gena-lm-bert-base / modeling_bert.py

add AutoModel support

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	# code from huggingface transformers 4.17.0
	# coding=utf-8
	# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
	# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""PyTorch BERT model."""

	import importlib
	import math
	import os
	import warnings
	from dataclasses import dataclass
	from typing import Optional, Tuple

	import torch
	import torch.utils.checkpoint
	from packaging import version
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

	from transformers.activations import ACT2FN
	from transformers.file_utils import (
	ModelOutput,
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	replace_return_docstrings,
	)
	from transformers.modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	BaseModelOutputWithPoolingAndCrossAttentions,
	CausalLMOutputWithCrossAttentions,
	MaskedLMOutput,
	MultipleChoiceModelOutput,
	NextSentencePredictorOutput,
	QuestionAnsweringModelOutput,
	SequenceClassifierOutput,
	TokenClassifierOutput,
	)
	from transformers.modeling_utils import (
	PreTrainedModel,
	apply_chunking_to_forward,
	find_pruneable_heads_and_indices,
	prune_linear_layer,
	)
	from transformers.utils import logging
	from transformers.models.bert.configuration_bert import BertConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "bert-base-uncased"
	_CONFIG_FOR_DOC = "BertConfig"
	_TOKENIZER_FOR_DOC = "BertTokenizer"

	BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"bert-base-uncased",
	"bert-large-uncased",
	"bert-base-cased",
	"bert-large-cased",
	"bert-base-multilingual-uncased",
	"bert-base-multilingual-cased",
	"bert-base-chinese",
	"bert-base-german-cased",
	"bert-large-uncased-whole-word-masking",
	"bert-large-cased-whole-word-masking",
	"bert-large-uncased-whole-word-masking-finetuned-squad",
	"bert-large-cased-whole-word-masking-finetuned-squad",
	"bert-base-cased-finetuned-mrpc",
	"bert-base-german-dbmdz-cased",
	"bert-base-german-dbmdz-uncased",
	"cl-tohoku/bert-base-japanese",
	"cl-tohoku/bert-base-japanese-whole-word-masking",
	"cl-tohoku/bert-base-japanese-char",
	"cl-tohoku/bert-base-japanese-char-whole-word-masking",
	"TurkuNLP/bert-base-finnish-cased-v1",
	"TurkuNLP/bert-base-finnish-uncased-v1",
	"wietsedv/bert-base-dutch-cased",
	# See all BERT models at https://huggingface.co/models?filter=bert
	]


	def get_cls_by_name(name: str) -> type:
	"""Get class by its name and module path.

	Args:
	name (str): e.g., transfomers:T5ForConditionalGeneration, modeling_t5:my_class

	Returns:
	type: found class for `name`
	"""
	module_name, cls_name = name.split(':')
	return getattr(importlib.import_module(module_name), cls_name)


	def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
	"""Load tf checkpoints in a pytorch model."""
	try:
	import re

	import numpy as np
	import tensorflow as tf
	except ImportError:
	logger.error(
	"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
	"https://www.tensorflow.org/install/ for installation instructions."
	)
	raise
	tf_path = os.path.abspath(tf_checkpoint_path)
	logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
	# Load weights from TF model
	init_vars = tf.train.list_variables(tf_path)
	names = []
	arrays = []
	for name, shape in init_vars:
	logger.info(f"Loading TF weight {name} with shape {shape}")
	array = tf.train.load_variable(tf_path, name)
	names.append(name)
	arrays.append(array)

	for name, array in zip(names, arrays):
	name = name.split("/")
	# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
	# which are not required for using pretrained model
	if any(
	n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
	for n in name
	):
	logger.info(f"Skipping {'/'.join(name)}")
	continue
	pointer = model
	for m_name in name:
	if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
	scope_names = re.split(r"_(\d+)", m_name)
	else:
	scope_names = [m_name]
	if scope_names[0] == "kernel" or scope_names[0] == "gamma":
	pointer = getattr(pointer, "weight")
	elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
	pointer = getattr(pointer, "bias")
	elif scope_names[0] == "output_weights":
	pointer = getattr(pointer, "weight")
	elif scope_names[0] == "squad":
	pointer = getattr(pointer, "classifier")
	else:
	try:
	pointer = getattr(pointer, scope_names[0])
	except AttributeError:
	logger.info(f"Skipping {'/'.join(name)}")
	continue
	if len(scope_names) >= 2:
	num = int(scope_names[1])
	pointer = pointer[num]
	if m_name[-11:] == "_embeddings":
	pointer = getattr(pointer, "weight")
	elif m_name == "kernel":
	array = np.transpose(array)
	try:
	if pointer.shape != array.shape:
	raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
	except AssertionError as e:
	e.args += (pointer.shape, array.shape)
	raise
	logger.info(f"Initialize PyTorch weight {name}")
	pointer.data = torch.from_numpy(array)
	return model


	class BertEmbeddings(nn.Module):
	"""Construct the embeddings from word, position and token_type embeddings."""

