# coding=utf-8
# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
#
# 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 Longformer model. """

import logging
import math
import warnings

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from torch.nn import functional as F

from .configuration_longformer import LongformerConfig
from .file_utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_callable,
    replace_return_docstrings,
)
from .modeling_bert import BertIntermediate, BertLayerNorm, BertOutput, BertPooler, BertPreTrainedModel, BertSelfOutput
from .modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPooling,
    MaskedLMOutput,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from .modeling_roberta import RobertaEmbeddings, RobertaLMHead
from .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer


logger = logging.getLogger(__name__)

_CONFIG_FOR_DOC = "LongformerConfig"
_TOKENIZER_FOR_DOC = "LongformerTokenizer"

LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "allenai/longformer-base-4096",
    "allenai/longformer-large-4096",
    "allenai/longformer-large-4096-finetuned-triviaqa",
    "allenai/longformer-base-4096-extra.pos.embd.only",
    "allenai/longformer-large-4096-extra.pos.embd.only",
    # See all Longformer models at https://huggingface.co/models?filter=longformer
]


def _get_question_end_index(input_ids, sep_token_id):
    """
        Computes the index of the first occurance of `sep_token_id`.
    """

    sep_token_indices = (input_ids == sep_token_id).nonzero()
    batch_size = input_ids.shape[0]

    assert sep_token_indices.shape[1] == 2, "`input_ids` should have two dimensions"
    assert (
        sep_token_indices.shape[0] == 3 * batch_size
    ), f"There should be exactly three separator tokens: {sep_token_id} in every sample for questions answering. You might also consider to set `global_attention_mask` manually in the forward function to avoid this error."

    return sep_token_indices.view(batch_size, 3, 2)[:, 0, 1]


def _compute_global_attention_mask(input_ids, sep_token_id, before_sep_token=True):
    """
        Computes global attention mask by putting attention on all tokens
        before `sep_token_id` if `before_sep_token is True` else after
        `sep_token_id`.
    """

    question_end_index = _get_question_end_index(input_ids, sep_token_id)
    question_end_index = question_end_index.unsqueeze(dim=1)  # size: batch_size x 1
    # bool attention mask with True in locations of global attention
    attention_mask = torch.arange(input_ids.shape[1], device=input_ids.device)
    if before_sep_token is True:
        attention_mask = (attention_mask.expand_as(input_ids) < question_end_index).to(torch.uint8)
    else:
        # last token is separation token and should not be counted and in the middle are two separation tokens
        attention_mask = (attention_mask.expand_as(input_ids) > (question_end_index + 1)).to(torch.uint8) * (
            attention_mask.expand_as(input_ids) < input_ids.shape[-1]
        ).to(torch.uint8)

    return attention_mask


class LongformerSelfAttention(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

    @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_by_window_overlap_except_last_row(chunked_hidden_states):
        """shift every row 1 step right, converting columns into diagonals"""
        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_overlapL+window_overlapwindow_overlap+window_overlap
        chunked_hidden_states = chunked_hidden_states[
            :, :, :-window_overlap
        ]  # total_num_heads x num_chunks x window_overlapL+window_overlapwindow_overlap
        chunked_hidden_states = chunked_hidden_states.view(
            total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim
        )  # total_num_heads x num_chunks, window_overlap x hidden_dim+window_overlap
        chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
        return chunked_hidden_states

    @staticmethod
    def _chunk(hidden_states, window_overlap):
        """convert into overlapping chunkings. 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)

    def _mask_invalid_locations(self, 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 Longformer)
        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)

        chunked_query = self._chunk(query, window_overlap)
        chunked_key = self._chunk(key, window_overlap)

        # matrix multipication
        # 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
        chunked_attention_scores = torch.einsum("bcxd,bcyd->bcxy", (chunked_query, chunked_key))  # multiply

        # convert diagonals into columns
        diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(
            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_by_window_overlap_except_last_row(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)

    def forward(
        self, hidden_states, attention_mask=None, output_attentions=False,
    ):
        """
        LongformerSelfAttention expects `len(hidden_states)` to be multiple of `attention_window`.
        Padding to `attention_window` happens in LongformerModel.forward to avoid redoing the padding on each layer.

