modeling_deberta_v2.py
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modeling_deberta_v2.py
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	# coding=utf-8
	# Copyright 2020 Microsoft and the Hugging Face 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 DeBERTa-v2 model. """

	import math
	from collections.abc import Sequence

	import numpy as np
	import torch
	from torch import _softmax_backward_data, nn
	from torch.nn import CrossEntropyLoss, LayerNorm

	from ...activations import ACT2FN
	from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
	from ...modeling_outputs import (
	BaseModelOutput,
	MaskedLMOutput,
	QuestionAnsweringModelOutput,
	SequenceClassifierOutput,
	TokenClassifierOutput,
	)
	from ...modeling_utils import PreTrainedModel
	from ...utils import logging
	from .configuration_deberta_v2 import DebertaV2Config


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "DebertaV2Config"
	_TOKENIZER_FOR_DOC = "DebertaV2Tokenizer"
	_CHECKPOINT_FOR_DOC = "microsoft/deberta-v2-xlarge"

	DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"microsoft/deberta-v2-xlarge",
	"microsoft/deberta-v2-xxlarge",
	"microsoft/deberta-v2-xlarge-mnli",
	"microsoft/deberta-v2-xxlarge-mnli",
	]


	# Copied from transformers.models.deberta.modeling_deberta.ContextPooler
	class ContextPooler(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
	self.dropout = StableDropout(config.pooler_dropout)
	self.config = config

	def forward(self, hidden_states):
	# We "pool" the model by simply taking the hidden state corresponding
	# to the first token.

	context_token = hidden_states[:, 0]
	context_token = self.dropout(context_token)
	pooled_output = self.dense(context_token)
	pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
	return pooled_output

	@property
	def output_dim(self):
	return self.config.hidden_size


	# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2
	class XSoftmax(torch.autograd.Function):
	"""
	Masked Softmax which is optimized for saving memory

	Args:
	input (:obj:`torch.tensor`): The input tensor that will apply softmax.
	mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
	dim (int): The dimension that will apply softmax

	Example::

	>>> import torch
	>>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax

	>>> # Make a tensor
	>>> x = torch.randn([4,20,100])

	>>> # Create a mask
	>>> mask = (x>0).int()

	>>> y = XSoftmax.apply(x, mask, dim=-1)
	"""

	@staticmethod
	def forward(self, input, mask, dim):
	self.dim = dim
	rmask = ~(mask.bool())

	output = input.masked_fill(rmask, float("-inf"))
	output = torch.softmax(output, self.dim)
	output.masked_fill_(rmask, 0)
	self.save_for_backward(output)
	return output

	@staticmethod
	def backward(self, grad_output):
	(output,) = self.saved_tensors
	inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)
	return inputGrad, None, None


	# Copied from transformers.models.deberta.modeling_deberta.DropoutContext
	class DropoutContext(object):
	def __init__(self):
	self.dropout = 0
	self.mask = None
	self.scale = 1
	self.reuse_mask = True


	# Copied from transformers.models.deberta.modeling_deberta.get_mask
	def get_mask(input, local_context):
	if not isinstance(local_context, DropoutContext):
	dropout = local_context
	mask = None
	else:
	dropout = local_context.dropout
	dropout *= local_context.scale
	mask = local_context.mask if local_context.reuse_mask else None

	if dropout > 0 and mask is None:
	mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool()

	if isinstance(local_context, DropoutContext):
	if local_context.mask is None:
	local_context.mask = mask

	return mask, dropout


	# Copied from transformers.models.deberta.modeling_deberta.XDropout
	class XDropout(torch.autograd.Function):
	"""Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""

	@staticmethod
	def forward(ctx, input, local_ctx):
	mask, dropout = get_mask(input, local_ctx)
	ctx.scale = 1.0 / (1 - dropout)
	if dropout > 0:
	ctx.save_for_backward(mask)
	return input.masked_fill(mask, 0) * ctx.scale
	else:
	return input

