text: # coding=utf-8
  # Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan 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 LED model. """
  
  
  import math
  import random
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  
  import torch
  import torch.nn.functional as F
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  
  from ...activations import ACT2FN
  from ...file_utils import (
      ModelOutput,
      add_code_sample_docstrings,
      add_end_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_outputs import (
      BaseModelOutputWithPastAndCrossAttentions,
      Seq2SeqLMOutput,
      Seq2SeqModelOutput,
      Seq2SeqQuestionAnsweringModelOutput,
      Seq2SeqSequenceClassifierOutput,
  )
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_led import LEDConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "LEDConfig"
  _TOKENIZER_FOR_DOC = "LEDTokenizer"
  
  
  LED_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "allenai/led-base-16384",
      # See all LED models at https://huggingface.co/models?filter=led
  ]
  
  
  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, "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
  
  
  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)
  
  
  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
      expanded_attention_mask = inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
  
      # make sure that global_attn_mask is positive
      expanded_attention_mask = expanded_attention_mask * inverted_mask
  
      return expanded_attention_mask
  
  
  class LEDLearnedPositionalEmbedding(nn.Embedding):
      """
      This module learns positional embeddings up to a fixed maximum size.
      """
  
      def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
          assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
          super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)
  
      def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
          """`input_ids_shape` is expected to be [bsz x seqlen]."""
          bsz, seq_len = input_ids_shape[:2]
          positions = torch.arange(
              past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
          )
          return super().forward(positions)
  
  
  # Copied from transformers.models.longformer.modeling_longformer.LongformerSelfAttention with Longformer->LEDEncoder
  class LEDEncoderSelfAttention(nn.Module):
      def __init__(self, config, layer_id):
          super().__init__()
          if config.hidden_size % config.num_attention_heads != 0:
              raise ValueError(
                  "The hidden size (%d) is not a multiple of the number of attention "
                  "heads (%d)" % (config.hidden_size, config.num_attention_heads)
              )
          self.num_heads = config.num_attention_heads
          self.head_dim = int(config.hidden_size / config.num_attention_heads)
          self.embed_dim = config.hidden_size
  
          self.query = nn.Linear(config.hidden_size, self.embed_dim)
          self.key = nn.Linear(config.hidden_size, self.embed_dim)
          self.value = nn.Linear(config.hidden_size, self.embed_dim)
  
          # separate projection layers for tokens with global attention
          self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
          self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
          self.value_global = nn.Linear(config.hidden_size, self.embed_dim)
  
          self.dropout = config.attention_probs_dropout_prob
  
          self.layer_id = layer_id
          attention_window = config.attention_window[self.layer_id]
          assert (
              attention_window % 2 == 0
          ), f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
          assert (
              attention_window > 0
          ), f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
  
          self.one_sided_attn_window_size = attention_window // 2
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          is_index_masked=None,
          is_index_global_attn=None,
          is_global_attn=None,
          output_attentions=False,
      ):
          """
          :class:`LEDEncoderSelfAttention` expects `len(hidden_states)` to be multiple of `attention_window`. Padding to
          `attention_window` happens in :meth:`LEDEncoderModel.forward` to avoid redoing the padding on each layer.
  
          The `attention_mask` is changed in :meth:`LEDEncoderModel.forward` from 0, 1, 2 to:
  
              * -10000: no attention
              * 0: local attention
              * +10000: global attention
          """
          hidden_states = hidden_states.transpose(0, 1)
  
          # project hidden states
          query_vectors = self.query(hidden_states)
          key_vectors = self.key(hidden_states)
          value_vectors = self.value(hidden_states)
  
          seq_len, batch_size, embed_dim = hidden_states.size()
          assert (
              embed_dim == self.embed_dim
          ), f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}"
  
          # normalize query
          query_vectors /= math.sqrt(self.head_dim)
  
          query_vectors = query_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
          key_vectors = key_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
  
          attn_scores = self._sliding_chunks_query_key_matmul(
              query_vectors, key_vectors, self.one_sided_attn_window_size
          )
  
          # values to pad for attention probs
          remove_from_windowed_attention_mask = (attention_mask != 0)[:, :, None, None]
  
          # cast to fp32/fp16 then replace 1's with -inf
          float_mask = remove_from_windowed_attention_mask.type_as(query_vectors).masked_fill(
              remove_from_windowed_attention_mask, -10000.0
          )
          # diagonal mask with zeros everywhere and -inf inplace of padding
          diagonal_mask = self._sliding_chunks_query_key_matmul(
              float_mask.new_ones(size=float_mask.size()), float_mask, self.one_sided_attn_window_size
          )
  
          # pad local attention probs
          attn_scores += diagonal_mask
  
          assert list(attn_scores.size()) == [
              batch_size,
              seq_len,
              self.num_heads,
              self.one_sided_attn_window_size * 2 + 1,
          ], f"local_attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads}, {self.one_sided_attn_window_size * 2 + 1}), but is of size {attn_scores.size()}"
  
          # compute local attention probs from global attention keys and contact over window dim
          if is_global_attn:
              # compute global attn indices required through out forward fn
              (
                  max_num_global_attn_indices,
                  is_index_global_attn_nonzero,
                  is_local_index_global_attn_nonzero,
                  is_local_index_no_global_attn_nonzero,
              ) = self._get_global_attn_indices(is_index_global_attn)
              # calculate global attn probs from global key
  
              global_key_attn_scores = self._concat_with_global_key_attn_probs(
                  query_vectors=query_vectors,
                  key_vectors=key_vectors,
                  max_num_global_attn_indices=max_num_global_attn_indices,
                  is_index_global_attn_nonzero=is_index_global_attn_nonzero,
                  is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
                  is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
              )
              # concat to local_attn_probs
              # (batch_size, seq_len, num_heads, extra attention count + 2*window+1)
              attn_scores = torch.cat((global_key_attn_scores, attn_scores), dim=-1)
  
              # free memory
              del global_key_attn_scores
  
          attn_probs = F.softmax(attn_scores, dim=-1, dtype=torch.float32)  # use fp32 for numerical stability
  
          # softmax sometimes inserts NaN if all positions are masked, replace them with 0
          attn_probs = torch.masked_fill(attn_probs, is_index_masked[:, :, None, None], 0.0)
          attn_probs = attn_probs.type_as(attn_scores)
  