	def __init__(self, config):
	super().__init__()
	self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
	if config.position_embedding_type == 'absolute':
	self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
	self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

	# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
	# any TensorFlow checkpoint file
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	# position_ids (1, len position emb) is contiguous in memory and exported when serialized
	self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
	self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
	if version.parse(torch.__version__) > version.parse("1.6.0"):
	self.register_buffer(
	"token_type_ids",
	torch.zeros(self.position_ids.size(), dtype=torch.long),
	persistent=False,
	)

	def forward(
	self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
	):
	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[:, past_key_values_length: seq_length + past_key_values_length]

	# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
	# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
	# issue #5664
	if token_type_ids is None:
	if hasattr(self, "token_type_ids"):
	buffered_token_type_ids = self.token_type_ids[:, :seq_length]
	buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
	token_type_ids = buffered_token_type_ids_expanded
	else:
	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)
	token_type_embeddings = self.token_type_embeddings(token_type_ids)
	embeddings = inputs_embeds + token_type_embeddings
	if self.position_embedding_type == "absolute":
	position_embeddings = self.position_embeddings(position_ids)
	embeddings += position_embeddings
	embeddings = self.LayerNorm(embeddings)
	embeddings = self.dropout(embeddings)
	return embeddings


	class BertSelfAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None, has_relative_attention_bias=False):
	"""Bert self-attention with abs/relative position encodings and sparsity.

	Args:
	config: HF model configuration loaded from json
	position_embedding_type (str, optional): absolute, relative_key, relative_key_query or
	relative_attention_bias . Defaults to None.
	has_relative_attention_bias (bool, optional): Use it's own relative embeddings matrix. Defaults to False.
	"""
	super().__init__()
	if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
	raise ValueError(
	f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
	f"heads ({config.num_attention_heads})"
	)

	self.config = config
	self.is_decoder = config.is_decoder
	# max_seq_len is used in absolute, relative_key & relative_key_query and to pre-define sparsity layout
	self.max_seq_len = config.max_position_embeddings

	self.num_attention_heads = config.num_attention_heads
	self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
	self.all_head_size = self.num_attention_heads * self.attention_head_size

	# sparse attention configuration
	self.is_sparse = False
	sparse_config_cls_name = getattr(config, 'sparse_config_cls', None)
	if sparse_config_cls_name:
	self.is_sparse = True
	sparse_config_cls = get_cls_by_name(sparse_config_cls_name)
	self.sparse_config = sparse_config_cls(**self.config.sparse_attention)

	if self.is_decoder and self.is_sparse:
	raise RuntimeError('SparseAttention with BertModel decoder is not currently supported!')

	self.query = nn.Linear(config.hidden_size, self.all_head_size)
	self.key = nn.Linear(config.hidden_size, self.all_head_size)
	self.value = nn.Linear(config.hidden_size, self.all_head_size)

	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
	self.softmax = nn.Softmax(dim=-1)
	self.position_embedding_type = position_embedding_type or getattr(config, "position_embedding_type", "absolute")
	self.has_relative_attention_bias = has_relative_attention_bias

	if self.is_sparse and self.position_embedding_type not in ['absolute', 'relative_attention_bias', 'rotary']:
	raise RuntimeError(f'SparseAttention supports `absolute`, `relative_attention_bias` and `rotary` position '
	f'embeddings, but: position_embeddings_type = {self.position_embedding_type}')

	if self.is_decoder and self.position_embedding_type == 'relative_attention_bias':
	raise RuntimeError(f'BertSelfAttention does not support `relative_attention_bias` with `is_decoder` '
	f' = {self.is_decoder}')

	if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
	self.max_position_embeddings = config.max_position_embeddings
	self.max_seq_len = 2 * config.max_position_embeddings
	self.distance_embedding = nn.Embedding(self.max_distance - 1, self.attention_head_size)
	elif self.position_embedding_type == 'relative_attention_bias' and self.has_relative_attention_bias:
	self.relative_attention_num_buckets = self.config.relative_attention_num_buckets
	self.relative_last_bucket_distance = self.config.relative_last_bucket_distance
	self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.num_attention_heads)
	elif self.position_embedding_type == 'rotary':
	self.rotary_base = getattr(config, 'rotary_base', None)
	self.rotary_dim = getattr(config, 'rotary_dim', self.attention_head_size)
	self.rotary_emb = RotaryEmbedding(self.rotary_dim, base=self.rotary_base)

	if self.is_sparse:
	try:
	from deepspeed.ops.sparse_attention import SparseSelfAttention
	except ImportError as e:
	logger.error(f'DeepSpeed is required for Sparse Ops: {e}')
	raise
	self.sparse_self_attention = SparseSelfAttention(self.sparse_config, max_seq_length=self.max_seq_len)

