"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_longformer import LongformerConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "allenai/longformer-base-4096"
_CONFIG_FOR_DOC = "LongformerConfig"
@dataclass
class LongformerBaseModelOutput(ModelOutput):
    """Args:"""
    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 LongformerBaseModelOutputWithPooling(ModelOutput):
    """Args:"""
    last_hidden_state: torch.FloatTensor
    pooler_output: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerMaskedLMOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerQuestionAnsweringModelOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    start_logits: torch.FloatTensor = None
    end_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerSequenceClassifierOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerMultipleChoiceModelOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerTokenClassifierOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
def _get_question_end_index(input_ids, 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):
    question_end_index = _get_question_end_index(input_ids, sep_token_id)
    question_end_index = question_end_index.unsqueeze(dim=1)
    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.bool)
    else: attention_mask = (attention_mask.expand_as(input_ids) > (question_end_index + 1)).to(torch.bool) * (attention_mask.expand_as(input_ids) < input_ids.shape[-1]).to(torch.bool)
    return attention_mask
def create_position_ids_from_input_ids(input_ids, padding_idx):
    mask = input_ids.ne(padding_idx).int()
    incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
    return incremental_indices.long() + padding_idx
class LongformerEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.padding_idx = config.pad_token_id
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx)
    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
        if position_ids is None:
            if input_ids is not None: position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
            else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=position_ids.device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
        input_shape = inputs_embeds.size()[:-1]
        sequence_length = input_shape[1]
        position_ids = torch.arange(self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device)
        return position_ids.unsqueeze(0).expand(input_shape)
class LongformerSelfAttention(nn.Module):
    def __init__(self, config, layer_id):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0: raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({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)
        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
        self.config = config
    def forward(self, hidden_states, attention_mask=None, layer_head_mask=None, is_index_masked=None, is_index_global_attn=None, is_global_attn=None, output_attentions=False):
        hidden_states = hidden_states.transpose(0, 1)
        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}"
        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)
        remove_from_windowed_attention_mask = (attention_mask != 0)[:, :, None, None]
        float_mask = remove_from_windowed_attention_mask.type_as(query_vectors).masked_fill(remove_from_windowed_attention_mask, torch.finfo(query_vectors.dtype).min)
        diagonal_mask = self._sliding_chunks_query_key_matmul(float_mask.new_ones(size=float_mask.size()), float_mask, self.one_sided_attn_window_size)
        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()}")
        if is_global_attn:
            (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)
            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)
            attn_scores = torch.cat((global_key_attn_scores, attn_scores), dim=-1)
            del global_key_attn_scores
        attn_probs = nn.functional.softmax(attn_scores, dim=-1, dtype=torch.float32)
        if layer_head_mask is not None:
            assert layer_head_mask.size() == (self.num_heads,), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
            attn_probs = layer_head_mask.view(1, 1, -1, 1) * attn_probs
        attn_probs = torch.masked_fill(attn_probs, is_index_masked[:, :, None, None], 0.0)
        attn_probs = attn_probs.type_as(attn_scores)
        del attn_scores
        attn_probs = nn.functional.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)
        if is_global_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: 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()
        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,
            layer_head_mask=layer_head_mask, 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)
            nonzero_global_attn_output = global_attn_output[is_local_index_global_attn_nonzero[0], :, is_local_index_global_attn_nonzero[1]]
            attn_output[is_index_global_attn_nonzero[::-1]] = nonzero_global_attn_output.view(len(is_local_index_global_attn_nonzero[0]), -1)
            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):
        hidden_states_padded = nn.functional.pad(hidden_states_padded, padding)
        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):
        total_num_heads, num_chunks, window_overlap, hidden_dim = chunked_hidden_states.size()
        chunked_hidden_states = nn.functional.pad(chunked_hidden_states, (0, window_overlap + 1))
        chunked_hidden_states = chunked_hidden_states.view(total_num_heads, num_chunks, -1)
        chunked_hidden_states = chunked_hidden_states[:, :, :-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, onnx_export: bool = False):