        The `attention_mask` is changed in `BertModel.forward` from 0, 1, 2 to
            -ve: no attention
              0: local attention
            +ve: global attention

        """

        attention_mask = attention_mask.squeeze(dim=2).squeeze(dim=1)

        # is index masked or global attention
        is_index_masked = attention_mask < 0
        is_index_global_attn = attention_mask > 0
        is_global_attn = is_index_global_attn.flatten().any().item()

        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_probs = (batch_size, seq_len, num_heads, window*2+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).unsqueeze(dim=-1).unsqueeze(dim=-1)

        # 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"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 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_fp32 = F.softmax(attn_scores, dim=-1, dtype=torch.float32)  # use fp32 for numerical stability
        attn_probs = attn_probs_fp32.type_as(attn_scores)

        # free memory
        del attn_probs_fp32

        # softmax sometimes inserts NaN if all positions are masked, replace them with 0
        attn_probs = torch.masked_fill(attn_probs, is_index_masked.unsqueeze(-1).unsqueeze(-1), 0.0)

        # 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 = 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
            )

        attn_output = attn_output.transpose(0, 1)

        if output_attentions:
            if is_global_attn:
                # With global attention, return global attention probabilities only
                # batch_size x num_heads x sequence_length x window_size
                # which is the attention weights from all tokens to all tokens for global attention
                # It doesn't not return local attention. Only tokens with global attention have values > 0.0
                attn_probs = attn_probs[:, :, :, :max_num_global_attn_indices]
                # pad attn_probs to max length with 0.0 since global attn did not attend there
                window_size = self.one_sided_attn_window_size * 2 + 1
                attn_probs = F.pad(attn_probs, (0, window_size - max_num_global_attn_indices), value=0.0,)
                attn_probs = attn_probs.permute(0, 2, 1, 3)
            else:
                # without global attention, return local attention probabilities
                # batch_size x num_heads x sequence_length x window_size
                # which is the attention weights of every token attending to its neighbours
                attn_probs = attn_probs.permute(0, 2, 1, 3)

        outputs = (attn_output, attn_probs) if output_attentions else (attn_output,)
        return outputs

    @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.unsqueeze(1).unsqueeze(2), -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_output = global_attn_output.view(
            batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim
        )
        return global_attn_output


class LongformerAttention(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.self = LongformerSelfAttention(config, layer_id)
        self.output = BertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self, hidden_states, attention_mask=None, output_attentions=False,
    ):
        self_outputs = self.self(hidden_states, attention_mask, output_attentions,)
        attn_output = self.output(self_outputs[0], hidden_states)
        outputs = (attn_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


class LongformerLayer(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.attention = LongformerAttention(config, layer_id)
        self.intermediate = BertIntermediate(config)
        self.output = BertOutput(config)

    def forward(
        self, hidden_states, attention_mask=None, output_attentions=False,
    ):
        self_attn_outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions,)
        attn_output = self_attn_outputs[0]
        outputs = self_attn_outputs[1:]  # add self attentions if we output attention weights

        intermediate_output = self.intermediate(attn_output)
        layer_output = self.output(intermediate_output, attn_output)
        outputs = (layer_output,) + outputs
        return outputs


class LongformerEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([LongformerLayer(config, layer_id=i) for i in range(config.num_hidden_layers)])

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=False,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if getattr(self.config, "gradient_checkpointing", False):

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module), hidden_states, attention_mask,
                )
            else:
                layer_outputs = layer_module(hidden_states, attention_mask, output_attentions,)
            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )


class LongformerPreTrainedModel(PreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained
        models.
    """

    config_class = LongformerConfig
    base_model_prefix = "longformer"

    def _init_weights(self, module):
        """ Initialize the weights """
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, BertLayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()


LONGFORMER_START_DOCSTRING = r"""

    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ sub-class.
    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.LongformerConfig`): 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.
"""

LONGFORMER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`transformers.LonmgformerTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.__call__` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

            `What are attention masks? <../glossary.html#attention-mask>`__

        global_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
            Mask to decide the attention given on each token, local attention or global attenion.
            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).

        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
            Segment token indices to indicate first and second portions of the inputs.
            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
            corresponds to a `sentence B` token

            `What are token type IDs? <../glossary.html#token-type-ids>`_
        position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.

            `What are position IDs? <../glossary.html#position-ids>`_
        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            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 `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the hidden states of all layers are returned. See ``hidden_states`` under returned tensors for more detail.
        return_dict (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
            plain tuple.
"""


@add_start_docstrings(
    "The bare Longformer Model outputting raw hidden-states without any specific head on top.",
    LONGFORMER_START_DOCSTRING,
)
class LongformerModel(LongformerPreTrainedModel):
    """
    This class overrides :class:`~transformers.RobertaModel` to provide the ability to process
    long sequences following the selfattention approach described in `Longformer: the Long-Document Transformer
    <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer selfattention
    combines a local (sliding window) and global attention to extend to long documents without the O(n^2) increase in
    memory and compute.

    The selfattention module `LongformerSelfAttention` implemented here supports the combination of local and
    global attention but it lacks support for autoregressive attention and dilated attention. Autoregressive
    and dilated attention are more relevant for autoregressive language modeling than finetuning on downstream
    tasks. Future release will add support for autoregressive attention, but the support for dilated attention
    requires a custom CUDA kernel to be memory and compute efficient.