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

	def transpose_key_for_scores(self, x):
	# to remove redundant transpose in attention_scores matmul operation
	new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
	x = x.view(*new_x_shape)
	return x.permute(0, 2, 3, 1)

	@staticmethod
	def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
	"""
	Adapted from Mesh Tensorflow:
	https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593

	#todo: refactor, the same code is used in modeling_t5

	Translate relative position to a bucket number for relative attention. The relative position is defined as
	memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
	position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
	small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
	positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
	This should allow for more graceful generalization to longer sequences than the model has been trained on

	Args:
	relative_position: an int32 Tensor
	bidirectional: a boolean - whether the attention is bidirectional
	num_buckets: an integer
	max_distance: an integer

	Returns:
	a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
	"""
	relative_buckets = 0
	if bidirectional:
	num_buckets //= 2
	relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
	relative_position = torch.abs(relative_position)
	else:
	relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
	# now relative_position is in the range [0, inf)

	# half of the buckets are for exact increments in positions
	max_exact = num_buckets // 2
	is_small = relative_position < max_exact

	# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
	relative_postion_if_large = max_exact + (
	torch.log(relative_position.float() / max_exact)
	/ math.log(max_distance / max_exact)
	* (num_buckets - max_exact)
	).to(torch.long)
	relative_postion_if_large = torch.min(
	relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
	)

	relative_buckets += torch.where(is_small, relative_position, relative_postion_if_large)
	return relative_buckets

	def compute_bias(self, query_length, key_length):
	""" Compute binned relative position bias """
	context_position = torch.arange(query_length, dtype=torch.long)[:, None]
	memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
	relative_position = memory_position - context_position # shape (query_length, key_length)
	relative_position_bucket = self._relative_position_bucket(
	relative_position, # shape (query_length, key_length)
	bidirectional=(not self.is_decoder),
	num_buckets=self.relative_attention_num_buckets,
	max_distance=self.relative_last_bucket_distance,
	)
	relative_position_bucket = relative_position_bucket.to(self.relative_attention_bias.weight.device)
	values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
	values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
	return values

	def get_relative_attention_bias(self, position_bias, batch_size, query_length, key_length):
	if position_bias is None and self.has_relative_attention_bias:
	position_bias = self.compute_bias(query_length, key_length)
	position_bias = position_bias.repeat(batch_size, 1, 1, 1)
	return position_bias

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_value=None,
	position_bias=None,
	output_attentions=False,
	):
	mixed_query_layer = self.query(hidden_states)

	# If this is instantiated as a cross-attention module, the keys
	# and values come from an encoder; the attention mask needs to be
	# such that the encoder's padding tokens are not attended to.
	is_cross_attention = encoder_hidden_states is not None

	if is_cross_attention and past_key_value is not None:
	# reuse k,v, cross_attentions
	key_layer = past_key_value[0]
	value_layer = past_key_value[1]
	attention_mask = encoder_attention_mask
	elif is_cross_attention:
	key_layer = self.transpose_key_for_scores(self.key(encoder_hidden_states))
	value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
	attention_mask = encoder_attention_mask
	elif past_key_value is not None:
	key_layer = self.transpose_key_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))
	key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
	value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
	else:
	key_layer = self.transpose_key_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))

	query_layer = self.transpose_for_scores(mixed_query_layer)

	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_layer, value_layer)

	bs, seq_len, _ = hidden_states.shape
	# query shape: bs x n_heads x seq_len x head_dim
	# key shape: bs x n_heads x head_dim x seq_len

	if self.position_embedding_type == 'rotary':
	# todo: in key_layer and value_layer past states already concatenated
	# but rotary embeddings should not be applied to past states
	if past_key_value is not None:
	raise RuntimeError(f'past_key_values is not None are not supported in BertSelfAttention.forward with '
	f'position_embedding_type = {self.position_embedding_type}.')
	# traspose to bs x n_heads x seq_len x head_dim
	key_layer = key_layer.transpose(-1, -2)
	if self.rotary_dim < self.attention_head_size:
	query_rot = query_layer[..., :self.rotary_dim]
	query_pass = query_layer[..., self.rotary_dim:]

	key_rot = key_layer[..., :self.rotary_dim]
	key_pass = key_layer[..., self.rotary_dim:]
	else: # full rotary
	query_rot = query_layer
	key_rot = key_layer

	cos, sin = self.rotary_emb(key_rot, seq_len=seq_len)
	query_layer, key_layer = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, offset=0)
	if self.rotary_dim < self.attention_head_size:
	query_layer = torch.cat((query_layer, query_pass), dim=-1)
	key_layer = torch.cat((key_layer, key_pass), dim=-1)
	# transpose to bs x n_heads x head_dim x seq_len
	key_layer = key_layer.transpose(-1, -2)

	if not self.is_sparse:
	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer)

	if self.position_embedding_type in ["relative_key", "relative_key_query"]:
	position_ids_l = torch.arange(seq_len, dtype=torch.long, device=hidden_states.device).view(-1, 1)
	position_ids_r = torch.arange(seq_len, dtype=torch.long, device=hidden_states.device).view(1, -1)
	distance = position_ids_l - position_ids_r
	positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
	positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility

