text: # coding=utf-8
  # Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ PyTorch Speech2Text model. """
  
  
  import math
  import random
  from typing import Optional, Tuple
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  from torch.nn import CrossEntropyLoss
  
  from ...activations import ACT2FN
  from ...file_utils import (
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_outputs import (
      BaseModelOutput,
      BaseModelOutputWithPastAndCrossAttentions,
      Seq2SeqLMOutput,
      Seq2SeqModelOutput,
  )
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_speech_to_text import Speech2TextConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "Speech2TextConfig"
  _TOKENIZER_FOR_DOC = "Speech2TextTokenizer"
  
  
  SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "facebook/s2t-small-librispeech-asr",
      # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
  ]
  
  
  # Copied from transformers.models.bart.modeling_bart.shift_tokens_right
  def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
      """
      Shift input ids one token to the right.
      """
      shifted_input_ids = input_ids.new_zeros(input_ids.shape)
      shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
      shifted_input_ids[:, 0] = decoder_start_token_id
  
      assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
      # replace possible -100 values in labels by `pad_token_id`
      shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
  
      return shifted_input_ids
  
  
  # Copied from transformers.models.bart.modeling_bart._make_causal_mask
  def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
      """
      Make causal mask used for bi-directional self-attention.
      """
      bsz, tgt_len = input_ids_shape
      mask = torch.full((tgt_len, tgt_len), float("-inf"))
      mask_cond = torch.arange(mask.size(-1))
      mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
      mask = mask.to(dtype)
  
      if past_key_values_length > 0:
          mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
      return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
  
  
  # Copied from transformers.models.bart.modeling_bart._expand_mask
  def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
      """
      Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
      """
      bsz, src_len = mask.size()
      tgt_len = tgt_len if tgt_len is not None else src_len
  
      expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
  
      inverted_mask = 1.0 - expanded_mask
  
      return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
  
  
  class Conv1dSubsampler(nn.Module):
      """
      Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
      via gated linear units (https://arxiv.org/abs/1911.08460)
      """
  
      def __init__(self, config):
          super(Conv1dSubsampler, self).__init__()
          self.config = config
          self.num_layers = config.num_conv_layers
          self.in_channels = config.input_feat_per_channel * config.input_channels
          self.mid_channels = config.conv_channels
          self.out_channels = config.d_model
          self.kernel_sizes = config.conv_kernel_sizes
  
          self.conv_layers = nn.ModuleList(
              nn.Conv1d(
                  self.in_channels if i == 0 else self.mid_channels // 2,
                  self.mid_channels if i < self.num_layers - 1 else self.out_channels * 2,
                  kernel_size=k,
                  stride=2,
                  padding=k // 2,
              )
              for i, k in enumerate(self.kernel_sizes)
          )
  
      def forward(self, input_features):
          hidden_states = input_features.transpose(1, 2).contiguous()  # -> B x (C x D) x T
          for conv in self.conv_layers:
              hidden_states = conv(hidden_states)
              hidden_states = nn.functional.glu(hidden_states, dim=1)
          hidden_states = hidden_states.transpose(1, 2).contiguous()  # -> T x B x (C x D)
          return hidden_states
  
  
  class Speech2TextSinusoidalPositionalEmbedding(nn.Module):
      """This module produces sinusoidal positional embeddings of any length."""
  
      def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
          super().__init__()
          self.offset = 2
          self.embedding_dim = embedding_dim
          self.padding_idx = padding_idx
          self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
  
      def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
          emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
          if hasattr(self, "weights"):
              # in forward, put the weights on correct device
              emb_weights = emb_weights.to(self.weights.device)
  
          self.weights = nn.Parameter(emb_weights)
          self.weights.requires_grad = False
          self.weights.detach_()
  
      @staticmethod
      def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
          """
          Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the
          description in Section 3.5 of "Attention Is All You Need".
          """
          half_dim = embedding_dim // 2
          emb = math.log(10000) / (half_dim - 1)
          emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
          emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
          emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
          if embedding_dim % 2 == 1:
              # zero pad
              emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
          if padding_idx is not None:
              emb[padding_idx, :] = 0
          return emb
  
      @torch.no_grad()
      def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
          bsz, seq_len = input_ids.size()
          # Create the position ids from the input token ids. Any padded tokens remain padded.
          position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(
              input_ids.device
          )
  
          # expand embeddings if needed
          max_pos = self.padding_idx + 1 + seq_len
          if max_pos > self.weights.size(0):
              self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
  
          return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()
  
      def create_position_ids_from_input_ids(
          self, input_ids: torch.Tensor, padding_idx: int, past_key_values_length: Optional[int] = 0
      ):
          """
          Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
          symbols are ignored. This is modified from fairseq's `utils.make_positions`.
  
