jina-reranker-v1-turbo-en / modeling_bert.py

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implement compute_score api

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	# coding=utf-8
	# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
	# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
	# Copyright (c) 2023 Jina AI GmbH. 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 math
	import os
	import warnings
	from dataclasses import dataclass
	from typing import List, Optional, Tuple, Union
	import numpy as np

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

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

	# Torch implementation
	try:
	from torch.nn.functional import scaled_dot_product_attention
	except ImportError:
	scaled_dot_product_attention = None

	# This is used by encode but user may not have it installed
	try:
	from tqdm.autonotebook import trange

	has_tqdm = True
	except ImportError:
	has_tqdm = False

	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "bert-base-uncased"
	_CONFIG_FOR_DOC = "JinaBertConfig"

	# TokenClassification docstring
	_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = (
	"dbmdz/bert-large-cased-finetuned-conll03-english"
	)
	_TOKEN_CLASS_EXPECTED_OUTPUT = "['O', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC'] "
	_TOKEN_CLASS_EXPECTED_LOSS = 0.01

	# QuestionAnswering docstring
	_CHECKPOINT_FOR_QA = "deepset/bert-base-cased-squad2"
	_QA_EXPECTED_OUTPUT = "'a nice puppet'"
	_QA_EXPECTED_LOSS = 7.41
	_QA_TARGET_START_INDEX = 14
	_QA_TARGET_END_INDEX = 15

	# SequenceClassification docstring
	_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "textattack/bert-base-uncased-yelp-polarity"
	_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
	_SEQ_CLASS_EXPECTED_LOSS = 0.01


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

	def __init__(self, config: JinaBertConfig):
	super().__init__()
	self.word_embeddings = nn.Embedding(
	config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
	)
	if config.position_embedding_type != "alibi":
	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)),
	persistent=False,
	)
	self.register_buffer(
	"token_type_ids",
	torch.zeros(self.position_ids.size(), dtype=torch.long),
	persistent=False,
	)

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	past_key_values_length: int = 0,
	) -> torch.Tensor:
	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 JinaBertSelfAttention(nn.Module):
	def __init__(self, config: JinaBertConfig, position_embedding_type=None):
	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.attn_implementation = config.attn_implementation
	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

	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.position_embedding_type = position_embedding_type or getattr(
	config, "position_embedding_type", "absolute"
	)
	if (
	self.position_embedding_type == "relative_key"
	or self.position_embedding_type == "relative_key_query"
	):
	self.max_position_embeddings = config.max_position_embeddings
	self.distance_embedding = nn.Embedding(
	2 * config.max_position_embeddings - 1, self.attention_head_size
	)

	self.is_decoder = config.is_decoder

	def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
	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 forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	bias: Optional[torch.FloatTensor] = None,
	) -> Tuple[torch.Tensor]:
	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_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_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_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)

	use_cache = past_key_value is not None
	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)

	if self.attn_implementation == 'torch' and scaled_dot_product_attention is not None:
	b, _, s, _ = query_layer.shape
	new_bias = attention_mask + bias
	attn = scaled_dot_product_attention(query_layer, key_layer, value_layer, new_bias)
	attn = attn.permute(0, 2, 1, 3).contiguous()
	return (attn.view(b, s, self.all_head_size),)

	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	if (
	self.position_embedding_type == "relative_key"
	or self.position_embedding_type == "relative_key_query"
	):
	query_length, key_length = query_layer.shape[2], key_layer.shape[2]
	if use_cache:
	position_ids_l = torch.tensor(
	key_length - 1, dtype=torch.long, device=hidden_states.device
	).view(-1, 1)
	else:
	position_ids_l = torch.arange(
	query_length, dtype=torch.long, device=hidden_states.device
	).view(-1, 1)
	position_ids_r = torch.arange(
	key_length, 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

	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(
	"bhrd,lrd->bhlr", key_layer, positional_embedding
	)
	attention_scores = (
	attention_scores
	+ relative_position_scores_query
	+ relative_position_scores_key
	)