	# https://arxiv.org/abs/2009.13658
	if self.position_embedding_type == "relative_key":
	relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores
	elif self.position_embedding_type == "relative_key_query":
	relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	relative_position_scores_key = torch.einsum("bhdr,lrd->bhlr", key_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
	elif self.position_embedding_type == 'relative_attention_bias':
	position_bias = self.get_relative_attention_bias(position_bias, bs, seq_len, seq_len)
	attention_scores = attention_scores + position_bias

	attention_scores = attention_scores / math.sqrt(self.attention_head_size)
	if attention_mask is not None:
	# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
	attention_scores = attention_scores + attention_mask

	# Normalize the attention scores to probabilities.
	attention_probs = self.softmax(attention_scores)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.dropout(attention_probs)

	# Mask heads if we want to
	if head_mask is not None:
	attention_probs = attention_probs * head_mask

	context_layer = torch.matmul(attention_probs, value_layer)
	else:
	# sparse attention
	# todo: return attention_probs
	# todo: support relative_key -> need to change einsum with sparse operators..
	# sparse attention supports masks with following shapes:
	# key_padding_mask: (bs x seq_len) or (bs x 1 x 1 x seq_len)
	# attention_mask: seq_len x seq_len or (1 x 1 x seq_len x seq_len)
	if self.position_embedding_type == 'relative_attention_bias':
	position_bias = self.get_relative_attention_bias(position_bias, bs, seq_len, seq_len)

	query_dtype = query_layer.dtype
	if query_dtype != torch.half:
	# deepspeed sparse_self_attention supports only fp16 inputs
	# manually cast to half in case if running in fp32 or O1 modes
	query_layer, key_layer, value_layer = query_layer.half(), key_layer.half(), value_layer.half()
	# attention_mask = attention_mask.half()
	if position_bias is not None:
	position_bias = position_bias.half()
	context_layer = self.sparse_self_attention(query_layer, key_layer, value_layer, rpe=position_bias,
	key_padding_mask=attention_mask)
	if query_dtype == torch.float:
	context_layer = context_layer.float()

	context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
	new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
	context_layer = context_layer.view(new_context_layer_shape)

	if self.is_sparse and output_attentions:
	# todo: return sparse attention_scores or None, to not break the run
	raise RuntimeError(f'SparseAttention does not support output_attention = {output_attentions}')

	outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

	if self.position_embedding_type == 'relative_attention_bias':
	outputs = outputs + (position_bias,)

	if self.is_decoder:
	outputs = outputs + (past_key_value,)
	return outputs


	class BertSelfOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.pre_layer_norm = getattr(config, 'pre_layer_norm', False)
	self.bert_output_layer = True
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	if not self.pre_layer_norm:
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

	def forward(self, hidden_states, input_tensor):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	if not self.pre_layer_norm:
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	class BertAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None, has_relative_attention_bias=False):
	super().__init__()
	self.self = BertSelfAttention(config, position_embedding_type=position_embedding_type,
	has_relative_attention_bias=has_relative_attention_bias)
	self.output = BertSelfOutput(config)
	self.pruned_heads = set()

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

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

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

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


	class BertIntermediate(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


	class BertOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.pre_layer_norm = getattr(config, 'pre_layer_norm', False)
	self.bert_output_layer = True
	self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	if not self.pre_layer_norm:
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

	def forward(self, hidden_states, input_tensor):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	if not self.pre_layer_norm:
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	class BertLayer(nn.Module):
	def __init__(self, config, has_relative_attention_bias=False):
	super().__init__()
	self.chunk_size_feed_forward = config.chunk_size_feed_forward
	self.seq_len_dim = 1
	self.pre_layer_norm = getattr(config, 'pre_layer_norm', False)
	if self.pre_layer_norm:
	self.pre_attention_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.post_attention_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.attention = BertAttention(config, has_relative_attention_bias=has_relative_attention_bias)
	self.is_decoder = config.is_decoder
	self.add_cross_attention = config.add_cross_attention
	if self.add_cross_attention:
	if not self.is_decoder:
	raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
	self.crossattention = BertAttention(config, position_embedding_type="absolute")
	self.intermediate = BertIntermediate(config)
	self.output = BertOutput(config)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_value=None,
	position_bias=None,
	output_attentions=False,
	):
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
	self_attention_outputs = self.attention(
	hidden_states if not self.pre_layer_norm else self.pre_attention_ln(hidden_states),
	attention_mask,
	head_mask,
	position_bias=position_bias,
	output_attentions=output_attentions,
	past_key_value=self_attn_past_key_value,
	)
	attention_output = self_attention_outputs[0]