          Args:
              x: torch.Tensor x:
          Returns: torch.Tensor
          """
          # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
          mask = input_ids.ne(padding_idx).int()
          incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
          return incremental_indices.long() + padding_idx
  
  
  # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Speech2Text
  class Speech2TextAttention(nn.Module):
      """Multi-headed attention from 'Attention Is All You Need' paper"""
  
      def __init__(
          self,
          embed_dim: int,
          num_heads: int,
          dropout: float = 0.0,
          is_decoder: bool = False,
          bias: bool = True,
      ):
          super().__init__()
          self.embed_dim = embed_dim
          self.num_heads = num_heads
          self.dropout = dropout
          self.head_dim = embed_dim // num_heads
          assert (
              self.head_dim * num_heads == self.embed_dim
          ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads})."
          self.scaling = self.head_dim ** -0.5
          self.is_decoder = is_decoder
  
          self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
  
      def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
          return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          key_value_states: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          attention_mask: Optional[torch.Tensor] = None,
          layer_head_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
          """Input shape: Batch x Time x Channel"""
  
          # if key_value_states are provided this layer is used as a cross-attention layer
          # for the decoder
          is_cross_attention = key_value_states is not None
          bsz, tgt_len, embed_dim = hidden_states.size()
  
          # get query proj
          query_states = self.q_proj(hidden_states) * self.scaling
          # get key, value proj
          if is_cross_attention and past_key_value is not None:
              # reuse k,v, cross_attentions
              key_states = past_key_value[0]
              value_states = past_key_value[1]
          elif is_cross_attention:
              # cross_attentions
              key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
              value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
          elif past_key_value is not None:
              # reuse k, v, self_attention
              key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
              value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
              key_states = torch.cat([past_key_value[0], key_states], dim=2)
              value_states = torch.cat([past_key_value[1], value_states], dim=2)
          else:
              # self_attention
              key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
              value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
  
          if self.is_decoder:
              # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
              # Further calls to cross_attention layer can then reuse all cross-attention
              # key/value_states (first "if" case)
              # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
              # all previous decoder key/value_states. Further calls to uni-directional self-attention
              # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
              # if encoder bi-directional self-attention `past_key_value` is always `None`
              past_key_value = (key_states, value_states)
  
          proj_shape = (bsz * self.num_heads, -1, self.head_dim)
          query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
          key_states = key_states.view(*proj_shape)
          value_states = value_states.view(*proj_shape)
  
          src_len = key_states.size(1)
          attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
  
          if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
              raise ValueError(
                  f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
              )
  
          if attention_mask is not None:
              if attention_mask.size() != (bsz, 1, tgt_len, src_len):
                  raise ValueError(
                      f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                  )
              attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
              attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
  
          attn_weights = F.softmax(attn_weights, dim=-1)
  
          if layer_head_mask is not None:
              if layer_head_mask.size() != (self.num_heads,):
                  raise ValueError(
                      f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
                  )
              attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
              attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
  
          if output_attentions:
              # this operation is a bit awkward, but it's required to
              # make sure that attn_weights keeps its gradient.
              # In order to do so, attn_weights have to be reshaped
              # twice and have to be reused in the following
              attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
              attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
          else:
              attn_weights_reshaped = None
  
          attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training)
  
          attn_output = torch.bmm(attn_probs, value_states)
  
          if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
              raise ValueError(
                  f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
              )
  
          attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
          attn_output = attn_output.transpose(1, 2)
          attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
  
          attn_output = self.out_proj(attn_output)
  