          # free memory
          del attn_scores
  
          # apply dropout
          attn_probs = F.dropout(attn_probs, p=self.dropout, training=self.training)
  
          value_vectors = value_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
  
          # compute local attention output with global attention value and add
          if is_global_attn:
              # compute sum of global and local attn
              attn_output = self._compute_attn_output_with_global_indices(
                  value_vectors=value_vectors,
                  attn_probs=attn_probs,
                  max_num_global_attn_indices=max_num_global_attn_indices,
                  is_index_global_attn_nonzero=is_index_global_attn_nonzero,
                  is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
              )
          else:
              # compute local attn only
              attn_output = self._sliding_chunks_matmul_attn_probs_value(
                  attn_probs, value_vectors, self.one_sided_attn_window_size
              )
  
          assert attn_output.size() == (batch_size, seq_len, self.num_heads, self.head_dim), "Unexpected size"
          attn_output = attn_output.transpose(0, 1).reshape(seq_len, batch_size, embed_dim).contiguous()
  
          # compute value for global attention and overwrite to attention output
          # TODO: remove the redundant computation
          if is_global_attn:
              global_attn_output, global_attn_probs = self._compute_global_attn_output_from_hidden(
                  hidden_states=hidden_states,
                  max_num_global_attn_indices=max_num_global_attn_indices,
                  is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
                  is_index_global_attn_nonzero=is_index_global_attn_nonzero,
                  is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
                  is_index_masked=is_index_masked,
              )
  
              # get only non zero global attn output
              nonzero_global_attn_output = global_attn_output[
                  is_local_index_global_attn_nonzero[0], :, is_local_index_global_attn_nonzero[1]
              ]
  
              # overwrite values with global attention
              attn_output[is_index_global_attn_nonzero[::-1]] = nonzero_global_attn_output.view(
                  len(is_local_index_global_attn_nonzero[0]), -1
              )
              # The attention weights for tokens with global attention are
              # just filler values, they were never used to compute the output.
              # Fill with 0 now, the correct values are in 'global_attn_probs'.
              attn_probs[is_index_global_attn_nonzero] = 0
  
          outputs = (attn_output.transpose(0, 1),)
  
          if output_attentions:
              outputs += (attn_probs,)
  
          return outputs + (global_attn_probs,) if (is_global_attn and output_attentions) else outputs
  
      @staticmethod
      def _pad_and_transpose_last_two_dims(hidden_states_padded, padding):
          """pads rows and then flips rows and columns"""
          hidden_states_padded = F.pad(
              hidden_states_padded, padding
          )  # padding value is not important because it will be overwritten
          hidden_states_padded = hidden_states_padded.view(
              *hidden_states_padded.size()[:-2], hidden_states_padded.size(-1), hidden_states_padded.size(-2)
          )
          return hidden_states_padded
  
      @staticmethod
      def _pad_and_diagonalize(chunked_hidden_states):
          """
          shift every row 1 step right, converting columns into diagonals.
  
          Example::
  
                chunked_hidden_states: [ 0.4983,  2.6918, -0.0071,  1.0492,
                                         -1.8348,  0.7672,  0.2986,  0.0285,
                                         -0.7584,  0.4206, -0.0405,  0.1599,
                                         2.0514, -1.1600,  0.5372,  0.2629 ]
                window_overlap = num_rows = 4
               (pad & diagonalize) =>
               [ 0.4983,  2.6918, -0.0071,  1.0492, 0.0000,  0.0000,  0.0000
                 0.0000,  -1.8348,  0.7672,  0.2986,  0.0285, 0.0000,  0.0000
                 0.0000,  0.0000, -0.7584,  0.4206, -0.0405,  0.1599, 0.0000
                 0.0000,  0.0000,  0.0000, 2.0514, -1.1600,  0.5372,  0.2629 ]
          """
          total_num_heads, num_chunks, window_overlap, hidden_dim = chunked_hidden_states.size()
          chunked_hidden_states = F.pad(
              chunked_hidden_states, (0, window_overlap + 1)
          )  # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
          chunked_hidden_states = chunked_hidden_states.view(
              total_num_heads, num_chunks, -1
          )  # total_num_heads x num_chunks x window_overlap*window_overlap+window_overlap
          chunked_hidden_states = chunked_hidden_states[
              :, :, :-window_overlap
          ]  # total_num_heads x num_chunks x window_overlap*window_overlap
          chunked_hidden_states = chunked_hidden_states.view(
              total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim
          )
          chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
          return chunked_hidden_states
  
      @staticmethod
      def _chunk(hidden_states, window_overlap):
          """convert into overlapping chunks. Chunk size = 2w, overlap size = w"""
  
          # non-overlapping chunks of size = 2w
          hidden_states = hidden_states.view(
              hidden_states.size(0),
              hidden_states.size(1) // (window_overlap * 2),
              window_overlap * 2,
              hidden_states.size(2),
          )
  
          # use `as_strided` to make the chunks overlap with an overlap size = window_overlap
          chunk_size = list(hidden_states.size())
          chunk_size[1] = chunk_size[1] * 2 - 1
  
          chunk_stride = list(hidden_states.stride())
          chunk_stride[1] = chunk_stride[1] // 2
          return hidden_states.as_strided(size=chunk_size, stride=chunk_stride)
  
      @staticmethod
      def _mask_invalid_locations(input_tensor, affected_seq_len) -> torch.Tensor:
          beginning_mask_2d = input_tensor.new_ones(affected_seq_len, affected_seq_len + 1).tril().flip(dims=[0])
          beginning_mask = beginning_mask_2d[None, :, None, :]
          ending_mask = beginning_mask.flip(dims=(1, 3))
          beginning_input = input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1]
          beginning_mask = beginning_mask.expand(beginning_input.size())
          beginning_input.masked_fill_(beginning_mask == 1, -float("inf"))  # `== 1` converts to bool or uint8
          ending_input = input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :]
          ending_mask = ending_mask.expand(ending_input.size())
          ending_input.masked_fill_(ending_mask == 1, -float("inf"))  # `== 1` converts to bool or uint8
  
      def _sliding_chunks_query_key_matmul(self, query: torch.Tensor, key: torch.Tensor, window_overlap: int):
          """
          Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
          implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained LEDEncoder) with an
          overlap of size window_overlap
          """
          batch_size, seq_len, num_heads, head_dim = query.size()
          assert (
              seq_len % (window_overlap * 2) == 0
          ), f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}"
          assert query.size() == key.size()
  
          chunks_count = seq_len // window_overlap - 1
  
          # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
          query = query.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
          key = key.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
  
          query = self._chunk(query, window_overlap)
          key = self._chunk(key, window_overlap)
  