        if not onnx_export:
            hidden_states = hidden_states.view(hidden_states.size(0), torch.div(hidden_states.size(1), (window_overlap * 2), rounding_mode="trunc"),
            window_overlap * 2, hidden_states.size(2))
            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)
        chunk_size = [hidden_states.size(0), torch.div(hidden_states.size(1), window_overlap, rounding_mode="trunc") - 1, window_overlap * 2, hidden_states.size(2)]
        overlapping_chunks = torch.empty(chunk_size, device=hidden_states.device)
        for chunk in range(chunk_size[1]): overlapping_chunks[:, chunk, :, :] = hidden_states[:, chunk * window_overlap : chunk * window_overlap + 2 * window_overlap, :]
        return overlapping_chunks
    @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())
        input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1] = torch.full_like(beginning_input, -float("inf")).where(beginning_mask.bool(), beginning_input)
        ending_input = input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :]
        ending_mask = ending_mask.expand(ending_input.size())
        input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :] = torch.full_like(ending_input, -float("inf")).where(ending_mask.bool(), ending_input)
    def _sliding_chunks_query_key_matmul(self, query: torch.Tensor, key: torch.Tensor, window_overlap: int):
        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 = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
        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, getattr(self.config, "onnx_export", False))
        key = self._chunk(key, window_overlap, getattr(self.config, "onnx_export", False))
        diagonal_chunked_attention_scores = torch.einsum("bcxd,bcyd->bcxy", (query, key))
        diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(diagonal_chunked_attention_scores, padding=(0, 0, 0, 1))
        diagonal_attention_scores = diagonal_chunked_attention_scores.new_zeros((batch_size * num_heads, chunks_count + 1, window_overlap, window_overlap * 2 + 1))
        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]
        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 :]
        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):
        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 = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
        chunked_attn_probs = attn_probs.transpose(1, 2).reshape(batch_size * num_heads, torch.div(seq_len, window_overlap, rounding_mode="trunc"), window_overlap, 2 * window_overlap + 1)
        value = value.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
        padded_value = nn.functional.pad(value, (0, 0, window_overlap, window_overlap), value=-1)
        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):
        num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
        max_num_global_attn_indices = num_global_attn_indices.max()
        is_index_global_attn_nonzero = is_index_global_attn.nonzero(as_tuple=True)
        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)
        is_local_index_global_attn_nonzero = is_local_index_global_attn.nonzero(as_tuple=True)
        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]
        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]
        attn_probs_from_global_key = torch.einsum("blhd,bshd->blhs", (query_vectors, key_vectors_only_global))
        attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3)
        attn_probs_from_global_key[is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, :] = torch.finfo(attn_probs_from_global_key.dtype).min
        attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3)
        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]
        attn_probs_only_global = attn_probs.narrow(-1, 0, max_num_global_attn_indices)
        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]
        attn_output_only_global = torch.matmul(attn_probs_only_global.transpose(1, 2).clone(), value_vectors_only_global.transpose(1, 2).clone()).transpose(1, 2)
        attn_probs_without_global = attn_probs.narrow(-1, max_num_global_attn_indices, attn_probs.size(-1) - max_num_global_attn_indices).contiguous()
        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, layer_head_mask, 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]
        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_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)
        global_query_vectors_only_global /= math.sqrt(self.head_dim)
        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))
        global_key_vectors = (global_key_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1))
        global_value_vectors = (global_value_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1))
        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 = global_attn_scores.transpose(1, 2)
        global_attn_scores[is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, :] = torch.finfo(global_attn_scores.dtype).min
        global_attn_scores = global_attn_scores.transpose(1, 2)
        global_attn_scores = global_attn_scores.masked_fill(is_index_masked[:, None, None, :], torch.finfo(global_attn_scores.dtype).min)
        global_attn_scores = global_attn_scores.view(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
        global_attn_probs_float = nn.functional.softmax(global_attn_scores, dim=-1, dtype=torch.float32)
        if layer_head_mask is not None:
            assert layer_head_mask.size() == (self.num_heads,), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