    """

    config_class = LongformerConfig
    base_model_prefix = "longformer"

    def __init__(self, config):
        super().__init__(config)
        self.config = config

        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)}"
            )

        self.embeddings = RobertaEmbeddings(config)
        self.encoder = LongformerEncoder(config)
        self.pooler = BertPooler(config)

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """ Prunes heads of the model.
            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
            See base class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def _pad_to_window_size(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        token_type_ids: torch.Tensor,
        position_ids: torch.Tensor,
        inputs_embeds: torch.Tensor,
        pad_token_id: int,
    ):
        """A helper function to pad tokens and mask to work with implementation of Longformer selfattention."""
        # 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 position_ids is not None:
                # pad with position_id = pad_token_id as in modeling_roberta.RobertaEmbeddings
                position_ids = F.pad(position_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.embeddings(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
            token_type_ids = F.pad(token_type_ids, (0, padding_len), value=0)  # pad with token_type_id = 0

        return padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds

    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

    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        global_attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""

    Returns:

    Examples::

        >>> import torch
        >>> from transformers import LongformerModel, LongformerTokenizer

        >>> model = LongformerModel.from_pretrained('allenai/longformer-base-4096', return_dict=True)
        >>> tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')

        >>> SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000)  # long input document
        >>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1

        >>> # Attention mask values -- 0: no attention, 1: local attention, 2: global attention
        >>> attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to local attention
        >>> attention_mask[:, [1, 4, 21,]] = 2  # Set global attention based on the task. For example,
        ...                                     # classification: the <s> token
        ...                                     # QA: question tokens
        ...                                     # LM: potentially on the beginning of sentences and paragraphs
        >>> outputs = model(input_ids, attention_mask=attention_mask)
        >>> sequence_output = outputs.last_hidden_state
        >>> pooled_output = outputs.pooler_output
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if 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 not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        # 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)

        padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds = self._pad_to_window_size(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            pad_token_id=self.config.pad_token_id,
        )

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)

        embedding_output = self.embeddings(
            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
        )

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output)

        # undo padding
        if padding_len > 0:
            # unpad `sequence_output` because the calling function is expecting a length == input_ids.size(1)
            sequence_output = sequence_output[:, :-padding_len]

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


@add_start_docstrings("""Longformer Model with a `language modeling` head on top. """, LONGFORMER_START_DOCSTRING)
class LongformerForMaskedLM(LongformerPreTrainedModel):
    config_class = LongformerConfig
    base_model_prefix = "longformer"

    def __init__(self, config):
        super().__init__(config)

        self.longformer = LongformerModel(config)
        self.lm_head = RobertaLMHead(config)

        self.init_weights()

    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        global_attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        **kwargs
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Labels for computing the masked language modeling loss.
            Indices should be in ``[-100, 0, ..., config.vocab_size]`` (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]``
        kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
            Used to hide legacy arguments that have been deprecated.

    Returns:

    Examples::

        >>> import torch
        >>> from transformers import LongformerForMaskedLM, LongformerTokenizer

        >>> model = LongformerForMaskedLM.from_pretrained('allenai/longformer-base-4096', return_dict=True)
        >>> tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')

        >>> SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000)  # long input document
        >>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1

        >>> attention_mask = None  # default is local attention everywhere, which is a good choice for MaskedLM
        ...                        # check ``LongformerModel.forward`` for more details how to set `attention_mask`
        >>> outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
        >>> loss = outputs.loss
        >>> prediction_logits = output.logits
        """

        if "masked_lm_labels" in kwargs:
            warnings.warn(
                "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
                FutureWarning,
            )
            labels = kwargs.pop("masked_lm_labels")
        assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.longformer(
            input_ids,
            attention_mask=attention_mask,
            global_attention_mask=global_attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        prediction_scores = self.lm_head(sequence_output)

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return MaskedLMOutput(
            loss=masked_lm_loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """Longformer Model transformer with a sequence classification/regression head on top (a linear layer
    on top of the pooled output) e.g. for GLUE tasks. """,
    LONGFORMER_START_DOCSTRING,
)
class LongformerForSequenceClassification(BertPreTrainedModel):
    config_class = LongformerConfig
    base_model_prefix = "longformer"

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.longformer = LongformerModel(config)
        self.classifier = LongformerClassificationHead(config)

        self.init_weights()