	@staticmethod
	def backward(ctx, grad_output):
	if ctx.scale > 1:
	(mask,) = ctx.saved_tensors
	return grad_output.masked_fill(mask, 0) * ctx.scale, None
	else:
	return grad_output, None


	# Copied from transformers.models.deberta.modeling_deberta.StableDropout
	class StableDropout(torch.nn.Module):
	"""
	Optimized dropout module for stabilizing the training

	Args:
	drop_prob (float): the dropout probabilities
	"""

	def __init__(self, drop_prob):
	super().__init__()
	self.drop_prob = drop_prob
	self.count = 0
	self.context_stack = None

	def forward(self, x):
	"""
	Call the module

	Args:
	x (:obj:`torch.tensor`): The input tensor to apply dropout
	"""
	if self.training and self.drop_prob > 0:
	return XDropout.apply(x, self.get_context())
	return x

	def clear_context(self):
	self.count = 0
	self.context_stack = None

	def init_context(self, reuse_mask=True, scale=1):
	if self.context_stack is None:
	self.context_stack = []
	self.count = 0
	for c in self.context_stack:
	c.reuse_mask = reuse_mask
	c.scale = scale

	def get_context(self):
	if self.context_stack is not None:
	if self.count >= len(self.context_stack):
	self.context_stack.append(DropoutContext())
	ctx = self.context_stack[self.count]
	ctx.dropout = self.drop_prob
	self.count += 1
	return ctx
	else:
	return self.drop_prob


	# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm
	class DebertaV2SelfOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
	self.dropout = StableDropout(config.hidden_dropout_prob)

	def forward(self, hidden_states, input_tensor):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2
	class DebertaV2Attention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.self = DisentangledSelfAttention(config)
	self.output = DebertaV2SelfOutput(config)
	self.config = config

	def forward(
	self,
	hidden_states,
	attention_mask,
	return_att=False,
	query_states=None,
	relative_pos=None,
	rel_embeddings=None,
	):
	self_output = self.self(
	hidden_states,
	attention_mask,
	return_att,
	query_states=query_states,
	relative_pos=relative_pos,
	rel_embeddings=rel_embeddings,
	)
	if return_att:
	self_output, att_matrix = self_output
	if query_states is None:
	query_states = hidden_states
	attention_output = self.output(self_output, query_states)

	if return_att:
	return (attention_output, att_matrix)
	else:
	return attention_output


	# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2
	class DebertaV2Intermediate(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):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.intermediate_act_fn(hidden_states)
	return hidden_states


	# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm
	class DebertaV2Output(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
	self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
	self.dropout = StableDropout(config.hidden_dropout_prob)
	self.config = config

	def forward(self, hidden_states, input_tensor):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2
	class DebertaV2Layer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.attention = DebertaV2Attention(config)
	self.intermediate = DebertaV2Intermediate(config)
	self.output = DebertaV2Output(config)

	def forward(
	self,
	hidden_states,
	attention_mask,
	return_att=False,
	query_states=None,
	relative_pos=None,
	rel_embeddings=None,
	):
	attention_output = self.attention(
	hidden_states,
	attention_mask,
	return_att=return_att,
	query_states=query_states,
	relative_pos=relative_pos,
	rel_embeddings=rel_embeddings,
	)
	if return_att:
	attention_output, att_matrix = attention_output
	intermediate_output = self.intermediate(attention_output)
	layer_output = self.output(intermediate_output, attention_output)
	if return_att:
	return (layer_output, att_matrix)
	else:
	return layer_output


	class ConvLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	kernel_size = getattr(config, "conv_kernel_size", 3)
	groups = getattr(config, "conv_groups", 1)
	self.conv_act = getattr(config, "conv_act", "tanh")
	self.conv = torch.nn.Conv1d(
	config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups
	)
	self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
	self.dropout = StableDropout(config.hidden_dropout_prob)
	self.config = config