	# if decoder, the last output is tuple of self-attn cache
	if self.is_decoder:
	outputs = self_attention_outputs[1:-1]
	present_key_value = self_attention_outputs[-1]
	else:
	outputs = self_attention_outputs[1:] # add self attentions if we output attention weights

	cross_attn_present_key_value = None
	if self.is_decoder and encoder_hidden_states is not None:
	if not hasattr(self, "crossattention"):
	raise ValueError(
	f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
	)

	# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
	cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
	cross_attention_outputs = self.crossattention(
	attention_output,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	cross_attn_past_key_value,
	position_bias,
	output_attentions,
	)
	attention_output = cross_attention_outputs[0]
	outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights

	# add cross-attn cache to positions 3,4 of present_key_value tuple
	cross_attn_present_key_value = cross_attention_outputs[-1]
	present_key_value = present_key_value + cross_attn_present_key_value

	if self.pre_layer_norm:
	attention_output = hidden_states + attention_output

	layer_output = apply_chunking_to_forward(
	self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
	)

	outputs = (layer_output,) + outputs

	# if decoder, return the attn key/values as the last output
	if self.is_decoder:
	outputs = outputs + (present_key_value,)

	return outputs

	def feed_forward_chunk(self, attention_output):
	intermediate_inp = attention_output if not self.pre_layer_norm else self.post_attention_ln(attention_output)
	intermediate_output = self.intermediate(intermediate_inp)
	layer_output = self.output(intermediate_output, attention_output)
	if self.pre_layer_norm:
	layer_output = layer_output + attention_output
	return layer_output


	class BertEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.pre_layer_norm = getattr(config, 'pre_layer_norm', False)
	# last_layer_ln is used with pre_layer_norm:
	# pre_layer_norm: https://arxiv.org/abs/2002.04745
	# x = x + mha(ln(x))
	# x = x + ffn(mha)
	# if last_layer:
	# x = ln(x)
	# post_layer_norm (standart bert):
	# x = ln(x + mha(x))
	# x = ln(x + ffn(x))
	self.last_layer_norm = getattr(config, 'last_layer_norm', self.pre_layer_norm)
	if not self.pre_layer_norm and self.last_layer_norm:
	raise RuntimeError('last_layer_norm could be used only with pre_layer_norm=True')
	self.layer = nn.ModuleList(
	[BertLayer(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_hidden_layers)]
	)
	self.gradient_checkpointing = False
	if self.pre_layer_norm and self.last_layer_norm:
	self.last_layer_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=False,
	output_hidden_states=False,
	return_dict=True,
	):
	all_hidden_states = () if output_hidden_states else None
	all_self_attentions = () if output_attentions else None
	all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
	position_bias = None

	next_decoder_cache = () if use_cache else None
	for i, layer_module in enumerate(self.layer):
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	layer_head_mask = head_mask[i] if head_mask is not None else None
	past_key_value = past_key_values[i] if past_key_values is not None else None

	if self.gradient_checkpointing and self.training:

	if use_cache:
	logger.warning(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

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

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer_module),
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	layer_outputs = layer_module(
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	position_bias,
	output_attentions,
	)

	hidden_states = layer_outputs[0]
	if use_cache:
	next_decoder_cache += (layer_outputs[-1],)
	if output_attentions:
	all_self_attentions = all_self_attentions + (layer_outputs[1],)
	if self.config.add_cross_attention:
	all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

	if self.config.position_embedding_type == 'relative_attention_bias':
	if not output_attentions:
	position_bias = layer_outputs[1]
	else:
	position_bias = layer_outputs[2]

	if self.pre_layer_norm and self.last_layer_norm:
	hidden_states = self.last_layer_ln(hidden_states)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v
	for v in [
	hidden_states,
	next_decoder_cache,
	all_hidden_states,
	all_self_attentions,
	all_cross_attentions,
	]
	if v is not None
	)
	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=next_decoder_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	cross_attentions=all_cross_attentions,
	)


	class BertPooler(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):
	# We "pool" the model by simply taking the hidden state corresponding
	# to the first token.
	first_token_tensor = hidden_states[:, 0]
	pooled_output = self.dense(first_token_tensor)
	pooled_output = self.activation(pooled_output)
	return pooled_output


	class BertPredictionHeadTransform(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


	class BertLMPredictionHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.transform = BertPredictionHeadTransform(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


	class BertOnlyMLMHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.predictions = BertLMPredictionHead(config)