            global_attn_probs_float = layer_head_mask.view(1, -1, 1, 1) * global_attn_probs_float.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
            global_attn_probs_float = global_attn_probs_float.view(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
        global_attn_probs = nn.functional.dropout(global_attn_probs_float.type_as(global_attn_scores), p=self.dropout, training=self.training)
        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 LongformerSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class LongformerAttention(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.self = LongformerSelfAttention(config, layer_id)
        self.output = LongformerSelfOutput(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)
        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)
        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, layer_head_mask=None, is_index_masked=None, is_index_global_attn=None, is_global_attn=None, output_attentions=False):
        self_outputs = self.self(hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_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], hidden_states)
        outputs = (attn_output,) + self_outputs[1:]
        return outputs
class LongformerIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else: self.intermediate_act_fn = config.hidden_act
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
class LongformerOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class LongformerLayer(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.attention = LongformerAttention(config, layer_id)
        self.intermediate = LongformerIntermediate(config)
        self.output = LongformerOutput(config)
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
    def forward(self, hidden_states, attention_mask=None, layer_head_mask=None, is_index_masked=None, is_index_global_attn=None, is_global_attn=None, output_attentions=False):
        self_attn_outputs = self.attention(hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_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_attn_outputs[0]
        outputs = self_attn_outputs[1:]
        layer_output = apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attn_output)
        outputs = (layer_output,) + outputs
        return outputs
    def ff_chunk(self, attn_output):
        intermediate_output = self.intermediate(attn_output)
        layer_output = self.output(intermediate_output, attn_output)
        return layer_output
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)])
        self.gradient_checkpointing = False
    def forward(self, hidden_states, attention_mask=None, head_mask=None, padding_len=0, output_attentions=False, output_hidden_states=False, return_dict=True):
        is_index_masked = attention_mask < 0
        is_index_global_attn = attention_mask > 0
        is_global_attn = is_index_global_attn.flatten().any().item()
        all_hidden_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
        if head_mask is not None: assert head_mask.size()[0] == (len(self.layer)), f"The head_mask should be specified for {len(self.layer)} layers, but it is for {head_mask.size()[0]}."
        for idx, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, head_mask[idx] if head_mask is not None else None,
                is_index_masked, is_index_global_attn, is_global_attn, output_attentions)
            else:
                layer_outputs = layer_module(hidden_states, attention_mask=attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None,
                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:
                all_attentions = all_attentions + (layer_outputs[1].transpose(1, 2),)
                if is_global_attn: all_global_attentions = all_global_attentions + (layer_outputs[2].transpose(2, 3),)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        hidden_states = hidden_states[:, : hidden_states.shape[1] - padding_len]
        if output_hidden_states: all_hidden_states = tuple([state[:, : state.shape[1] - padding_len] for state in all_hidden_states])
        if output_attentions: all_attentions = tuple([state[:, :, : state.shape[2] - padding_len, :] for state in all_attentions])
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions, all_global_attentions] if v is not None)
        return LongformerBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, global_attentions=all_global_attentions)
class LongformerPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output
class LongformerLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.decoder.bias = self.bias
    def forward(self, features, **kwargs):
        x = self.dense(features)
        x = gelu(x)
        x = self.layer_norm(x)
        x = self.decoder(x)
        return x
    def _tie_weights(self):
        if self.decoder.bias.device.type == "meta": self.decoder.bias = self.bias
        else: self.bias = self.decoder.bias
class LongformerPreTrainedModel(PreTrainedModel):
    config_class = LongformerConfig
    base_model_prefix = "longformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["LongformerSelfAttention"]
    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
LONGFORMER_START_DOCSTRING = r"""
    This model inherits from [`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 ([`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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
LONGFORMER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(0)`, *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#attention-mask)
        global_attention_mask (`torch.FloatTensor` of shape `(0)`, *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).