	def forward(self, hidden_states, residual_states, input_mask):
	out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()
	rmask = (1 - input_mask).bool()
	out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)
	out = ACT2FN[self.conv_act](self.dropout(out))

	layer_norm_input = residual_states + out
	output = self.LayerNorm(layer_norm_input).to(layer_norm_input)

	if input_mask is None:
	output_states = output
	else:
	if input_mask.dim() != layer_norm_input.dim():
	if input_mask.dim() == 4:
	input_mask = input_mask.squeeze(1).squeeze(1)
	input_mask = input_mask.unsqueeze(2)

	input_mask = input_mask.to(output.dtype)
	output_states = output * input_mask

	return output_states


	class DebertaV2Encoder(nn.Module):
	"""Modified BertEncoder with relative position bias support"""

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

	self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])
	self.relative_attention = getattr(config, "relative_attention", False)

	if self.relative_attention:
	self.max_relative_positions = getattr(config, "max_relative_positions", -1)
	if self.max_relative_positions < 1:
	self.max_relative_positions = config.max_position_embeddings

	self.position_buckets = getattr(config, "position_buckets", -1)
	pos_ebd_size = self.max_relative_positions * 2

	if self.position_buckets > 0:
	pos_ebd_size = self.position_buckets * 2

	self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)

	self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("\|")]

	if "layer_norm" in self.norm_rel_ebd:
	self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)

	self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None

	def get_rel_embedding(self):
	rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
	if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd):
	rel_embeddings = self.LayerNorm(rel_embeddings)
	return rel_embeddings

	def get_attention_mask(self, attention_mask):
	if attention_mask.dim() <= 2:
	extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
	attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
	attention_mask = attention_mask.byte()
	elif attention_mask.dim() == 3:
	attention_mask = attention_mask.unsqueeze(1)

	return attention_mask

	def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
	if self.relative_attention and relative_pos is None:
	q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)
	relative_pos = build_relative_position(
	q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions
	)
	return relative_pos

	def forward(
	self,
	hidden_states,
	attention_mask,
	output_hidden_states=True,
	output_attentions=False,
	query_states=None,
	relative_pos=None,
	return_dict=True,
	):
	if attention_mask.dim() <= 2:
	input_mask = attention_mask
	else:
	input_mask = (attention_mask.sum(-2) > 0).byte()
	attention_mask = self.get_attention_mask(attention_mask)
	relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)

	all_hidden_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	if isinstance(hidden_states, Sequence):
	next_kv = hidden_states[0]
	else:
	next_kv = hidden_states
	rel_embeddings = self.get_rel_embedding()
	output_states = next_kv
	for i, layer_module in enumerate(self.layer):

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (output_states,)

	output_states = layer_module(
	next_kv,
	attention_mask,
	output_attentions,
	query_states=query_states,
	relative_pos=relative_pos,
	rel_embeddings=rel_embeddings,
	)
	if output_attentions:
	output_states, att_m = output_states

	if i == 0 and self.conv is not None:
	output_states = self.conv(hidden_states, output_states, input_mask)

	if query_states is not None:
	query_states = output_states
	if isinstance(hidden_states, Sequence):
	next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
	else:
	next_kv = output_states

	if output_attentions:
	all_attentions = all_attentions + (att_m,)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (output_states,)

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


	def make_log_bucket_position(relative_pos, bucket_size, max_position):
	sign = np.sign(relative_pos)
	mid = bucket_size // 2
	abs_pos = np.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, np.abs(relative_pos))
	log_pos = np.ceil(np.log(abs_pos / mid) / np.log((max_position - 1) / mid) * (mid - 1)) + mid
	bucket_pos = np.where(abs_pos <= mid, relative_pos, log_pos * sign).astype(np.int)
	return bucket_pos


	def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):
	"""
	Build relative position according to the query and key