          # matrix multiplication
          # bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
          # bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
          # bcxy: batch_size * num_heads x chunks x 2window_overlap x window_overlap
          diagonal_chunked_attention_scores = torch.einsum("bcxd,bcyd->bcxy", (query, key))  # multiply
  
          # convert diagonals into columns
          diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(
              diagonal_chunked_attention_scores, padding=(0, 0, 0, 1)
          )
  
          # allocate space for the overall attention matrix where the chunks are combined. The last dimension
          # has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
          # window_overlap previous words). The following column is attention score from each word to itself, then
          # followed by window_overlap columns for the upper triangle.
  
          diagonal_attention_scores = diagonal_chunked_attention_scores.new_empty(
              (batch_size * num_heads, chunks_count + 1, window_overlap, window_overlap * 2 + 1)
          )
  
          # copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
          # - copying the main diagonal and the upper triangle
          diagonal_attention_scores[:, :-1, :, window_overlap:] = diagonal_chunked_attention_scores[
              :, :, :window_overlap, : window_overlap + 1
          ]
          diagonal_attention_scores[:, -1, :, window_overlap:] = diagonal_chunked_attention_scores[
              :, -1, window_overlap:, : window_overlap + 1
          ]
          # - copying the lower triangle
          diagonal_attention_scores[:, 1:, :, :window_overlap] = diagonal_chunked_attention_scores[
              :, :, -(window_overlap + 1) : -1, window_overlap + 1 :
          ]
  
          diagonal_attention_scores[:, 0, 1:window_overlap, 1:window_overlap] = diagonal_chunked_attention_scores[
              :, 0, : window_overlap - 1, 1 - window_overlap :
          ]
  
          # separate batch_size and num_heads dimensions again
          diagonal_attention_scores = diagonal_attention_scores.view(
              batch_size, num_heads, seq_len, 2 * window_overlap + 1
          ).transpose(2, 1)
  
          self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
          return diagonal_attention_scores
  
      def _sliding_chunks_matmul_attn_probs_value(
          self, attn_probs: torch.Tensor, value: torch.Tensor, window_overlap: int
      ):
          """
          Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
          same shape as `attn_probs`
          """
          batch_size, seq_len, num_heads, head_dim = value.size()
  
          assert seq_len % (window_overlap * 2) == 0
          assert attn_probs.size()[:3] == value.size()[:3]
          assert attn_probs.size(3) == 2 * window_overlap + 1
          chunks_count = seq_len // window_overlap - 1
          # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap
  
          chunked_attn_probs = attn_probs.transpose(1, 2).reshape(
              batch_size * num_heads, seq_len // window_overlap, window_overlap, 2 * window_overlap + 1
          )
  
          # group batch_size and num_heads dimensions into one
          value = value.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
  
          # pad seq_len with w at the beginning of the sequence and another window overlap at the end
          padded_value = F.pad(value, (0, 0, window_overlap, window_overlap), value=-1)
  
          # chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
          chunked_value_size = (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim)
          chunked_value_stride = padded_value.stride()
          chunked_value_stride = (
              chunked_value_stride[0],
              window_overlap * chunked_value_stride[1],
              chunked_value_stride[1],
              chunked_value_stride[2],
          )
          chunked_value = padded_value.as_strided(size=chunked_value_size, stride=chunked_value_stride)
  
          chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
  
          context = torch.einsum("bcwd,bcdh->bcwh", (chunked_attn_probs, chunked_value))
          return context.view(batch_size, num_heads, seq_len, head_dim).transpose(1, 2)
  
      @staticmethod
      def _get_global_attn_indices(is_index_global_attn):
          """ compute global attn indices required throughout forward pass """
          # helper variable
          num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
  
          # max number of global attn indices in batch
          max_num_global_attn_indices = num_global_attn_indices.max()
  
          # indices of global attn
          is_index_global_attn_nonzero = is_index_global_attn.nonzero(as_tuple=True)
  
          # helper variable
          is_local_index_global_attn = torch.arange(
              max_num_global_attn_indices, device=is_index_global_attn.device
          ) < num_global_attn_indices.unsqueeze(dim=-1)
  
          # location of the non-padding values within global attention indices
          is_local_index_global_attn_nonzero = is_local_index_global_attn.nonzero(as_tuple=True)
  
          # location of the padding values within global attention indices
          is_local_index_no_global_attn_nonzero = (is_local_index_global_attn == 0).nonzero(as_tuple=True)
          return (
              max_num_global_attn_indices,
              is_index_global_attn_nonzero,
              is_local_index_global_attn_nonzero,
              is_local_index_no_global_attn_nonzero,
          )
  
      def _concat_with_global_key_attn_probs(
          self,
          key_vectors,
          query_vectors,
          max_num_global_attn_indices,
          is_index_global_attn_nonzero,
          is_local_index_global_attn_nonzero,
          is_local_index_no_global_attn_nonzero,
      ):
          batch_size = key_vectors.shape[0]
  
          # create only global key vectors
          key_vectors_only_global = key_vectors.new_zeros(
              batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
          )
  
          key_vectors_only_global[is_local_index_global_attn_nonzero] = key_vectors[is_index_global_attn_nonzero]
  
          # (batch_size, seq_len, num_heads, max_num_global_attn_indices)
          attn_probs_from_global_key = torch.einsum("blhd,bshd->blhs", (query_vectors, key_vectors_only_global))
  
          attn_probs_from_global_key[
              is_local_index_no_global_attn_nonzero[0], :, :, is_local_index_no_global_attn_nonzero[1]
          ] = -10000.0
  
          return attn_probs_from_global_key
  
      def _compute_attn_output_with_global_indices(
          self,
          value_vectors,
          attn_probs,
          max_num_global_attn_indices,
          is_index_global_attn_nonzero,
          is_local_index_global_attn_nonzero,
      ):
          batch_size = attn_probs.shape[0]
  
          # cut local attn probs to global only
          attn_probs_only_global = attn_probs.narrow(-1, 0, max_num_global_attn_indices)
          # get value vectors for global only
          value_vectors_only_global = value_vectors.new_zeros(
              batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
          )
          value_vectors_only_global[is_local_index_global_attn_nonzero] = value_vectors[is_index_global_attn_nonzero]
  
          # use `matmul` because `einsum` crashes sometimes with fp16
          # attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
          # compute attn output only global
          attn_output_only_global = torch.matmul(
              attn_probs_only_global.transpose(1, 2), value_vectors_only_global.transpose(1, 2)
          ).transpose(1, 2)
  