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


	class BertOnlyNSPHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.seq_relationship = nn.Linear(config.hidden_size, 2)

	def forward(self, pooled_output):
	seq_relationship_score = self.seq_relationship(pooled_output)
	return seq_relationship_score


	class BertPreTrainingHeads(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.predictions = BertLMPredictionHead(config)
	self.seq_relationship = nn.Linear(config.hidden_size, 2)

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


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

	config_class = BertConfig
	load_tf_weights = load_tf_weights_in_bert
	base_model_prefix = "bert"
	supports_gradient_checkpointing = True
	_keys_to_ignore_on_load_missing = [r"position_ids"]

	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
	std = self.config.initializer_range
	if hasattr(module, 'bert_output_layer') and self.config.pre_layer_norm:
	std /= math.sqrt(2.0 * self.config.num_hidden_layers)
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=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)

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, BertEncoder):
	module.gradient_checkpointing = value


	@dataclass
	class BertForPreTrainingOutput(ModelOutput):
	"""
	Output type of [`BertForPreTraining`].

	Args:
	loss (optional, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
	Total loss as the sum of the masked language modeling loss and the next sequence prediction
	(classification) loss.
	mlm_loss: masked language modeling loss
	nsp_loss: next sequence prediction loss
	prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
	Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
	seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
	Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
	before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the initial embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	mlm_loss: Optional[torch.FloatTensor] = None
	nsp_loss: Optional[torch.FloatTensor] = None
	prediction_logits: torch.FloatTensor = None
	seq_relationship_logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	attentions: Optional[Tuple[torch.FloatTensor]] = None


	BERT_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 ([`BertConfig`]): 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.
	"""

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

	Indices can be obtained using [`BertTokenizer`]. 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)
	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)
	head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	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 [`~file_utils.ModelOutput`] instead of a plain tuple.
	"""


	@add_start_docstrings(
	"The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
	BERT_START_DOCSTRING,
	)
	class BertModel(BertPreTrainedModel):
	"""

	The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
	cross-attention is added between the self-attention layers, following the architecture described in [Attention is
	all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
	Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

	To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
	to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
	`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
	"""

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

	if hasattr(config, 'sparse_attention'):
	self.is_sparse = True
	self.sparse_block_size = self.config.sparse_attention['block']
	else:
	self.is_sparse = False

	if self.is_sparse and self.config.is_decoder:
	raise RuntimeError('SparseAttention with BertModel decoder is not currently supported!')

	self.embeddings = BertEmbeddings(config)
	self.encoder = BertEncoder(config)

	self.pooler = BertPooler(config) if add_pooling_layer else None

	# Initialize weights and apply final processing
	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):
	"""
	Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
	class PreTrainedModel
	"""
	for layer, heads in heads_to_prune.items():
	self.encoder.layer[layer].attention.prune_heads(heads)

	@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	processor_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=BaseModelOutputWithPoolingAndCrossAttentions,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
	the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
	the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	"""
	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 self.config.is_decoder:
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	else:
	use_cache = False

	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")

	batch_size, seq_length = input_shape
	device = input_ids.device if input_ids is not None else inputs_embeds.device

	# if sparse attention is used, input sequence length should be divisible by block size
	if self.is_sparse and seq_length % self.sparse_block_size != 0:
	raise RuntimeError(f'BertModel with sparse attention is used, but seq_len = {seq_length} '
	f'is not divisible by block_size = {self.sparse_block_size}')

	# past_key_values_length
	past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

	if attention_mask is None:
	attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)

	if token_type_ids is None:
	if hasattr(self.embeddings, "token_type_ids"):
	buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
	buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
	token_type_ids = buffered_token_type_ids_expanded
	else:
	token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

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

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if self.config.is_decoder and encoder_hidden_states is not None:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
	if encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
	encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_extended_attention_mask = None

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

	embedding_output = self.embeddings(
	input_ids=input_ids,
	position_ids=position_ids,
	token_type_ids=token_type_ids,
	inputs_embeds=inputs_embeds,
	past_key_values_length=past_key_values_length,
	)
	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask=extended_attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_extended_attention_mask,
	past_key_values=past_key_values,
	use_cache=use_cache,
	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 BaseModelOutputWithPoolingAndCrossAttentions(
	last_hidden_state=sequence_output,
	pooler_output=pooled_output,
	past_key_values=encoder_outputs.past_key_values,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	cross_attentions=encoder_outputs.cross_attentions,
	)


	@add_start_docstrings(
	"""
	Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
	sentence prediction (classification)` head.
	""",
	BERT_START_DOCSTRING,
	)
	class BertForPreTraining(BertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.bert = BertModel(config)
	self.cls = BertPreTrainingHeads(config)

	# Initialize weights and apply final processing
	self.post_init()

	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(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=BertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	next_sentence_label=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (`torch.LongTensor` of shape `(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]`
	next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the next sequence prediction (classification) loss. Input should be a sequence
	pair (see `input_ids` docstring) Indices should be in `[0, 1]`:

	- 0 indicates sequence B is a continuation of sequence A,
	- 1 indicates sequence B is a random sequence.
	kwargs (`Dict[str, any]`, optional, defaults to {}):
	Used to hide legacy arguments that have been deprecated.