	We assume the absolute position of query :math:`P_q` is range from (0, query_size) and the absolute position of key
	:math:`P_k` is range from (0, key_size), The relative positions from query to key is :math:`R_{q \\rightarrow k} =
	P_q - P_k`

	Args:
	query_size (int): the length of query
	key_size (int): the length of key
	bucket_size (int): the size of position bucket
	max_position (int): the maxium allowed absolute positoin

	Return:
	:obj:`torch.LongTensor`: A tensor with shape [1, query_size, key_size]

	"""
	q_ids = np.arange(0, query_size)
	k_ids = np.arange(0, key_size)
	rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0], 1))
	if bucket_size > 0 and max_position > 0:
	rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)
	rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long)
	rel_pos_ids = rel_pos_ids[:query_size, :]
	rel_pos_ids = rel_pos_ids.unsqueeze(0)
	return rel_pos_ids


	@torch.jit.script
	# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand
	def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
	return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])


	@torch.jit.script
	# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand
	def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
	return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])


	@torch.jit.script
	# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand
	def pos_dynamic_expand(pos_index, p2c_att, key_layer):
	return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))


	class DisentangledSelfAttention(torch.nn.Module):
	"""
	Disentangled self-attention module

	Parameters:
	config (:obj:`DebertaV2Config`):
	A model config class instance with the configuration to build a new model. The schema is similar to
	`BertConfig`, for more details, please refer :class:`~transformers.DebertaV2Config`

	"""

	def __init__(self, config):
	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_attention_heads = config.num_attention_heads
	_attention_head_size = config.hidden_size // config.num_attention_heads
	self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size)
	self.all_head_size = self.num_attention_heads * self.attention_head_size
	self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
	self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
	self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)

	self.share_att_key = getattr(config, "share_att_key", False)
	self.pos_att_type = config.pos_att_type
	self.relative_attention = getattr(config, "relative_attention", False)

	if self.relative_attention:
	self.position_buckets = getattr(config, "position_buckets", -1)
	self.max_relative_positions = getattr(config, "max_relative_positions", -1)
	if self.max_relative_positions < 1:
	self.max_relative_positions = config.max_position_embeddings
	self.pos_ebd_size = self.max_relative_positions
	if self.position_buckets > 0:
	self.pos_ebd_size = self.position_buckets

	self.pos_dropout = StableDropout(config.hidden_dropout_prob)

	if not self.share_att_key:
	if "c2p" in self.pos_att_type or "p2p" in self.pos_att_type:
	self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
	if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
	self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size)

	self.dropout = StableDropout(config.attention_probs_dropout_prob)

	def transpose_for_scores(self, x, attention_heads):
	new_x_shape = x.size()[:-1] + (attention_heads, -1)
	x = x.view(*new_x_shape)
	return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1))

	def forward(
	self,
	hidden_states,
	attention_mask,
	return_att=False,
	query_states=None,
	relative_pos=None,
	rel_embeddings=None,
	):
	"""
	Call the module

	Args:
	hidden_states (:obj:`torch.FloatTensor`):
	Input states to the module usually the output from previous layer, it will be the Q,K and V in
	`Attention(Q,K,V)`

	attention_mask (:obj:`torch.ByteTensor`):
	An attention mask matrix of shape [`B`, `N`, `N`] where `B` is the batch size, `N` is the maximum
	sequence length in which element [i,j] = `1` means the `i` th token in the input can attend to the `j`
	th token.

	return_att (:obj:`bool`, optional):
	Whether return the attention matrix.

	query_states (:obj:`torch.FloatTensor`, optional):
	The `Q` state in `Attention(Q,K,V)`.

	relative_pos (:obj:`torch.LongTensor`):
	The relative position encoding between the tokens in the sequence. It's of shape [`B`, `N`, `N`] with
	values ranging in [`-max_relative_positions`, `max_relative_positions`].

	rel_embeddings (:obj:`torch.FloatTensor`):
	The embedding of relative distances. It's a tensor of shape [:math:`2 \\times
	\\text{max_relative_positions}`, `hidden_size`].