        head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        token_type_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            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#token-type-ids)
        position_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            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#position-ids)
        inputs_embeds (`torch.FloatTensor` of shape `(0, hidden_size)`, *optional*):
            Optionally, instead of passing `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 (`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 (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~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):
    def __init__(self, config, add_pooling_layer=True):
        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
        else: assert len(config.attention_window) == config.num_hidden_layers, (f"`len(config.attention_window)` should equal `config.num_hidden_layers`. Expected {config.num_hidden_layers}, given {len(config.attention_window)}")
        self.embeddings = LongformerEmbeddings(config)
        self.encoder = LongformerEncoder(config)
        self.pooler = LongformerPooler(config) if add_pooling_layer else None
        self.post_init()
    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):
        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):
        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.warning_once(f"Input ids are automatically padded to be a multiple of `config.attention_window`: {attention_window}")
            if input_ids is not None: input_ids = nn.functional.pad(input_ids, (0, padding_len), value=pad_token_id)
            if position_ids is not None: position_ids = nn.functional.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 = nn.functional.pad(attention_mask, (0, padding_len), value=0)
            token_type_ids = nn.functional.pad(token_type_ids, (0, padding_len), value=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):
        if attention_mask is not None: attention_mask = attention_mask * (global_attention_mask + 1)
        else: attention_mask = global_attention_mask + 1
        return attention_mask
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, global_attention_mask: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, LongformerBaseModelOutputWithPooling]:
        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:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            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)
        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)
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)[:, 0, 0, :]
        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, head_mask=head_mask, padding_len=padding_len, 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) if self.pooler is not None else None
        if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:]
        return LongformerBaseModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states,
        attentions=encoder_outputs.attentions, global_attentions=encoder_outputs.global_attentions)
@add_start_docstrings("Longformer Model with a `language modeling` head on top.", LONGFORMER_START_DOCSTRING)
class LongformerForMaskedLM(LongformerPreTrainedModel):
    _tied_weights_keys = ["lm_head.decoder"]
    def __init__(self, config):
        super().__init__(config)
        self.longformer = LongformerModel(config, add_pooling_layer=False)
        self.lm_head = LongformerLMHead(config)
        self.post_init()
    def get_output_embeddings(self): return self.lm_head.decoder
    def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, global_attention_mask: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, LongformerMaskedLMOutput]:
        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, head_mask=head_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()
            labels = labels.to(prediction_scores.device)
            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 LongformerMaskedLMOutput(loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, global_attentions=outputs.global_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(LongformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.longformer = LongformerModel(config, add_pooling_layer=False)
        self.classifier = LongformerClassificationHead(config)
        self.post_init()
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, global_attention_mask: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, LongformerSequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if global_attention_mask is None:
            logger.warning_once("Initializing global attention on CLS token...")
            global_attention_mask = torch.zeros_like(input_ids)
            global_attention_mask[:, 0] = 1
        outputs = self.longformer(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, head_mask=head_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:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return LongformerSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, global_attentions=outputs.global_attentions)
class LongformerClassificationHead(nn.Module):
    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, :]
        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(LongformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.longformer = LongformerModel(config, add_pooling_layer=False)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, global_attention_mask: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple, LongformerQuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if global_attention_mask is None:
            if input_ids is None: logger.warning("It is not possible to automatically generate the `global_attention_mask` because input_ids is None. Please make sure that it is correctly set.")
            else: 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, head_mask=head_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).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()
        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1)
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = 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 LongformerQuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states,
        attentions=outputs.attentions, global_attentions=outputs.global_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(LongformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.longformer = LongformerModel(config, add_pooling_layer=False)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, global_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, LongformerTokenClassifierOutput]:
        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, head_mask=head_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()
            labels = labels.to(logits.device)
            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 LongformerTokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, global_attentions=outputs.global_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(LongformerPreTrainedModel):
    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.post_init()
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def forward(self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None,
    global_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, LongformerMultipleChoiceModelOutput]:
        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
        if global_attention_mask is None and input_ids is not None:
            logger.warning_once("Initializing global attention on multiple choice...")
            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,
        head_mask=head_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()
            labels = labels.to(reshaped_logits.device)
            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 LongformerMultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, global_attentions=outputs.global_attentions)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