          return attn_output, attn_weights_reshaped, past_key_value
  
  
  class Speech2TextEncoderLayer(nn.Module):
      def __init__(self, config: Speech2TextConfig):
          super().__init__()
          self.embed_dim = config.d_model
          self.self_attn = Speech2TextAttention(
              embed_dim=self.embed_dim,
              num_heads=config.encoder_attention_heads,
              dropout=config.attention_dropout,
          )
          self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
          self.dropout = config.dropout
          self.activation_fn = ACT2FN[config.activation_function]
          self.activation_dropout = config.activation_dropout
          self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
          self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
          self.final_layer_norm = nn.LayerNorm(self.embed_dim)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          attention_mask: torch.Tensor,
          layer_head_mask: torch.Tensor,
          output_attentions: bool = False,
      ):
          """
          Args:
              hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape :obj:`(seq_len, batch, embed_dim)`
              attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                  :obj:`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              layer_head_mask (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size
                  :obj:`(config.encoder_attention_heads,)`.
              output_attentions (:obj:`bool`, `optional`):
                  Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                  returned tensors for more detail.
          """
          residual = hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
          hidden_states, attn_weights, _ = self.self_attn(
              hidden_states=hidden_states,
              attention_mask=attention_mask,
              layer_head_mask=layer_head_mask,
              output_attentions=output_attentions,
          )
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
  
          residual = hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = F.dropout(hidden_states, p=self.activation_dropout, training=self.training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
  
          if hidden_states.dtype == torch.float16 and (
              torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
          ):
              clamp_value = torch.finfo(hidden_states.dtype).max - 1000
              hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (attn_weights,)
  
          return outputs
  
  
  class Speech2TextDecoderLayer(nn.Module):
      def __init__(self, config: Speech2TextConfig):
          super().__init__()
          self.embed_dim = config.d_model
  
          self.self_attn = Speech2TextAttention(
              embed_dim=self.embed_dim,
              num_heads=config.decoder_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=True,
          )
          self.dropout = config.dropout
          self.activation_fn = ACT2FN[config.activation_function]
          self.activation_dropout = config.activation_dropout
  
          self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
          self.encoder_attn = Speech2TextAttention(
              self.embed_dim,
              config.decoder_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=True,
          )
          self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
          self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
          self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
          self.final_layer_norm = nn.LayerNorm(self.embed_dim)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          encoder_hidden_states: Optional[torch.Tensor] = None,
          encoder_attention_mask: Optional[torch.Tensor] = None,
          layer_head_mask: Optional[torch.Tensor] = None,
          cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          output_attentions: Optional[bool] = False,
          use_cache: Optional[bool] = True,
      ):
          """
          Args:
              hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape :obj:`(seq_len, batch, embed_dim)`
              attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                  :obj:`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              encoder_hidden_states (:obj:`torch.FloatTensor`): cross attention input to the layer of shape :obj:`(seq_len, batch, embed_dim)`
              encoder_attention_mask (:obj:`torch.FloatTensor`): encoder attention mask of size
                  :obj:`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              layer_head_mask (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size
                  :obj:`(encoder_attention_heads,)`.
              cross_attn_layer_head_mask (:obj:`torch.FloatTensor`): mask for cross-attention heads in a given layer of
                  size `(decoder_attention_heads,)`.
              past_key_value (:obj:`Tuple(torch.FloatTensor)`): cached past key and value projection states
              output_attentions (:obj:`bool`, `optional`):
                  Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                  returned tensors for more detail.
          """
          residual = hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Self Attention
          # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
          self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
          # add present self-attn cache to positions 1,2 of present_key_value tuple
          hidden_states, self_attn_weights, present_key_value = self.self_attn(
              hidden_states=hidden_states,
              past_key_value=self_attn_past_key_value,
              attention_mask=attention_mask,
              layer_head_mask=layer_head_mask,
              output_attentions=output_attentions,
          )
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
  
          # Cross-Attention Block
          cross_attn_present_key_value = None
          cross_attn_weights = None
          if encoder_hidden_states is not None:
              residual = hidden_states
              hidden_states = self.encoder_attn_layer_norm(hidden_states)
  
              # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
              cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
              hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
                  hidden_states=hidden_states,
                  key_value_states=encoder_hidden_states,
                  attention_mask=encoder_attention_mask,
                  layer_head_mask=cross_attn_layer_head_mask,
                  past_key_value=cross_attn_past_key_value,
                  output_attentions=output_attentions,
              )
              hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
              hidden_states = residual + hidden_states
  
              # add cross-attn to positions 3,4 of present_key_value tuple
              present_key_value = present_key_value + cross_attn_present_key_value
  