	"""
	if query_states is None:
	query_states = hidden_states
	query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads)
	key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads)
	value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads)

	rel_att = None
	# Take the dot product between "query" and "key" to get the raw attention scores.
	scale_factor = 1
	if "c2p" in self.pos_att_type:
	scale_factor += 1
	if "p2c" in self.pos_att_type:
	scale_factor += 1
	if "p2p" in self.pos_att_type:
	scale_factor += 1
	scale = math.sqrt(query_layer.size(-1) * scale_factor)
	attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale
	if self.relative_attention:
	rel_embeddings = self.pos_dropout(rel_embeddings)
	rel_att = self.disentangled_attention_bias(
	query_layer, key_layer, relative_pos, rel_embeddings, scale_factor
	)

	if rel_att is not None:
	attention_scores = attention_scores + rel_att
	attention_scores = attention_scores
	attention_scores = attention_scores.view(
	-1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1)
	)

	# bsz x height x length x dimension
	attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
	attention_probs = self.dropout(attention_probs)
	context_layer = torch.bmm(
	attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer
	)
	context_layer = (
	context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1))
	.permute(0, 2, 1, 3)
	.contiguous()
	)
	new_context_layer_shape = context_layer.size()[:-2] + (-1,)
	context_layer = context_layer.view(*new_context_layer_shape)
	if return_att:
	return (context_layer, attention_probs)
	else:
	return context_layer

	def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
	if relative_pos is None:
	q = query_layer.size(-2)
	relative_pos = build_relative_position(
	q, key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions
	)
	if relative_pos.dim() == 2:
	relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
	elif relative_pos.dim() == 3:
	relative_pos = relative_pos.unsqueeze(1)
	# bsz x height x query x key
	elif relative_pos.dim() != 4:
	raise ValueError(f"Relative postion ids must be of dim 2 or 3 or 4. {relative_pos.dim()}")

	att_span = self.pos_ebd_size
	relative_pos = relative_pos.long().to(query_layer.device)

	rel_embeddings = rel_embeddings[self.pos_ebd_size - att_span : self.pos_ebd_size + att_span, :].unsqueeze(0)
	if self.share_att_key:
	pos_query_layer = self.transpose_for_scores(
	self.query_proj(rel_embeddings), self.num_attention_heads
	).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1)
	pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat(
	query_layer.size(0) // self.num_attention_heads, 1, 1
	)
	else:
	if "c2p" in self.pos_att_type or "p2p" in self.pos_att_type:
	pos_key_layer = self.transpose_for_scores(
	self.pos_key_proj(rel_embeddings), self.num_attention_heads
	).repeat(
	query_layer.size(0) // self.num_attention_heads, 1, 1
	) # .split(self.all_head_size, dim=-1)
	if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
	pos_query_layer = self.transpose_for_scores(
	self.pos_query_proj(rel_embeddings), self.num_attention_heads
	).repeat(
	query_layer.size(0) // self.num_attention_heads, 1, 1
	) # .split(self.all_head_size, dim=-1)

	score = 0
	# content->position
	if "c2p" in self.pos_att_type:
	scale = math.sqrt(pos_key_layer.size(-1) * scale_factor)
	c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2))
	c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)
	c2p_att = torch.gather(
	c2p_att,
	dim=-1,
	index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]),
	)
	score += c2p_att / scale