          # reshape attn probs
          attn_probs_without_global = attn_probs.narrow(
              -1, max_num_global_attn_indices, attn_probs.size(-1) - max_num_global_attn_indices
          ).contiguous()
  
          # compute attn output with global
          attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(
              attn_probs_without_global, value_vectors, self.one_sided_attn_window_size
          )
          return attn_output_only_global + attn_output_without_global
  
      def _compute_global_attn_output_from_hidden(
          self,
          hidden_states,
          max_num_global_attn_indices,
          is_local_index_global_attn_nonzero,
          is_index_global_attn_nonzero,
          is_local_index_no_global_attn_nonzero,
          is_index_masked,
      ):
          seq_len, batch_size = hidden_states.shape[:2]
  
          # prepare global hidden states
          global_attn_hidden_states = hidden_states.new_zeros(max_num_global_attn_indices, batch_size, self.embed_dim)
          global_attn_hidden_states[is_local_index_global_attn_nonzero[::-1]] = hidden_states[
              is_index_global_attn_nonzero[::-1]
          ]
  
          # global key, query, value
          global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
          global_key_vectors = self.key_global(hidden_states)
          global_value_vectors = self.value_global(hidden_states)
  
          # normalize
          global_query_vectors_only_global /= math.sqrt(self.head_dim)
  
          # reshape
          global_query_vectors_only_global = (
              global_query_vectors_only_global.contiguous()
              .view(max_num_global_attn_indices, batch_size * self.num_heads, self.head_dim)
              .transpose(0, 1)
          )  # (batch_size * self.num_heads, max_num_global_attn_indices, head_dim)
          global_key_vectors = (
              global_key_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
          )  # batch_size * self.num_heads, seq_len, head_dim)
          global_value_vectors = (
              global_value_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
          )  # batch_size * self.num_heads, seq_len, head_dim)
  
          # compute attn scores
          global_attn_scores = torch.bmm(global_query_vectors_only_global, global_key_vectors.transpose(1, 2))
  
          assert list(global_attn_scores.size()) == [
              batch_size * self.num_heads,
              max_num_global_attn_indices,
              seq_len,
          ], f"global_attn_scores have the wrong size. Size should be {(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)}, but is {global_attn_scores.size()}."
  
          global_attn_scores = global_attn_scores.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
  
          global_attn_scores[
              is_local_index_no_global_attn_nonzero[0], :, is_local_index_no_global_attn_nonzero[1], :
          ] = -10000.0
  
          global_attn_scores = global_attn_scores.masked_fill(
              is_index_masked[:, None, None, :],
              -10000.0,
          )
  
          global_attn_scores = global_attn_scores.view(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
  
          # compute global attn probs
          global_attn_probs_float = F.softmax(
              global_attn_scores, dim=-1, dtype=torch.float32
          )  # use fp32 for numerical stability
  
          global_attn_probs = F.dropout(
              global_attn_probs_float.type_as(global_attn_scores), p=self.dropout, training=self.training
          )
  
          # global attn output
          global_attn_output = torch.bmm(global_attn_probs, global_value_vectors)
  
          assert list(global_attn_output.size()) == [
              batch_size * self.num_heads,
              max_num_global_attn_indices,
              self.head_dim,
          ], f"global_attn_output tensor has the wrong size. Size should be {(batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim)}, but is {global_attn_output.size()}."
  
          global_attn_probs = global_attn_probs.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
          global_attn_output = global_attn_output.view(
              batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim
          )
          return global_attn_output, global_attn_probs
  
  
  class LEDEncoderAttention(nn.Module):
      def __init__(self, config, layer_id):
          super().__init__()
          self.longformer_self_attn = LEDEncoderSelfAttention(config, layer_id=layer_id)
          self.output = nn.Linear(config.d_model, config.d_model)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          is_index_masked: Optional[torch.Tensor] = None,
          is_index_global_attn: Optional[torch.Tensor] = None,
          is_global_attn: Optional[bool] = None,
          output_attentions: bool = False,
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
          """Input shape: Batch x Time x Channel"""
  
          self_outputs = self.longformer_self_attn(
              hidden_states=hidden_states,
              attention_mask=attention_mask,
              is_index_masked=is_index_masked,
              is_index_global_attn=is_index_global_attn,
              is_global_attn=is_global_attn,
              output_attentions=output_attentions,
          )
  
          attn_output = self.output(self_outputs[0])
          outputs = (attn_output,) + self_outputs[1:]
  
          return outputs
  
  
  class LEDDecoderAttention(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,
          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))
  
          assert attn_weights.size() == (
              bsz * self.num_heads,
              tgt_len,
              src_len,
          ), 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:
              assert attention_mask.size() == (
                  bsz,
                  1,
                  tgt_len,
                  src_len,
              ), 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 output_attentions:
              # this operation is a bit akward, but it's required to
              # make sure that attn_weights keeps its gradient.
              # In order to do so, attn_weights have to 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)
  
          assert attn_output.size() == (
              bsz * self.num_heads,
              tgt_len,
              self.head_dim,
          ), 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)
              .transpose(1, 2)
              .reshape(bsz, tgt_len, embed_dim)
          )
  
          attn_output = self.out_proj(attn_output)
  
          return attn_output, attn_weights_reshaped, past_key_value
  
  
  class LEDEncoderLayer(nn.Module):
      def __init__(self, config: LEDConfig, layer_id: int):
          super().__init__()
          self.embed_dim = config.d_model
          self.self_attn = LEDEncoderAttention(config, layer_id)
          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,
          is_index_masked=None,
          is_index_global_attn=None,
          is_global_attn=None,
          output_attentions=False,
      ):
          """
          Args:
              hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
              attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
          """
          residual = hidden_states
          attn_outputs = self.self_attn(
              hidden_states=hidden_states,
              attention_mask=attention_mask,
              is_index_masked=is_index_masked,
              is_index_global_attn=is_index_global_attn,
              is_global_attn=is_global_attn,
              output_attentions=output_attentions,
          )
          hidden_states = attn_outputs[0]
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          residual = 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
          hidden_states = self.final_layer_norm(hidden_states)
  
          if 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)
          return (hidden_states,) + attn_outputs[1:]
  
  
  class LEDDecoderLayer(nn.Module):
      def __init__(self, config: LEDConfig):
          super().__init__()
          self.embed_dim = config.d_model
  
          self.self_attn = LEDDecoderAttention(
              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 = LEDDecoderAttention(
              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,
          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 `(seq_len, batch, embed_dim)`
              attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                  `(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 `(seq_len, batch, embed_dim)`
              encoder_attention_mask (:obj:`torch.FloatTensor`): encoder attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              past_key_value (:obj:`Tuple(torch.FloatTensor)`): cached past key and value projection states
              output_attentions (:obj:`bool`): Whether the base model outputs attentions.
                  This requires the attentions tensor to be reshaped in this function.
          """
          residual = 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,
              output_attentions=output_attentions,
          )
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          hidden_states = self.self_attn_layer_norm(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
  