          # Fully Connected
          residual = hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = F.dropout(hidden_states, p=self.activation_dropout, training=self.training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (self_attn_weights, cross_attn_weights)
  
          if use_cache:
              outputs += (present_key_value,)
  
          return outputs
  
  
  class Speech2TextPreTrainedModel(PreTrainedModel):
      config_class = Speech2TextConfig
      base_model_prefix = "model"
  
      def _init_weights(self, module):
          std = self.config.init_std
          if isinstance(module, (nn.Linear, nn.Conv1d)):
              module.weight.data.normal_(mean=0.0, std=std)
              if module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.Embedding):
              module.weight.data.normal_(mean=0.0, std=std)
              if module.padding_idx is not None:
                  module.weight.data[module.padding_idx].zero_()
  
      def _get_subsampled_output_lengths(self, input_lengths: torch.LongTensor):
          """
          Computes the output length of the convolutional layers
          """
  
          for i in range(self.config.num_conv_layers):
              input_lengths = (input_lengths - 1) // 2 + 1
  
          return input_lengths
  
      def _get_subsampled_encoder_attn_mask(self, attention_mask):
          # generate creates 3D attention mask, because of the shape of input_features
          # convert it to 2D if thats the case
          if len(attention_mask.shape) > 2:
              attention_mask = attention_mask[:, :, -1]
  
          subsampled_lengths = self._get_subsampled_output_lengths(attention_mask.sum(-1))
          max_len = subsampled_lengths.max().item()
          bsz = attention_mask.size()[0]
          attention_mask = torch.zeros((bsz, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
  
          # these two operations makes sure that all values
          # before the output lengths indices are attended to
          attention_mask[(torch.arange(bsz, device=attention_mask.device), subsampled_lengths - 1)] = 1
          attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).long()
          return attention_mask
  
  
  SPEECH_TO_TEXT_START_DOCSTRING = r"""
      This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
      methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
      pruning heads etc.)
  
      This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
      subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
      general usage and behavior.
  
      Parameters:
          config (:class:`~transformers.Speech2TextConfig`):
              Model configuration class with all the parameters of the model. Initializing with a config file does not
              load the weights associated with the model, only the configuration. Check out the
              :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
  """
  
  SPEECH_TO_TEXT_INPUTS_DOCSTRING = r"""
      Args:
          input_features (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length, feature_size)`):
              Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained
              by loading a ``.flac`` or ``.wav`` audio file into an array of type :obj:`List[float]` or a
              :obj:`numpy.ndarray`, *e.g.* via the soundfile library (``pip install soundfile``). To prepare the array
              into :obj:`input_features`, the :class:`~transformers.Speech2TextTokenizer` should be used for extracting
              the fbank features, padding and conversion into a tensor of type :obj:`torch.FloatTensor`. See
              :meth:`~transformers.Speech2TextTokenizer.__call__`
          attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to avoid performing convolution and attention on padding token indices. Mask values selected in ``[0,
              1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Indices of decoder input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.SpeechToTextTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are decoder input IDs? <../glossary.html#decoder-input-ids>`__
  
              SpeechToText uses the :obj:`eos_token_id` as the starting token for :obj:`decoder_input_ids` generation. If
              :obj:`past_key_values` is used, optionally only the last :obj:`decoder_input_ids` have to be input (see
              :obj:`past_key_values`).
          decoder_attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
              also be used by default. <<<<<<< HEAD
  
              If you want to change padding behavior, you should read
              :func:`modeling_speech_to_text._prepare_decoder_inputs` and modify to your needs. See diagram 1 in `the
              paper <https://arxiv.org/abs/1910.13461>`__ for more information on the default strategy.
          head_mask (:obj:`torch.Tensor` of shape :obj:`(encoder_layers, encoder_attention_heads)`, `optional`):
              Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in ``[0, 1]``:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          decoder_head_mask (:obj:`torch.Tensor` of shape :obj:`(decoder_layers, decoder_attention_heads)`, `optional`):
              Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in ``[0, 1]``:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          cross_attn_head_mask (:obj:`torch.Tensor` of shape :obj:`(decoder_layers, decoder_attention_heads)`, `optional`):
              Mask to nullify selected heads of the cross-attention modules. Mask values selected in ``[0, 1]``:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
              Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`:
              :obj:`attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`,
              `optional`) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
              cross-attention of the decoder.
          past_key_values (:obj:`Tuple[Tuple[torch.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
              Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.
  