	# position->content
	if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
	scale = math.sqrt(pos_query_layer.size(-1) * scale_factor)
	if key_layer.size(-2) != query_layer.size(-2):
	r_pos = build_relative_position(
	key_layer.size(-2),
	key_layer.size(-2),
	bucket_size=self.position_buckets,
	max_position=self.max_relative_positions,
	).to(query_layer.device)
	r_pos = r_pos.unsqueeze(0)
	else:
	r_pos = relative_pos

	p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
	if query_layer.size(-2) != key_layer.size(-2):
	pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)

	if "p2c" in self.pos_att_type:
	p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2))
	p2c_att = torch.gather(
	p2c_att,
	dim=-1,
	index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]),
	).transpose(-1, -2)
	if query_layer.size(-2) != key_layer.size(-2):
	p2c_att = torch.gather(
	p2c_att,
	dim=-2,
	index=pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))),
	)
	score += p2c_att / scale

	# position->position
	if "p2p" in self.pos_att_type:
	pos_query = pos_query_layer[:, :, att_span:, :]
	p2p_att = torch.matmul(pos_query, pos_key_layer.transpose(-1, -2))
	p2p_att = p2p_att.expand(query_layer.size()[:2] + p2p_att.size()[2:])
	if query_layer.size(-2) != key_layer.size(-2):
	p2p_att = torch.gather(
	p2p_att,
	dim=-2,
	index=pos_index.expand(query_layer.size()[:2] + (pos_index.size(-2), p2p_att.size(-1))),
	)
	p2p_att = torch.gather(
	p2p_att,
	dim=-1,
	index=c2p_pos.expand(
	[query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]
	),
	)
	score += p2p_att

	return score


	# Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm
	class DebertaV2Embeddings(nn.Module):
	"""Construct the embeddings from word, position and token_type embeddings."""

	def __init__(self, config):
	super().__init__()
	pad_token_id = getattr(config, "pad_token_id", 0)
	self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
	self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)

	self.position_biased_input = getattr(config, "position_biased_input", True)
	if not self.position_biased_input:
	self.position_embeddings = None
	else:
	self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)

	if config.type_vocab_size > 0:
	self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)

	if self.embedding_size != config.hidden_size:
	self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False)
	self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
	self.dropout = StableDropout(config.hidden_dropout_prob)
	self.config = config

	# position_ids (1, len position emb) is contiguous in memory and exported when serialized
	self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))

	def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
	if input_ids is not None:
	input_shape = input_ids.size()
	else:
	input_shape = inputs_embeds.size()[:-1]

	seq_length = input_shape[1]

	if position_ids is None:
	position_ids = self.position_ids[:, :seq_length]

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

	if inputs_embeds is None:
	inputs_embeds = self.word_embeddings(input_ids)

	if self.position_embeddings is not None:
	position_embeddings = self.position_embeddings(position_ids.long())
	else:
	position_embeddings = torch.zeros_like(inputs_embeds)

	embeddings = inputs_embeds
	if self.position_biased_input:
	embeddings += position_embeddings
	if self.config.type_vocab_size > 0:
	token_type_embeddings = self.token_type_embeddings(token_type_ids)
	embeddings += token_type_embeddings

	if self.embedding_size != self.config.hidden_size:
	embeddings = self.embed_proj(embeddings)

	embeddings = self.LayerNorm(embeddings)

	if mask is not None:
	if mask.dim() != embeddings.dim():
	if mask.dim() == 4:
	mask = mask.squeeze(1).squeeze(1)
	mask = mask.unsqueeze(2)
	mask = mask.to(embeddings.dtype)

	embeddings = embeddings * mask

	embeddings = self.dropout(embeddings)
	return embeddings


	# Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2
	class DebertaV2PreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = DebertaV2Config
	base_model_prefix = "deberta"
	_keys_to_ignore_on_load_missing = ["position_ids"]
	_keys_to_ignore_on_load_unexpected = ["position_embeddings"]