              # 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,
                  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
              hidden_states = self.encoder_attn_layer_norm(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.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
          hidden_states = self.final_layer_norm(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 LEDClassificationHead(nn.Module):
      """Head for sentence-level classification tasks."""
  
      def __init__(
          self,
          input_dim: int,
          inner_dim: int,
          num_classes: int,
          pooler_dropout: float,
      ):
          super().__init__()
          self.dense = nn.Linear(input_dim, inner_dim)
          self.dropout = nn.Dropout(p=pooler_dropout)
          self.out_proj = nn.Linear(inner_dim, num_classes)
  
      def forward(self, hidden_states: torch.Tensor):
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.dense(hidden_states)
          hidden_states = torch.tanh(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.out_proj(hidden_states)
          return hidden_states
  
  
  class LEDPreTrainedModel(PreTrainedModel):
      config_class = LEDConfig
      base_model_prefix = "led"
  
      def _init_weights(self, module):
          std = self.config.init_std
          if isinstance(module, nn.Linear):
              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_()
  
      @property
      def dummy_inputs(self):
          pad_token = self.config.pad_token_id
          input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
          dummy_inputs = {
              "attention_mask": input_ids.ne(pad_token),
              "input_ids": input_ids,
          }
          return dummy_inputs
  
  
  @dataclass
  # Copied from transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput with Longformer->LEDEncoder
  class LEDEncoderBaseModelOutput(ModelOutput):
      """
      Base class for LEDEncoder's outputs, with potential hidden states, local and global attentions.
  
      Args:
          last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, x + attention_window + 1)`, where ``x`` is the number of tokens with global attention
              mask.
  
              Local attentions weights after the attention softmax, used to compute the weighted average in the
              self-attention heads. Those are the attention weights from every token in the sequence to every token with
              global attention (first ``x`` values) and to every token in the attention window (remaining
              ``attention_window + 1`` values). Note that the first ``x`` values refer to tokens with fixed positions in
              the text, but the remaining ``attention_window + 1`` values refer to tokens with relative positions: the
              attention weight of a token to itself is located at index ``x + attention_window / 2`` and the
              ``attention_window / 2`` preceding (succeeding) values are the attention weights to the ``attention_window
              / 2`` preceding (succeeding) tokens. If the attention window contains a token with global attention, the
              attention weight at the corresponding index is set to 0; the value should be accessed from the first ``x``
              attention weights. If a token has global attention, the attention weights to all other tokens in
              :obj:`attentions` is set to 0, the values should be accessed from :obj:`global_attentions`.
          global_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, x)`, where ``x`` is the number of tokens with global attention mask.
  
              Global attentions weights after the attention softmax, used to compute the weighted average in the
              self-attention heads. Those are the attention weights from every token with global attention to every token
              in the sequence.
      """
  
      last_hidden_state: torch.FloatTensor
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
      global_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class LEDSeq2SeqModelOutput(ModelOutput):
      """
      Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
      decoding.
  
      Args:
          last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the decoder of the model.
  
              If :obj:`past_key_values` is used only the last hidden-state of the sequences of shape :obj:`(batch_size,
              1, hidden_size)` is output.
          past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(2,
              batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
              used (see :obj:`past_key_values` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
          decoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          encoder_last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder of the model.
          encoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
          encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          encoder_global_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, x)`, where ``x`` is the number of tokens with global attention mask.
  
              Global attentions weights after the attention softmax, used to compute the weighted average in the
              self-attention heads. Those are the attention weights from every token with global attention to every token
              in the sequence.
      """
  
      last_hidden_state: torch.FloatTensor = None
      past_key_values: Optional[List[torch.FloatTensor]] = None
      decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_last_hidden_state: Optional[torch.FloatTensor] = None
      encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_global_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class LEDSeq2SeqLMOutput(ModelOutput):
      """
      Base class for sequence-to-sequence language models outputs.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Language modeling loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(2,
              batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
              used (see :obj:`past_key_values` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
          decoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          encoder_last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder of the model.
          encoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
          encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          encoder_global_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, x)`, where ``x`` is the number of tokens with global attention mask.
  
              Global attentions weights after the attention softmax, used to compute the weighted average in the
              self-attention heads. Those are the attention weights from every token with global attention to every token
              in the sequence.
      """
  
      loss: Optional[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      past_key_values: Optional[List[torch.FloatTensor]] = None
      decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_last_hidden_state: Optional[torch.FloatTensor] = None
      encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_global_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class LEDSeq2SeqSequenceClassifierOutput(ModelOutput):
      """
      Base class for outputs of sequence-to-sequence sentence classification models.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(2,
              batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
              used (see :obj:`past_key_values` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
          decoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          encoder_last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder of the model.
          encoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
          encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          encoder_global_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, x)`, where ``x`` is the number of tokens with global attention mask.
  
              Global attentions weights after the attention softmax, used to compute the weighted average in the
              self-attention heads. Those are the attention weights from every token with global attention to every token
              in the sequence.
      """
  
      loss: Optional[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      past_key_values: Optional[List[torch.FloatTensor]] = None
      decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_last_hidden_state: Optional[torch.FloatTensor] = None
      encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_global_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class LEDSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
      """
      Base class for outputs of sequence-to-sequence question answering models.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
          start_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
              Span-start scores (before SoftMax).
          end_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
              Span-end scores (before SoftMax).
          past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(2,
              batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
              used (see :obj:`past_key_values` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
          decoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          encoder_last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder of the model.
          encoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
          encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          encoder_global_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, x)`, where ``x`` is the number of tokens with global attention mask.
  