    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="allenai/longformer-base-4096",
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        global_attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
            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 regression loss is computed (Mean-Square loss),
            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 global_attention_mask is None:
            logger.info("Initializing global attention on CLS token...")
            global_attention_mask = torch.zeros_like(input_ids)
            # global attention on cls token
            global_attention_mask[:, 0] = 1

        outputs = self.longformer(
            input_ids,
            attention_mask=attention_mask,
            global_attention_mask=global_attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1), labels.view(-1))
            else:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
        )


class LongformerClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, hidden_states, **kwargs):
        hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.dense(hidden_states)
        hidden_states = torch.tanh(hidden_states)
        hidden_states = self.dropout(hidden_states)
        output = self.out_proj(hidden_states)
        return output


@add_start_docstrings(
    """Longformer Model with a span classification head on top for extractive question-answering tasks like SQuAD / TriviaQA (a linear layers on top of
    the hidden-states output to compute `span start logits` and `span end logits`). """,
    LONGFORMER_START_DOCSTRING,
)
class LongformerForQuestionAnswering(BertPreTrainedModel):
    config_class = LongformerConfig
    base_model_prefix = "longformer"

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.longformer = LongformerModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        global_attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        start_positions=None,
        end_positions=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
            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`, defaults to :obj:`None`):
            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.
    Returns:

    Examples::

        >>> from transformers import LongformerTokenizer, LongformerForQuestionAnswering
        >>> import torch

        >>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
        >>> model = LongformerForQuestionAnswering.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa", return_dict=True)

        >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
        >>> encoding = tokenizer(question, text, return_tensors="pt")
        >>> input_ids = encoding["input_ids"]

        >>> # default is local attention everywhere
        >>> # the forward method will automatically set global attention on question tokens
        >>> attention_mask = encoding["attention_mask"]

        >>> outputs = model(input_ids, attention_mask=attention_mask)
        >>> start_logits = outputs.start_logits
        >>> end_logits = outputs.end_logits
        >>> all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())

        >>> answer_tokens = all_tokens[torch.argmax(start_logits) :torch.argmax(end_logits)+1]
        >>> answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) # remove space prepending space token

        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # set global attention on question tokens
        if global_attention_mask is None:
            logger.info("Initializing global attention on question tokens...")
            # put global attention on all tokens until `config.sep_token_id` is reached
            global_attention_mask = _compute_global_attention_mask(input_ids, self.config.sep_token_id)

        outputs = self.longformer(
            input_ids,
            attention_mask=attention_mask,
            global_attention_mask=global_attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            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[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """Longformer Model with a token classification head on top (a linear layer on top of
    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
    LONGFORMER_START_DOCSTRING,
)
class LongformerForTokenClassification(BertPreTrainedModel):
    config_class = LongformerConfig
    base_model_prefix = "longformer"

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.longformer = LongformerModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="allenai/longformer-base-4096",
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        global_attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Labels for computing the token classification loss.
            Indices should be in ``[0, ..., config.num_labels - 1]``.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.longformer(
            input_ids,
            attention_mask=attention_mask,
            global_attention_mask=global_attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            # Only keep active parts of the loss
            if attention_mask is not None:
                active_loss = attention_mask.view(-1) == 1
                active_logits = logits.view(-1, self.num_labels)
                active_labels = torch.where(
                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
                )
                loss = loss_fct(active_logits, active_labels)
            else:
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
        )


@add_start_docstrings(
    """Longformer Model with a multiple choice classification head on top (a linear layer on top of
    the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
    LONGFORMER_START_DOCSTRING,
)
class LongformerForMultipleChoice(BertPreTrainedModel):
    config_class = LongformerConfig
    base_model_prefix = "longformer"

    def __init__(self, config):
        super().__init__(config)

        self.longformer = LongformerModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, 1)

        self.init_weights()

    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="allenai/longformer-base-4096",
        output_type=MultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        token_type_ids=None,
        attention_mask=None,
        global_attention_mask=None,
        labels=None,
        position_ids=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
            Labels for computing the multiple choice classification loss.
            Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
            of the input tensors. (see `input_ids` above)
        """
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # set global attention on question tokens
        if global_attention_mask is None:
            logger.info("Initializing global attention on multiple choice...")
            # put global attention on all tokens after `config.sep_token_id`
            global_attention_mask = torch.stack(
                [
                    _compute_global_attention_mask(input_ids[:, i], self.config.sep_token_id, before_sep_token=False)
                    for i in range(num_choices)
                ],
                dim=1,
            )

        flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        flat_global_attention_mask = (
            global_attention_mask.view(-1, global_attention_mask.size(-1))
            if global_attention_mask is not None
            else None
        )
        flat_inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
            if inputs_embeds is not None
            else None
        )

        outputs = self.longformer(
            flat_input_ids,
            position_ids=flat_position_ids,
            token_type_ids=flat_token_type_ids,
            attention_mask=flat_attention_mask,
            global_attention_mask=flat_global_attention_mask,
            inputs_embeds=flat_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)

        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return MultipleChoiceModelOutput(
            loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
        )