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

	def _init_weights(self, module):
	"""Initialize the weights."""
	if isinstance(module, nn.Linear):
	# 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)
	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_()

	def _pre_load_hook(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
	"""
	Removes the classifier if it doesn't have the correct number of labels.
	"""
	self_state = self.state_dict()
	if (
	("classifier.weight" in self_state)
	and ("classifier.weight" in state_dict)
	and self_state["classifier.weight"].size() != state_dict["classifier.weight"].size()
	):
	logger.warning(
	f"The checkpoint classifier head has a shape {state_dict['classifier.weight'].size()} and this model "
	f"classifier head has a shape {self_state['classifier.weight'].size()}. Ignoring the checkpoint "
	f"weights. You should train your model on new data."
	)
	del state_dict["classifier.weight"]
	if "classifier.bias" in state_dict:
	del state_dict["classifier.bias"]


	DEBERTA_START_DOCSTRING = r"""
	The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention
	<https://arxiv.org/abs/2006.03654>`_ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build on top of
	BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
	improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

	This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
	subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
	general usage and behavior.```


	Parameters:
	config (:class:`~transformers.DebertaV2Config`): 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.
	"""

	DEBERTA_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.DebertaV2Tokenizer`. 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`):
	Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	`What are attention masks? <../glossary.html#attention-mask>`__
	token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{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.html#token-type-ids>`_
	position_ids (:obj:`torch.LongTensor` of shape :obj:`{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.html#position-ids>`_
	inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
	Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert `input_ids` indices into associated vectors
	than the model's internal embedding lookup matrix.
	output_attentions (:obj:`bool`, `optional`):
	Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
	tensors for more detail.
	output_hidden_states (:obj:`bool`, `optional`):
	Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
	more detail.
	return_dict (:obj:`bool`, `optional`):
	Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
	"""


	@add_start_docstrings(
	"The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.",
	DEBERTA_START_DOCSTRING,
	)
	# Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2
	class DebertaV2Model(DebertaV2PreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.embeddings = DebertaV2Embeddings(config)
	self.encoder = DebertaV2Encoder(config)
	self.z_steps = 0
	self.config = config
	self.init_weights()

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

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

	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
	"""
	raise NotImplementedError("The prune function is not implemented in DeBERTa model.")

	@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=SequenceClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	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)

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

	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask,
	output_hidden_states=True,
	output_attentions=output_attentions,
	return_dict=return_dict,
	)
	encoded_layers = encoder_outputs[1]

	if self.z_steps > 1:
	hidden_states = encoded_layers[-2]
	layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
	query_states = encoded_layers[-1]
	rel_embeddings = self.encoder.get_rel_embedding()
	attention_mask = self.encoder.get_attention_mask(attention_mask)
	rel_pos = self.encoder.get_rel_pos(embedding_output)
	for layer in layers[1:]:
	query_states = layer(
	hidden_states,
	attention_mask,
	return_att=False,
	query_states=query_states,
	relative_pos=rel_pos,
	rel_embeddings=rel_embeddings,
	)
	encoded_layers.append(query_states)

	sequence_output = encoded_layers[-1]

	if not return_dict:
	return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]

	return BaseModelOutput(
	last_hidden_state=sequence_output,
	hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
	attentions=encoder_outputs.attentions,
	)


	@add_start_docstrings("""DeBERTa Model with a `language modeling` head on top. """, DEBERTA_START_DOCSTRING)
	# Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM with Deberta->DebertaV2
	class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]
	_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

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

	self.deberta = DebertaV2Model(config)
	self.cls = DebertaV2OnlyMLMHead(config)

	self.init_weights()

	def get_output_embeddings(self):
	return self.cls.predictions.decoder

	def set_output_embeddings(self, new_embeddings):
	self.cls.predictions.decoder = new_embeddings

	@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=MaskedLMOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	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`):
	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]``
	"""

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

	outputs = self.deberta(
	input_ids,
	attention_mask=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.cls(sequence_output)

	masked_lm_loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss() # -100 index = padding token
	masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

	if not return_dict:
	output = (prediction_scores,) + outputs[1:]
	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,
	)


	# copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta
	class DebertaV2PredictionHeadTransform(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	if isinstance(config.hidden_act, str):
	self.transform_act_fn = ACT2FN[config.hidden_act]
	else:
	self.transform_act_fn = config.hidden_act
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

	def forward(self, hidden_states):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.transform_act_fn(hidden_states)
	hidden_states = self.LayerNorm(hidden_states)
	return hidden_states


	# copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta
	class DebertaV2LMPredictionHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.transform = DebertaV2PredictionHeadTransform(config)

	# The output weights are the same as the input embeddings, but there is
	# an output-only bias for each token.
	self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

	self.bias = nn.Parameter(torch.zeros(config.vocab_size))

	# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
	self.decoder.bias = self.bias

	def forward(self, hidden_states):
	hidden_states = self.transform(hidden_states)
	hidden_states = self.decoder(hidden_states)
	return hidden_states


	# copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta
	class DebertaV2OnlyMLMHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.predictions = DebertaV2LMPredictionHead(config)

	def forward(self, sequence_output):
	prediction_scores = self.predictions(sequence_output)
	return prediction_scores


	@add_start_docstrings(
	"""
	DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
	pooled output) e.g. for GLUE tasks.
	""",
	DEBERTA_START_DOCSTRING,
	)
	# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2
	class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	num_labels = getattr(config, "num_labels", 2)
	self.num_labels = num_labels

	self.deberta = DebertaV2Model(config)
	self.pooler = ContextPooler(config)
	output_dim = self.pooler.output_dim

	self.classifier = torch.nn.Linear(output_dim, num_labels)
	drop_out = getattr(config, "cls_dropout", None)
	drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
	self.dropout = StableDropout(drop_out)

	self.init_weights()

	def get_input_embeddings(self):
	return self.deberta.get_input_embeddings()

	def set_input_embeddings(self, new_embeddings):
	self.deberta.set_input_embeddings(new_embeddings)

	@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=SequenceClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	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`):
	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

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

	encoder_layer = outputs[0]
	pooled_output = self.pooler(encoder_layer)
	pooled_output = self.dropout(pooled_output)
	logits = self.classifier(pooled_output)

	loss = None
	if labels is not None:
	if self.num_labels == 1:
	# regression task
	loss_fn = torch.nn.MSELoss()
	logits = logits.view(-1).to(labels.dtype)
	loss = loss_fn(logits, labels.view(-1))
	elif labels.dim() == 1 or labels.size(-1) == 1:
	label_index = (labels >= 0).nonzero()
	labels = labels.long()
	if label_index.size(0) > 0:
	labeled_logits = torch.gather(logits, 0, label_index.expand(label_index.size(0), logits.size(1)))
	labels = torch.gather(labels, 0, label_index.view(-1))
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
	else:
	loss = torch.tensor(0).to(logits)
	else:
	log_softmax = torch.nn.LogSoftmax(-1)
	loss = -((log_softmax(logits) * labels).sum(-1)).mean()
	if not return_dict:
	output = (logits,) + outputs[1:]
	return ((loss,) + output) if loss is not None else output
	else:
	return SequenceClassifierOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	DeBERTa 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.
	""",
	DEBERTA_START_DOCSTRING,
	)
	# Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2
	class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]

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

	self.deberta = DebertaV2Model(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_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=TokenClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	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`):
	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.deberta(
	input_ids,
	attention_mask=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[1:]
	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(
	"""
	DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
	layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
	""",
	DEBERTA_START_DOCSTRING,
	)
	# Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering with Deberta->DebertaV2
	class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]

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

	self.deberta = DebertaV2Model(config)
	self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

	self.init_weights()

	@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=QuestionAnsweringModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	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`):
	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 (:obj:`sequence_length`). Position outside of the
	sequence are not taken into account for computing the loss.
	end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	Labels for position (index) of the end of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
	sequence are not taken into account for computing the loss.
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.deberta(
	input_ids,
	attention_mask=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[1:]
	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,
	)

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