              Global attentions weights after the attention softmax, used to compute the weighted average in the
              self-attention heads. Those are the attention weights from every token with global attention to every token
              in the sequence.
      """
  
      loss: Optional[torch.FloatTensor] = None
      start_logits: torch.FloatTensor = None
      end_logits: torch.FloatTensor = None
      past_key_values: Optional[List[torch.FloatTensor]] = None
      decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_last_hidden_state: Optional[torch.FloatTensor] = None
      encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_global_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  LED_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.LEDConfig`):
              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.
  """
  
  LED_GENERATION_EXAMPLE = r"""
      Summarization example::
  
          >>> from transformers import LEDTokenizer, LEDForConditionalGeneration, LEDConfig
  
          >>> model = LEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')
          >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384')
  
          >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
          >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
  
          >>> # Generate Summary
          >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
          >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
  """
  
  LED_INPUTS_DOCSTRING = 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.LEDTokenizer`. 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>`__
          decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Provide for translation and summarization training. By default, the model will create this tensor by
              shifting the :obj:`input_ids` to the right, following the paper.
          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.
  
              If you want to change padding behavior, you should read :func:`modeling_led._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.
          global_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to decide the attention given on each token, local attention or global attention for the encoder.
              Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is
              important for task-specific finetuning because it makes the model more flexible at representing the task.
              For example, for classification, the <s> token should be given global attention. For QA, all question
              tokens should also have global attention. Please refer to the `Longformer paper
              <https://arxiv.org/abs/2004.05150>`__ for more details. Mask values selected in ``[0, 1]``:
  
              - 0 for local attention (a sliding window attention),
              - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
          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)`.
          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.
          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 LEDEncoder(LEDPreTrainedModel):
      """
      Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
      :class:`LEDEncoderLayer`.
  
      Args:
          config: LEDConfig
          embed_tokens (torch.nn.Embedding): output embedding
      """
  
      def __init__(self, config: LEDConfig, embed_tokens: Optional[nn.Embedding] = None):
          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_encoder_position_embeddings
  
          if isinstance(config.attention_window, int):
              assert config.attention_window % 2 == 0, "`config.attention_window` has to be an even value"
              assert config.attention_window > 0, "`config.attention_window` has to be positive"
              config.attention_window = [config.attention_window] * config.num_hidden_layers  # one value per layer
          else:
              assert len(config.attention_window) == config.num_hidden_layers, (
                  "`len(config.attention_window)` should equal `config.num_hidden_layers`. "
                  f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
              )
  
          if embed_tokens is not None:
              self.embed_tokens = embed_tokens
          else:
              self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
  
          self.embed_positions = LEDLearnedPositionalEmbedding(
              self.max_source_positions,
              embed_dim,
              self.padding_idx,
          )
          self.layers = nn.ModuleList([LEDEncoderLayer(config, i) for i in range(config.encoder_layers)])
          self.layernorm_embedding = nn.LayerNorm(embed_dim)
  
          self.init_weights()
  
      def _merge_to_attention_mask(self, attention_mask: torch.Tensor, global_attention_mask: torch.Tensor):
          # longformer self attention expects attention mask to have 0 (no attn), 1 (local attn), 2 (global attn)
          # (global_attention_mask + 1) => 1 for local attention, 2 for global attention
          # => final attention_mask => 0 for no attention, 1 for local attention 2 for global attention
          if attention_mask is not None:
              attention_mask = attention_mask * (global_attention_mask + 1)
          else:
              # simply use `global_attention_mask` as `attention_mask`
              # if no `attention_mask` is given
              attention_mask = global_attention_mask + 1
          return attention_mask
  
      def _pad_to_window_size(
          self,
          input_ids: torch.Tensor,
          attention_mask: torch.Tensor,
          inputs_embeds: torch.Tensor,
          pad_token_id: int,
      ):
          """A helper function to pad tokens and mask to work with implementation of Longformer self-attention."""
          # padding
          attention_window = (
              self.config.attention_window
              if isinstance(self.config.attention_window, int)
              else max(self.config.attention_window)
          )
  
          assert attention_window % 2 == 0, f"`attention_window` should be an even value. Given {attention_window}"
          input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
          batch_size, seq_len = input_shape[:2]
  
          padding_len = (attention_window - seq_len % attention_window) % attention_window
          if padding_len > 0:
              logger.info(
                  "Input ids are automatically padded from {} to {} to be a multiple of `config.attention_window`: {}".format(
                      seq_len, seq_len + padding_len, attention_window
                  )
              )
              if input_ids is not None:
                  input_ids = F.pad(input_ids, (0, padding_len), value=pad_token_id)
              if inputs_embeds is not None:
                  input_ids_padding = inputs_embeds.new_full(
                      (batch_size, padding_len),
                      self.config.pad_token_id,
                      dtype=torch.long,
                  )
                  inputs_embeds_padding = self.embed_tokens(input_ids_padding)
                  inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
  
              attention_mask = F.pad(attention_mask, (0, padding_len), value=False)  # no attention on the padding tokens
  
          return padding_len, input_ids, attention_mask, inputs_embeds
  
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          global_attention_mask=None,
          inputs_embeds=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.LEDTokenizer`. 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>`__
              global_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                  Mask to decide the attention given on each token, local attention or global attention for the encoder.
                  Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is
                  important for task-specific finetuning because it makes the model more flexible at representing the
                  task. For example, for classification, the <s> token should be given global attention. For QA, all
                  question tokens should also have global attention. Please refer to the `Longformer paper
                  <https://arxiv.org/abs/2004.05150>`__ for more details. Mask values selected in ``[0, 1]``:
  
                  - 0 for local attention (a sliding window attention),
                  - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
              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
          )
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # check input_ids and inputs_embeds
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is None and inputs_embeds is None:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if inputs_embeds is None:
              inputs_embeds = self.embed_tokens(input_ids)
  
          # create default attention_mask
          if attention_mask is None:
              attention_mask = torch.ones(inputs_embeds.size()[:-1], device=inputs_embeds.device, dtype=torch.long)
  
          # merge `global_attention_mask` and `attention_mask`
          if global_attention_mask is not None:
              attention_mask = self._merge_to_attention_mask(attention_mask, global_attention_mask)
  
          # pad input if necessary
          padding_len, input_ids, attention_mask, inputs_embeds = self._pad_to_window_size(
              input_ids=input_ids,
              attention_mask=attention_mask,
              inputs_embeds=inputs_embeds,
              pad_token_id=self.config.pad_token_id,
          )
  
          # retrieve input_shape
          if 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]
  
          # convert attention_mask to float
          if attention_mask is not None:
              # [bsz, seq_len] -> [bsz, seq_len]; 1 -> 0.0; 0 -> "-inf"
              attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)[:, 0, 0, :]
  