	Returns:

	Example:

	```python
	>>> from transformers import BertTokenizer, BertForPreTraining
	>>> import torch

	>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
	>>> model = BertForPreTraining.from_pretrained("bert-base-uncased")

	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
	>>> outputs = model(**inputs)

	>>> prediction_logits = outputs.prediction_logits
	>>> seq_relationship_logits = outputs.seq_relationship_logits
	```
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

	sequence_output, pooled_output = outputs[:2]
	prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)

	total_loss = None
	masked_lm_loss = None
	next_sentence_loss = None
	if labels is not None and next_sentence_label is not None:
	loss_fct = CrossEntropyLoss()
	masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
	next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
	total_loss = masked_lm_loss + next_sentence_loss

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

	return BertForPreTrainingOutput(
	loss=total_loss,
	mlm_loss=masked_lm_loss,
	nsp_loss=next_sentence_loss,
	prediction_logits=prediction_scores,
	seq_relationship_logits=seq_relationship_score,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""Bert Model with a `language modeling` head on top for CLM fine-tuning.""", BERT_START_DOCSTRING
	)
	class BertLMHeadModel(BertPreTrainedModel):

	_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)

	if not config.is_decoder:
	logger.warning("If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`")

	self.bert = BertModel(config, add_pooling_layer=False)
	self.cls = BertOnlyMLMHead(config)

	# Initialize weights and apply final processing
	self.post_init()

	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(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	labels=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
	if the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
	in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the left-to-right language modeling loss (next word prediction). 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 n `[0, ...,
	config.vocab_size]`
	past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up
	decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
	that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
	all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
	(see `past_key_values`).

	Returns:

	Example:

	```python
	>>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
	>>> import torch

	>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
	>>> config = BertConfig.from_pretrained("bert-base-cased")
	>>> config.is_decoder = True
	>>> model = BertLMHeadModel.from_pretrained("bert-base-cased", config=config)

	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
	>>> outputs = model(**inputs)

	>>> prediction_logits = outputs.logits
	```
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	if labels is not None:
	use_cache = False

	outputs = self.bert(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	past_key_values=past_key_values,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	sequence_output = outputs[0]
	prediction_scores = self.cls(sequence_output)

	lm_loss = None
	if labels is not None:
	# we are doing next-token prediction; shift prediction scores and input ids by one
	shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
	labels = labels[:, 1:].contiguous()
	loss_fct = CrossEntropyLoss()
	lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

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

	return CausalLMOutputWithCrossAttentions(
	loss=lm_loss,
	logits=prediction_scores,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	cross_attentions=outputs.cross_attentions,
	)

	def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
	input_shape = input_ids.shape
	# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
	if attention_mask is None:
	attention_mask = input_ids.new_ones(input_shape)

	# cut decoder_input_ids if past is used
	if past is not None:
	input_ids = input_ids[:, -1:]

	return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}

	def _reorder_cache(self, past, beam_idx):
	reordered_past = ()
	for layer_past in past:
	reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
	return reordered_past


	@add_start_docstrings("""Bert Model with a `language modeling` head on top.""", BERT_START_DOCSTRING)
	class BertForMaskedLM(BertPreTrainedModel):

	_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)

	if config.is_decoder:
	logger.warning(
	"If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
	"bi-directional self-attention."
	)

	self.bert = BertModel(config, add_pooling_layer=False)
	self.cls = BertOnlyMLMHead(config)

	# Initialize weights and apply final processing
	self.post_init()

	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(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	processor_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,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (`torch.LongTensor` of shape `(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.bert(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	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[2:]
	return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

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

	def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
	input_shape = input_ids.shape
	effective_batch_size = input_shape[0]

	# add a dummy token
	if self.config.pad_token_id is None:
	raise ValueError("The PAD token should be defined for generation")

	attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
	dummy_token = torch.full(
	(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
	)
	input_ids = torch.cat([input_ids, dummy_token], dim=1)

	return {"input_ids": input_ids, "attention_mask": attention_mask}


	@add_start_docstrings(
	"""Bert Model with a `next sentence prediction (classification)` head on top.""",
	BERT_START_DOCSTRING,
	)
	class BertForNextSentencePrediction(BertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.bert = BertModel(config)
	self.cls = BertOnlyNSPHead(config)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	**kwargs,
	):
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
	(see `input_ids` docstring). Indices should be in `[0, 1]`:

	- 0 indicates sequence B is a continuation of sequence A,
	- 1 indicates sequence B is a random sequence.

