask is not None:
                  attn_mask = torch.cat(
                      [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
                  )
              if key_padding_mask is not None:
                  key_padding_mask = torch.cat(
                      [
                          key_padding_mask,
                          torch.zeros(key_padding_mask.size(0), 1).type_as(
                              key_padding_mask
                          ),
                      ],
                      dim=1,
                  )
  
          attn_weights = torch.bmm(q, k.transpose(1, 2))
          attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
  
          assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
  
          if attn_mask is not None:
              attn_mask = attn_mask.unsqueeze(0)
              if self.onnx_trace:
                  attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
              attn_weights += attn_mask
          
          if span_mask is not None:
              span_mask = span_mask.unsqueeze(1).repeat(1,self.num_heads,1,1)
              span_mask = span_mask.view(bsz * self.num_heads, tgt_len, src_len)
              attn_weights += span_mask
  
          if key_padding_mask is not None:
              # don't attend to padding symbols
              attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
              if not self.tpu:
                  attn_weights = attn_weights.masked_fill(
                      key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
                      float("-inf"),
                  )
              else:
                  attn_weights = attn_weights.transpose(0, 2)
                  attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
                  attn_weights = attn_weights.transpose(0, 2)
              attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
  
          #torch.set_printoptions(threshold=10000)
          #print(attn_weights[0])
          #raise ValueError()
          if before_softmax:
              return attn_weights, v
  
          attn_weights_float = utils.softmax(
              attn_weights, dim=-1, onnx_trace=self.onnx_trace
          )
          attn_weights = attn_weights_float.type_as(attn_weights)
          attn_probs = self.dropout_module(attn_weights, locked=False)
  
          assert v is not None
          attn = torch.bmm(attn_probs, v)
          assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
          if self.onnx_trace and attn.size(1) == 1:
              # when ONNX tracing a single decoder step (sequence length == 1)
              # the transpose is a no-op copy before view, thus unnecessary
              attn = attn.contiguous().view(tgt_len, bsz, embed_dim)
          else:
              attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
          
          if not self.in_attn:
              attn = self.out_proj(attn)
          attn_weights: Optional[Tensor] = None
          if need_weights:
              attn_weights = attn_weights_float.view(
                  bsz, self.num_heads, tgt_len, src_len
              ).transpose(1, 0)
              if not need_head_weights:
                  # average attention weights over heads
                  attn_weights = attn_weights.mean(dim=0)
  
          return attn, attn_weights
  
      @staticmethod
      def _append_prev_key_padding_mask(
          key_padding_mask: Optional[Tensor],
          prev_key_padding_mask: Optional[Tensor],
          batch_size: int,
          src_len: int,
          static_kv: bool,
      ) -> Optional[Tensor]:
          # saved key padding masks have shape (bsz, seq_len)
          if prev_key_padding_mask is not None and static_kv:
              new_key_padding_mask = prev_key_padding_mask
          elif prev_key_padding_mask is not None and key_padding_mask is not None:
              new_key_padding_mask = torch.cat(
                  [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
              )
          # During incremental decoding, as the padding token enters and
          # leaves the frame, there will be a time when prev or current
          # is None
          elif prev_key_padding_mask is not None:
              filler = torch.zeros(
                  (batch_size, src_len - prev_key_padding_mask.size(1)),
                  device=prev_key_padding_mask.device,
              )
              new_key_padding_mask = torch.cat(
                  [prev_key_padding_mask.float(), filler.float()], dim=1
              )
          elif key_padding_mask is not None:
              filler = torch.zeros(
                  (batch_size, src_len - key_padding_mask.size(1)),
                  device=key_padding_mask.device,
              )
              new_key_padding_mask = torch.cat(
                  [filler.float(), key_padding_mask.float()], dim=1
              )
          else:
              new_key_padding_mask = prev_key_padding_mask
          return new_key_padding_mask
  
      @torch.jit.export
      def reorder_incremental_state(
          self,
          incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
          new_order: Tensor,
      ):
          """Reorder buffered internal state (for incremental generation)."""
          input_buffer = self._get_input_buffer(incremental_state)
          if input_buffer is not None:
              for k in input_buffer.keys():
                  input_buffer_k = input_buffer[k]
                  if input_buffer_k is not None:
                      if self.encoder_decoder_attention and input_buffer_k.size(
                          0
                      ) == new_order.size(0):
                          break
                      input_buffer[k] = input_buffer_k.index_select(0, new_order)
              incremental_state = self._set_input_buffer(incremental_state, input_buffer)
          return incremental_state
  
      def _get_input_buffer(
          self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
      ) -> Dict[str, Optional[Tensor]]:
          result = self.get_incremental_state(incremental_state, "attn_state")
          if result is not None:
              return result
          else:
              empty_result: Dict[str, Optional[Tensor]] = {}
              return empty_result
  
      def _set_input_buffer(
          self,
          incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
          buffer: Dict[str, Optional[Tensor]],
      ):
          return self.set_incremental_state(incremental_state, "attn_state", buffer)
  
      def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):
          return attn_weights
  
      def upgrade_state_dict_named(self, state_dict, name):
          prefix = name + "." if name != "" else ""
          items_to_add = {}
          keys_to_remove = []
          for k in state_dict.keys():
              if k.endswith(prefix + "in_proj_weight"):
                  # in_proj_weight used to be q + k + v with same dimensions
                  dim = int(state_dict[k].shape[0] / 3)
                  items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim]
                  items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim]
                  items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :]
  
                  keys_to_remove.append(k)
  
                  k_bias = prefix + "in_proj_bias"
                  if k_bias in state_dict.keys():
                      dim = int(state_dict[k].shape[0] / 3)
                      items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim]
                      items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][
                          dim : 2 * dim
                      ]
                      items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :]
  
                      keys_to_remove.append(prefix + "in_proj_bias")
  
          for k in keys_to_remove:
              del state_dict[k]
  
          for key, value in items_to_add.items():
              state_dict[key] = value
  