          # get masking tensors
          is_index_masked = attention_mask < 0
          is_index_global_attn = attention_mask > 0
          is_global_attn = is_index_global_attn.flatten().any().item()
  
          embed_pos = self.embed_positions(input_shape)
  
          hidden_states = inputs_embeds + embed_pos
          hidden_states = self.layernorm_embedding(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
  
          encoder_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
          all_global_attentions = () if (output_attentions and is_global_attn) else None
  
          for encoder_layer in 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, None)
              else:
                  if getattr(self.config, "gradient_checkpointing", False):
  
                      def create_custom_forward(module):
                          def custom_forward(*inputs):
                              return module(*inputs, is_global_attn, output_attentions)
  
                          return custom_forward
  
                      layer_outputs = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(encoder_layer),
                          hidden_states,
                          attention_mask,
                          is_index_masked,
                          is_index_global_attn,
                      )
                  else:
                      layer_outputs = encoder_layer(
                          hidden_states,
                          attention_mask=attention_mask,
                          is_index_masked=is_index_masked,
                          is_index_global_attn=is_index_global_attn,
                          is_global_attn=is_global_attn,
                          output_attentions=output_attentions,
                      )
                  hidden_states = layer_outputs[0]
  
              if output_attentions:
                  # bzs x seq_len x num_attn_heads x (num_global_attn + attention_window_len + 1) => bzs x num_attn_heads x seq_len x (num_global_attn + attention_window_len + 1)
                  all_attentions = all_attentions + (layer_outputs[1].transpose(1, 2),)
  
                  if is_global_attn:
                      # bzs x num_attn_heads x num_global_attn x seq_len => bzs x num_attn_heads x seq_len x num_global_attn
                      all_global_attentions = all_global_attentions + (layer_outputs[2].transpose(2, 3),)
  
          if output_hidden_states:
              encoder_states = encoder_states + (hidden_states,)
  
          # undo padding
          if padding_len > 0:
              # unpad `hidden_states` because the calling function is expecting a length == input_ids.size(1)
              hidden_states = hidden_states[:, :-padding_len]
  
          if not return_dict:
              return tuple(
                  v for v in [hidden_states, encoder_states, all_attentions, all_global_attentions] if v is not None
              )
          return LEDEncoderBaseModelOutput(
              last_hidden_state=hidden_states,
              hidden_states=encoder_states,
              attentions=all_attentions,
              global_attentions=all_global_attentions,
          )
  
  
  class LEDDecoder(LEDPreTrainedModel):
      """
      Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`LEDDecoderLayer`
  
      Args:
          config: LEDConfig
          embed_tokens (torch.nn.Embedding): output embedding
      """
  
      def __init__(self, config: LEDConfig, embed_tokens: Optional[nn.Embedding] = None):
          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_decoder_position_embeddings
  
          if embed_tokens is not None:
              self.embed_tokens = embed_tokens
          else:
              self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
  
          self.embed_positions = LEDLearnedPositionalEmbedding(
              self.max_target_positions,
              config.d_model,
              self.padding_idx,
          )
          self.layers = nn.ModuleList([LEDDecoderLayer(config) for _ in range(config.decoder_layers)])
          self.layernorm_embedding = nn.LayerNorm(config.d_model)
  
          self.init_weights()
  
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          global_attention_mask=None,
          encoder_hidden_states=None,
          encoder_attention_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.LEDTokenizer`. 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>`__
              global_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                  Mask to decide the attention given on each token, local attention or global attention. Tokens with
                  global attention attends to all other tokens, and all other tokens attend to them. This is important
                  for task-specific finetuning because it makes the model more flexible at representing the task. For
                  example, for classification, the <s> token should be given global attention. For QA, all question
                  tokens should also have global attention. Please refer to the `Longformer paper
                  <https://arxiv.org/abs/2004.05150>`__ for more details. Mask values selected in ``[0, 1]``:
  
                  - 0 for local attention (a sliding window attention),
                  - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
              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>`__
              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)
  
          # 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 and combined_attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              combined_attention_mask = combined_attention_mask + _expand_mask(
                  attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
              )
  
          # expand encoder attention mask
          if encoder_hidden_states is not None and encoder_attention_mask is not None:
              # [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_shape, past_key_values_length)
  
          hidden_states = inputs_embeds + positions
          hidden_states = self.layernorm_embedding(hidden_states)
  
          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 else None
          next_decoder_cache = () if use_cache else None
          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):
                  if use_cache:
                      raise ValueError(
                          "When using `gradient_checkpointing`, make sure that `use_cache=False` and `config.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,
                      combined_attention_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                      None,
                  )
              else:
                  layer_outputs = decoder_layer(
                      hidden_states,
                      attention_mask=combined_attention_mask,
                      encoder_hidden_states=encoder_hidden_states,
                      encoder_attention_mask=encoder_attention_mask,
                      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],)
                  all_cross_attentions += (layer_outputs[2],)
  
          # 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 LED Model outputting raw hidden-states without any specific head on top.",
      LED_START_DOCSTRING,
  )
  class LEDModel(LEDPreTrainedModel):
      def __init__(self, config: LEDConfig):
          super().__init__(config)
  
          padding_idx, vocab_size = config.pad_token_id, config.vocab_size
          self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
  
          self.encoder = LEDEncoder(config, self.shared)
          self.decoder = LEDDecoder(config, self.shared)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.shared
  
      def set_input_embeddings(self, value):
          self.shared = value
          self.encoder.embed_tokens = self.shared
          self.decoder.embed_tokens = self.shared
  
      def get_encoder(self):
          return self.encoder
  
      def get_decoder(self):
          return self.decoder
  
      @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="allenai/led-base-16384",
          output_type=Seq2SeqModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          global_attention_mask=None,
          past_key_values=None,
          inputs_embeds=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_ids=input_ids,
                  attention_mask=attention_mask,
                  global_attention_mask=global_attention_mask,
                  inputs_embeds=inputs_embeds,
                  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 LEDEncoderBaseModelOutput when return_dict=False
          elif return_dict and not isinstance(encoder_outputs, LEDEncoderBaseModelOutput):
              encoder_outputs = LEDEncoderBaseModelOutput(
                  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,
                  global_attentions=encoder_outputs[3] if len(encoder_outputs) > 3 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,
              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 LEDSeq2SeqModelOutput(
              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,
              encoder_global_attentions=encoder_outputs.global_attentions,
          )
  