              If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
              (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
              instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size, sequence_length)`.
          decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded
              representation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_inputs_embeds`
              have to be input (see :obj:`past_key_values`). This is useful if you want more control over how to convert
              :obj:`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
  
              If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_inputs_embeds`
              takes the value of :obj:`inputs_embeds`.
          use_cache (:obj:`bool`, `optional`):
              If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
              decoding (see :obj:`past_key_values`).
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  
  class Speech2TextEncoder(Speech2TextPreTrainedModel):
      """
      Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
      :class:`Speech2TextEncoderLayer`.
  
      Args:
          config: Speech2TextConfig
          embed_tokens (torch.nn.Embedding): output embedding
      """
  
      def __init__(self, config: Speech2TextConfig):
          super().__init__(config)
  
          self.dropout = config.dropout
          self.layerdrop = config.encoder_layerdrop
  
          embed_dim = config.d_model
          self.padding_idx = config.pad_token_id
          self.max_source_positions = config.max_source_positions
          self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
  
          self.conv = Conv1dSubsampler(config)
  
          self.embed_positions = Speech2TextSinusoidalPositionalEmbedding(
              self.max_source_positions,
              embed_dim,
              self.padding_idx,
          )
          self.layers = nn.ModuleList([Speech2TextEncoderLayer(config) for _ in range(config.encoder_layers)])
          self.layer_norm = nn.LayerNorm(config.d_model)
  
          self.init_weights()
  
      def forward(
          self,
          input_features,
          attention_mask=None,
          head_mask=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          Args:
              input_features (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length, feature_size)`):
                  Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be
                  obtained by loading a ``.flac`` or ``.wav`` audio file into an array of type :obj:`List[float]` or a
                  :obj:`numpy.ndarray`, *e.g.* via the soundfile library (``pip install soundfile``). To prepare the
                  array into :obj:`input_features`, the :class:`~transformers.Speech2TextTokenizer` should be used for
                  extracting the fbank features, padding and conversion into a tensor of type :obj:`torch.FloatTensor`.
                  See :meth:`~transformers.Speech2TextTokenizer.__call__`
              attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in
                  ``[0, 1]``:
  
                  - 1 for tokens that are **not masked**,
                  - 0 for tokens that are **masked**.
  
                  `What are attention masks? <../glossary.html#attention-mask>`__
              head_mask (:obj:`torch.Tensor` of shape :obj:`(encoder_layers, encoder_attention_heads)`, `optional`):
                  Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``:
  
                  - 1 indicates the head is **not masked**,
                  - 0 indicates the head is **masked**.
  
              output_attentions (:obj:`bool`, `optional`):
                  Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                  returned tensors for more detail.
              output_hidden_states (:obj:`bool`, `optional`):
                  Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
                  for more detail.
              return_dict (:obj:`bool`, `optional`):
                  Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
          """
          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 attention_mask is not None:
              attention_mask = self._get_subsampled_encoder_attn_mask(attention_mask)
  
          inputs_embeds = self.conv(input_features)
          inputs_embeds = self.embed_scale * inputs_embeds
  
          if attention_mask is None:
              padding_mask = torch.zeros_like(inputs_embeds, dtype=torch.long)
          else:
              padding_mask = attention_mask.ne(1).long()
          embed_pos = self.embed_positions(padding_mask)
  
          hidden_states = inputs_embeds + embed_pos
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
  
          # expand attention_mask
          if attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
  
          encoder_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
  
          # check if head_mask has a correct number of layers specified if desired
          if head_mask is not None:
              assert head_mask.size()[0] == (
                  len(self.layers)
              ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
  
          for idx, encoder_layer in enumerate(self.layers):
              if output_hidden_states:
                  encoder_states = encoder_states + (hidden_states,)
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              dropout_probability = random.uniform(0, 1)
              if self.training and (dropout_probability < self.layerdrop):  # skip the layer
                  layer_outputs = (None, None)
              else:
                  if getattr(self.config, "gradient_checkpointing", False) and self.training:
  
                      def create_custom_forward(module):
                          def custom_forward(*inputs):
                              return module(*inputs, output_attentions)
  