	Returns:

	Example:

	```python
	>>> from transformers import BertTokenizer, BertForNextSentencePrediction
	>>> import torch

	>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
	>>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased")

	>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
	>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
	>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")

	>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
	>>> logits = outputs.logits
	>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
	```
	"""

	if "next_sentence_label" in kwargs:
	warnings.warn(
	"The `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.",
	FutureWarning,
	)
	labels = kwargs.pop("next_sentence_label")

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

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

	pooled_output = outputs[1]

	seq_relationship_scores = self.cls(pooled_output)

	next_sentence_loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss()
	next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))

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

	return NextSentencePredictorOutput(
	loss=next_sentence_loss,
	logits=seq_relationship_scores,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
	output) e.g. for GLUE tasks.
	""",
	BERT_START_DOCSTRING,
	)
	class BertForSequenceClassification(BertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.config = config

	self.bert = BertModel(config)
	classifier_dropout = (
	config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
	)
	self.dropout = nn.Dropout(classifier_dropout)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	processor_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,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	pos_weight=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
	config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
	`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.bert(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=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)

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

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


	@add_start_docstrings(
	"""
	Bert 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.
	""",
	BERT_START_DOCSTRING,
	)
	class BertForMultipleChoice(BertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.bert = BertModel(config)
	classifier_dropout = (
	config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
	)
	self.dropout = nn.Dropout(classifier_dropout)
	self.classifier = nn.Linear(config.hidden_size, 1)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
	@add_code_sample_docstrings(
	processor_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=MultipleChoiceModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
	num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
	`input_ids` above)
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

	input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
	attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
	token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
	position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
	inputs_embeds = (
	inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
	if inputs_embeds is not None
	else None
	)

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

	pooled_output = outputs[1]

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

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

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

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


	@add_start_docstrings(
	"""
	Bert 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.
	""",
	BERT_START_DOCSTRING,
	)
	class BertForTokenClassification(BertPreTrainedModel):

	_keys_to_ignore_on_load_unexpected = [r"pooler"]

	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.config = config
	if getattr(self.config, 'problem_type', None) is None:
	self.config.problem_type = 'single_label_classification'

	self.bert = BertModel(config, add_pooling_layer=False)
	classifier_dropout = (
	config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
	)
	self.dropout = nn.Dropout(classifier_dropout)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	processor_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,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	labels_mask=None,
	pos_weight=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (`torch.LongTensor` of shape `(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.bert(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	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:
	if 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':
	if labels_mask is None:
	loss_fct = BCEWithLogitsLoss(pos_weight=pos_weight)
	loss = loss_fct(logits, labels)
	else:
	loss_fct = BCEWithLogitsLoss(reduction='none', pos_weight=pos_weight)
	loss = loss_fct(logits, labels)
	loss = loss * labels_mask.unsqueeze(-1)
	loss = loss.sum() / labels_mask.sum() if labels_mask.sum() != 0.0 else torch.tensor(0.0, device=logits.device)

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

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


	@add_start_docstrings(
	"""
	Bert 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`).
	""",
	BERT_START_DOCSTRING,
	)
	class BertForQuestionAnswering(BertPreTrainedModel):

	_keys_to_ignore_on_load_unexpected = [r"pooler"]

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

	self.bert = BertModel(config, add_pooling_layer=False)
	self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	processor_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,
	head_mask=None,
	inputs_embeds=None,
	start_positions=None,
	end_positions=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	start_positions (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for position (index) of the start of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
	are not taken into account for computing the loss.
	end_positions (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for position (index) of the end of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
	are not taken into account for computing the loss.
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.bert(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	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 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 = 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 QuestionAnsweringModelOutput(
	loss=total_loss,
	start_logits=start_logits,
	end_logits=end_logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	# todo: move to separate file with other position embeddings?
	# https://github.com/EleutherAI/gpt-neox/blob/8229d921d329266323706c01dd6778fa71649ac7/megatron/model/positional_embeddings.py#L24
	# https://blog.eleuther.ai/rotary-embeddings/
	class RotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, base=10000):
	super().__init__()
	inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
	self.register_buffer("inv_freq", inv_freq)
	self.seq_len_cached = None
	self.cos_cached = None
	self.sin_cached = None

	def forward(self, x, seq_dim=1, seq_len=None):
	if seq_len is None:
	seq_len = x.shape[seq_dim]
	if seq_len != self.seq_len_cached:
	self.seq_len_cached = seq_len
	t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
	self.cos_cached = emb.cos()[None, None, :, :]
	self.sin_cached = emb.sin()[None, None, :, :]
	return self.cos_cached, self.sin_cached


	def rotate_half(x):
	x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
	return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions


	def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0):
	cos, sin = cos[:, :, offset: q.shape[2] + offset, :], sin[:, :, offset: q.shape[2] + offset, :]
	return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)