  
  @add_start_docstrings(
      "The LED Model with a language modeling head. Can be used for summarization.", LED_START_DOCSTRING
  )
  class LEDForConditionalGeneration(LEDPreTrainedModel):
      base_model_prefix = "led"
      _keys_to_ignore_on_load_missing = [
          r"final_logits_bias",
          r"encoder\.version",
          r"decoder\.version",
          r"lm_head\.weight",
      ]
  
      def __init__(self, config: LEDConfig):
          super().__init__(config)
          self.led = LEDModel(config)
          self.register_buffer("final_logits_bias", torch.zeros((1, self.led.shared.num_embeddings)))
          self.lm_head = nn.Linear(config.d_model, self.led.shared.num_embeddings, bias=False)
  
          self.init_weights()
  
      def get_encoder(self):
          return self.led.get_encoder()
  
      def get_decoder(self):
          return self.led.get_decoder()
  
      def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
          new_embeddings = super().resize_token_embeddings(new_num_tokens)
          self._resize_final_logits_bias(new_num_tokens)
          return new_embeddings
  
      def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
          old_num_tokens = self.final_logits_bias.shape[-1]
          if new_num_tokens <= old_num_tokens:
              new_bias = self.final_logits_bias[:, :new_num_tokens]
          else:
              extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
              new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
          self.register_buffer("final_logits_bias", new_bias)
  
      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(LED_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
      @add_end_docstrings(LED_GENERATION_EXAMPLE)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          global_attention_mask=None,
          past_key_values=None,
          inputs_embeds=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 masked 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:
  
          Conditional generation example::
  
              >>> from transformers import LEDTokenizer, LEDForConditionalGeneration
              >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384')
              >>> TXT = "My friends are <mask> but they eat too many carbs."
  
              >>> model = LEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')
              >>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
              >>> logits = model(input_ids).logits
  
              >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
              >>> probs = logits[0, masked_index].softmax(dim=0)
              >>> values, predictions = probs.topk(5)
  
              >>> tokenizer.decode(predictions).split()
          """
          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.led(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              encoder_outputs=encoder_outputs,
              global_attention_mask=global_attention_mask,
              past_key_values=past_key_values,
              inputs_embeds=inputs_embeds,
              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]) + self.final_logits_bias
  
          masked_lm_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (lm_logits,) + outputs[1:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
  
          return LEDSeq2SeqLMOutput(
              loss=masked_lm_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,
              encoder_global_attentions=outputs.encoder_global_attentions,
          )
  
      def prepare_inputs_for_generation(
          self, decoder_input_ids, past=None, attention_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 {
              "input_ids": None,  # encoder_outputs is defined. input_ids not needed
              "encoder_outputs": encoder_outputs,
              "past_key_values": past,
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": attention_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:
              # cached cross_attention states don't have to be reordered -> they are always the same
              reordered_past += (
                  tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
              )
          return reordered_past
  
  
  @add_start_docstrings(
      """
      LED model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
      tasks.
      """,
      LED_START_DOCSTRING,
  )
  class LEDForSequenceClassification(LEDPreTrainedModel):
      def __init__(self, config: LEDConfig, **kwargs):
          super().__init__(config, **kwargs)
          self.led = LEDModel(config)
          self.classification_head = LEDClassificationHead(
              config.d_model,
              config.d_model,
              config.num_labels,
              config.classifier_dropout,
          )
          self.led._init_weights(self.classification_head.dense)
          self.led._init_weights(self.classification_head.out_proj)
  
      @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="allenai/led-base-16384",
          output_type=Seq2SeqSequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          global_attention_mask=None,
          inputs_embeds=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,)`, `optional`):
              Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
              config.num_labels - 1]`. If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          if labels is not None:
              use_cache = False
  
          if input_ids is None and inputs_embeds is not None:
              raise NotImplementedError(
                  f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
              )
  
          outputs = self.led(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              global_attention_mask=global_attention_mask,
              encoder_outputs=encoder_outputs,
              inputs_embeds=inputs_embeds,
              decoder_inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          hidden_states = outputs[0]  # last hidden state
  
          eos_mask = input_ids.eq(self.config.eos_token_id)
  
          if len(torch.unique(eos_mask.sum(1))) > 1:
              raise ValueError("All examples must have the same number of <eos> tokens.")
          sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
              :, -1, :
          ]
          logits = self.classification_head(sentence_representation)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return LEDSeq2SeqSequenceClassifierOutput(
              loss=loss,
              logits=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,
              encoder_global_attentions=outputs.encoder_global_attentions,
          )
  
  
  @add_start_docstrings(
      """
      LED Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer
      on top of the hidden-states output to compute `span start logits` and `span end logits`).
      """,
      LED_START_DOCSTRING,
  )
  class LEDForQuestionAnswering(LEDPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          config.num_labels = 2
          self.num_labels = config.num_labels
  
          self.led = LEDModel(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.led._init_weights(self.qa_outputs)
  
      @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="allenai/led-base-16384",
          output_type=Seq2SeqQuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          global_attention_mask=None,
          start_positions=None,
          end_positions=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the start of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the end of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          if start_positions is not None and end_positions is not None:
              use_cache = False
  
          outputs = self.led(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              global_attention_mask=global_attention_mask,
              encoder_outputs=encoder_outputs,
              inputs_embeds=inputs_embeds,
              decoder_inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
  
          logits = self.qa_outputs(sequence_output)
          start_logits, end_logits = logits.split(1, dim=-1)
          start_logits = start_logits.squeeze(-1)
          end_logits = end_logits.squeeze(-1)
  
          total_loss = None
          if start_positions is not None and end_positions is not None:
              # If we are on multi-GPU, split add a dimension
              if len(start_positions.size()) > 1:
                  start_positions = start_positions.squeeze(-1)
              if len(end_positions.size()) > 1:
                  end_positions = end_positions.squeeze(-1)
              # sometimes the start/end positions are outside our model inputs, we ignore these terms
              ignored_index = start_logits.size(1)
              start_positions.clamp_(0, ignored_index)
              end_positions.clamp_(0, ignored_index)
  
              loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
              start_loss = loss_fct(start_logits, start_positions)
              end_loss = loss_fct(end_logits, end_positions)
              total_loss = (start_loss + end_loss) / 2
  
          if not return_dict:
              output = (
                  start_logits,
                  end_logits,
              ) + outputs[1:]
              return ((total_loss,) + output) if total_loss is not None else output
  
          return LEDSeq2SeqQuestionAnsweringModelOutput(
              loss=total_loss,
              start_logits=start_logits,
              end_logits=end_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,
              encoder_global_attentions=outputs.encoder_global_attentions,
          )
  