                          return custom_forward
  
                      layer_outputs = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(encoder_layer),
                          hidden_states,
                          attention_mask,
                          (head_mask[idx] if head_mask is not None else None),
                      )
                  else:
                      layer_outputs = encoder_layer(
                          hidden_states,
                          attention_mask,
                          layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                          output_attentions=output_attentions,
                      )
  
                  hidden_states = layer_outputs[0]
  
              if output_attentions:
                  all_attentions = all_attentions + (layer_outputs[1],)
  
          hidden_states = self.layer_norm(hidden_states)
          if output_hidden_states:
              encoder_states = encoder_states + (hidden_states,)
  
          if not return_dict:
              return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
          return BaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
          )
  
  
  class Speech2TextDecoder(Speech2TextPreTrainedModel):
      """
      Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`Speech2TextDecoderLayer`
  
      Args:
          config: Speech2TextConfig
          embed_tokens (torch.nn.Embedding): output embedding
      """
  
      def __init__(self, config: Speech2TextConfig):
          super().__init__(config)
          self.dropout = config.dropout
          self.layerdrop = config.decoder_layerdrop
          self.padding_idx = config.pad_token_id
          self.max_target_positions = config.max_target_positions
          self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
  
          self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
  
          self.embed_positions = Speech2TextSinusoidalPositionalEmbedding(
              self.max_target_positions,
              config.d_model,
              self.padding_idx,
          )
          self.layers = nn.ModuleList([Speech2TextDecoderLayer(config) for _ in range(config.decoder_layers)])
          self.layer_norm = nn.LayerNorm(config.d_model)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embed_tokens
  
      def set_input_embeddings(self, value):
          self.embed_tokens = value
  
      def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
          # create causal mask
          # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
          combined_attention_mask = None
          if input_shape[-1] > 1:
              combined_attention_mask = _make_causal_mask(
                  input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
              ).to(self.device)
  
          if attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
              combined_attention_mask = (
                  expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
              )
  
          return combined_attention_mask
  
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          head_mask=None,
          cross_attn_head_mask=None,
          past_key_values=None,
          inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          Args:
              input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
                  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                  provide it.
  
                  Indices can be obtained using :class:`~transformers.Speech2TextTokenizer`. See
                  :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
                  for details.
  
                  `What are input IDs? <../glossary.html#input-ids>`__
              attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                  Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
                  - 1 for tokens that are **not masked**,
                  - 0 for tokens that are **masked**.
  
                  `What are attention masks? <../glossary.html#attention-mask>`__
              encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, encoder_sequence_length, hidden_size)`, `optional`):
                  Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                  of the decoder.
              encoder_attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, encoder_sequence_length)`, `optional`):
                  Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
                  selected in ``[0, 1]``:
  
                  - 1 for tokens that are **not masked**,
                  - 0 for tokens that are **masked**.
  
                  `What are attention masks? <../glossary.html#attention-mask>`__
              head_mask (:obj:`torch.Tensor` of shape :obj:`(decoder_layers, decoder_attention_heads)`, `optional`):
                  Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``:
  
                  - 1 indicates the head is **not masked**,
                  - 0 indicates the head is **masked**.
  
              cross_attn_head_mask (:obj:`torch.Tensor` of shape :obj:`(decoder_layers, decoder_attention_heads)`, `optional`):
                  Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
                  on hidden heads. Mask values selected in ``[0, 1]``:
  
                  - 1 indicates the head is **not masked**,
                  - 0 indicates the head is **masked**.
  
              past_key_values (:obj:`Tuple[Tuple[torch.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                  Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
                  decoding.
  
                  If :obj:`past_key_values` are used, the user can optionally input only the last
                  :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of
                  shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size,
                  sequence_length)`.
              inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
                  Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded
                  representation. This is useful if you want more control over how to convert :obj:`input_ids` indices
                  into associated vectors than the model's internal embedding lookup matrix.
              output_attentions (:obj:`bool`, `optional`):
                  Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                  returned tensors for more detail.
              output_hidden_states (:obj:`bool`, `optional`):
                  Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
                  for more detail.
              return_dict (:obj:`bool`, `optional`):
                  Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
          """
          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
          )
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # retrieve input_ids and inputs_embeds
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = input_ids.size()
              input_ids = input_ids.view(-1, input_shape[-1])
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
  
          # past_key_values_length
          past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
  
          if inputs_embeds is None:
              inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
  
          attention_mask = self._prepare_decoder_attention_mask(
              attention_mask, input_shape, inputs_embeds, past_key_values_length
          )
  
          # expand encoder attention mask
          if encoder_hidden_states is not None and encoder_attention_mask is not None:
              encoder_attention_mask = self._get_subsampled_encoder_attn_mask(encoder_attention_mask)
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
  
          # embed positions
          positions = self.embed_positions(input_ids, past_key_values_length=past_key_values_length)
  
          hidden_states = inputs_embeds + positions
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
  
          # decoder layers
          all_hidden_states = () if output_hidden_states else None
          all_self_attns = () if output_attentions else None
          all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
          next_decoder_cache = () if use_cache else None
  
          # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
          for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
              if attn_mask is not None:
                  assert attn_mask.size()[0] == (
                      len(self.layers)
                  ), f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
          for idx, decoder_layer in enumerate(self.layers):
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              if output_hidden_states:
                  all_hidden_states += (hidden_states,)
              dropout_probability = random.uniform(0, 1)
              if self.training and (dropout_probability < self.layerdrop):
                  continue
  
              past_key_value = past_key_values[idx] if past_key_values is not None else None
  
              if getattr(self.config, "gradient_checkpointing", False) and self.training:
  
                  if use_cache:
                      logger.warning(
                          "`use_cache = True` is incompatible with `config.gradient_checkpointing = True`. Setting `use_cache = False`..."
                      )
                      use_cache = False
  
                  def create_custom_forward(module):
                      def custom_forward(*inputs):
                          # None for past_key_value
                          return module(*inputs, output_attentions, use_cache)
  
                      return custom_forward
  
                  layer_outputs = torch.utils.checkpoint.checkpoint(
                      create_custom_forward(decoder_layer),
                      hidden_states,
                      attention_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                      head_mask[idx] if head_mask is not None else None,
                      cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
                      None,
                  )
              else:
  
                  layer_outputs = decoder_layer(
                      hidden_states,
                      attention_mask=attention_mask,
                      encoder_hidden_states=encoder_hidden_states,
                      encoder_attention_mask=encoder_attention_mask,
                      layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                      cross_attn_layer_head_mask=(
                          cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
                      ),
                      past_key_value=past_key_value,
                      output_attentions=output_attentions,
                      use_cache=use_cache,
                  )
              hidden_states = layer_outputs[0]
  
              if use_cache:
                  next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
  
              if output_attentions:
                  all_self_attns += (layer_outputs[1],)
  
                  if encoder_hidden_states is not None:
                      all_cross_attentions += (layer_outputs[2],)
  
          hidden_states = self.layer_norm(hidden_states)
          # add hidden states from the last decoder layer
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          next_cache = next_decoder_cache if use_cache else None
          if not return_dict:
              return tuple(
                  v
                  for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
                  if v is not None
              )
          return BaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=hidden_states,
              past_key_values=next_cache,
              hidden_states=all_hidden_states,
              attentions=all_self_attns,
              cross_attentions=all_cross_attentions,
          )
  
  
  @add_start_docstrings(
      "The bare Speech2Text Model outputting raw hidden-states without any specific head on top.",
      SPEECH_TO_TEXT_START_DOCSTRING,
  )
  class Speech2TextModel(Speech2TextPreTrainedModel):
      def __init__(self, config: Speech2TextConfig):
          super().__init__(config)
  
          self.encoder = Speech2TextEncoder(config)
          self.decoder = Speech2TextDecoder(config)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.decoder.embed_tokens
  
      def set_input_embeddings(self, value):
          self.decoder.embed_tokens = value
  
      def get_encoder(self):
          return self.encoder
  
      def get_decoder(self):
          return self.decoder
  
      @add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="s2t_transformer_s",
          output_type=Seq2SeqModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_features=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          head_mask=None,
          decoder_head_mask=None,
          cross_attn_head_mask=None,
          encoder_outputs=None,
          past_key_values=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          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
          )
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if encoder_outputs is None:
              encoder_outputs = self.encoder(
                  input_features,
                  attention_mask=attention_mask,
                  head_mask=head_mask,
                  output_attentions=output_attentions,
                  output_hidden_states=output_hidden_states,
                  return_dict=return_dict,
              )
          # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
          elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
              encoder_outputs = BaseModelOutput(
                  last_hidden_state=encoder_outputs[0],
                  hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                  attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
              )
  
          # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
          decoder_outputs = self.decoder(
              input_ids=decoder_input_ids,
              attention_mask=decoder_attention_mask,
              encoder_hidden_states=encoder_outputs[0],
              encoder_attention_mask=attention_mask,
              head_mask=decoder_head_mask,
              cross_attn_head_mask=cross_attn_head_mask,
              past_key_values=past_key_values,
              inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          if not return_dict:
              return decoder_outputs + encoder_outputs
  
          return Seq2SeqModelOutput(
              last_hidden_state=decoder_outputs.last_hidden_state,
              past_key_values=decoder_outputs.past_key_values,
              decoder_hidden_states=decoder_outputs.hidden_states,
              decoder_attentions=decoder_outputs.attentions,
              cross_attentions=decoder_outputs.cross_attentions,
              encoder_last_hidden_state=encoder_outputs.last_hidden_state,
              encoder_hidden_states=encoder_outputs.hidden_states,
              encoder_attentions=encoder_outputs.attentions,
          )
  
  
  @add_start_docstrings(
      "The Speech2Text Model with a language modeling head. Can be used for summarization.",
      SPEECH_TO_TEXT_START_DOCSTRING,
  )
  class Speech2TextForConditionalGeneration(Speech2TextPreTrainedModel):
      base_model_prefix = "model"
      _keys_to_ignore_on_load_missing = [
          r"encoder\.version",
          r"decoder\.version",
          r"model.encoder.embed_positions.weights",
          r"model.decoder.embed_positions.weights",
      ]
      _keys_to_ignore_on_save = [
          r"model.encoder.embed_positions.weights",
          r"model.decoder.embed_positions.weights",
      ]
  
      def __init__(self, config: Speech2TextConfig):
          super().__init__(config)
          self.model = Speech2TextModel(config)
          self.lm_head = nn.Linear(config.d_model, self.config.vocab_size, bias=False)
  
          self.init_weights()
  
      def get_encoder(self):
          return self.model.get_encoder()
  
      def get_decoder(self):
          return self.model.get_decoder()
  
      def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
          new_embeddings = super().resize_token_embeddings(new_num_tokens)
          return new_embeddings
  
      def get_output_embeddings(self):
          return self.lm_head
  
      def set_output_embeddings(self, new_embeddings):
          self.lm_head = new_embeddings
  
      @add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_features=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          head_mask=None,
          decoder_head_mask=None,
          cross_attn_head_mask=None,
          encoder_outputs=None,
          past_key_values=None,
          decoder_inputs_embeds=None,
          labels=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the language modeling loss. Indices should either be in ``[0, ...,
              config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored
              (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.
  
          Returns:
  
          Example::
  
              >>> import torch
              >>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
              >>> from datasets import load_dataset
              >>> import soundfile as sf
  
              >>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
              >>> processor = Speech2Textprocessor.from_pretrained("facebook/s2t-small-librispeech-asr")
  
              >>> def map_to_array(batch):
              >>>     speech, _ = sf.read(batch["file"])
              >>>     batch["speech"] = speech
              >>>     return batch
  
              >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
              >>> ds = ds.map(map_to_array)
  
              >>> input_features = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt").input_features  # Batch size 1
              >>> generated_ids = model.generate(input_ids=input_features)
  
              >>> transcription = processor.batch_decode(generated_ids)
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if labels is not None:
              if decoder_input_ids is None:
                  decoder_input_ids = shift_tokens_right(
                      labels, self.config.pad_token_id, self.config.decoder_start_token_id
                  )
  
          outputs = self.model(
              input_features,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              encoder_outputs=encoder_outputs,
              decoder_attention_mask=decoder_attention_mask,
              head_mask=head_mask,
              decoder_head_mask=decoder_head_mask,
              cross_attn_head_mask=cross_attn_head_mask,
              past_key_values=past_key_values,
              decoder_inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          lm_logits = self.lm_head(outputs[0])
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (lm_logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return Seq2SeqLMOutput(
              loss=loss,
              logits=lm_logits,
              past_key_values=outputs.past_key_values,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  
      def prepare_inputs_for_generation(
          self,
          decoder_input_ids,
          past=None,
          attention_mask=None,
          head_mask=None,
          use_cache=None,
          encoder_outputs=None,
          **kwargs
      ):
          # cut decoder_input_ids if past is used
          if past is not None:
              decoder_input_ids = decoder_input_ids[:, -1:]
  
          return {
              "encoder_outputs": encoder_outputs,
              "past_key_values": past,
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": attention_mask,
              "head_mask": head_mask,
              "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
          }
  
      @staticmethod
      def _reorder_cache(past, beam_idx):
          reordered_past = ()
          for layer_past in past:
              reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
          return reordered_past
  