	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 = nn.functional.softmax(attention_scores + bias, dim=-1)

	# 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)

	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)

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

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


	class JinaBertSelfOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

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


	class JinaBertAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None):
	super().__init__()
	self.self = JinaBertSelfAttention(
	config, position_embedding_type=position_embedding_type
	)
	self.output = JinaBertSelfOutput(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: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	bias: Optional[torch.FloatTensor] = None,
	) -> Tuple[torch.Tensor]:
	self_outputs = self.self(
	hidden_states,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	output_attentions,
	bias,
	)
	attention_output = self.output(self_outputs[0], hidden_states)
	outputs = (attention_output,) + self_outputs[
	1:
	] # add attentions if we output them
	return outputs


	class JinaBertIntermediate(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
	if isinstance(config.hidden_act, str):
	self.intermediate_act_fn = ACT2FN[config.hidden_act]
	else:
	self.intermediate_act_fn = config.hidden_act

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.intermediate_act_fn(hidden_states)
	return hidden_states


	class JinaBertOutput(nn.Module):
	def __init__(self, config: JinaBertConfig):
	super().__init__()
	self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

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


	class JinaBertGLUMLP(nn.Module):
	def __init__(self, config: JinaBertConfig):
	super().__init__()
	self.config = config
	self.gated_layers = nn.Linear(
	config.hidden_size, config.intermediate_size * 2, bias=False
	)
	if config.feed_forward_type == 'reglu':
	self.act = nn.ReLU()
	elif config.feed_forward_type == 'geglu':
	self.act = nn.GELU()
	else:
	raise ValueError(
	f"feed_forward_type {config.feed_forward_type} not supported"
	)
	self.wo = nn.Linear(config.intermediate_size, config.hidden_size)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	residual_connection = hidden_states
	# compute the activation
	hidden_states = self.gated_layers(hidden_states)
	gated = hidden_states[:, :, : self.config.intermediate_size]
	non_gated = hidden_states[:, :, self.config.intermediate_size :]
	hidden_states = self.act(gated) * non_gated
	hidden_states = self.dropout(hidden_states)
	# multiply by the second matrix
	hidden_states = self.wo(hidden_states)
	# add the residual connection and post-LN
	hidden_states = self.layernorm(hidden_states + residual_connection)
	return hidden_states


	class JinaBertLayer(nn.Module):
	def __init__(self, config: JinaBertConfig):
	super().__init__()
	self.chunk_size_feed_forward = config.chunk_size_feed_forward
	self.seq_len_dim = 1
	self.attention = JinaBertAttention(config)
	self.is_decoder = config.is_decoder
	self.add_cross_attention = config.add_cross_attention
	self.feed_forward_type = config.feed_forward_type
	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 = JinaBertAttention(
	config, position_embedding_type="absolute"
	)
	if self.feed_forward_type.endswith('glu'):
	self.mlp = JinaBertGLUMLP(config)
	else:
	self.intermediate = JinaBertIntermediate(config)
	self.output = JinaBertOutput(config)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	bias: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	# 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,
	attention_mask,
	head_mask,
	output_attentions=output_attentions,
	past_key_value=self_attn_past_key_value,
	bias=bias,
	)
	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,
	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.feed_forward_type.endswith('glu'):
	layer_output = self.mlp(attention_output)
	else:
	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_output = self.intermediate(attention_output)
	layer_output = self.output(intermediate_output, attention_output)
	return layer_output


	class JinaBertEncoder(nn.Module):
	def __init__(self, config: JinaBertConfig):
	super().__init__()
	self.config = config
	self.layer = nn.ModuleList(
	[JinaBertLayer(config) for _ in range(config.num_hidden_layers)]
	)
	self.gradient_checkpointing = False
	self.num_attention_heads = config.num_attention_heads
	self.register_buffer(
	"alibi",
	self.rebuild_alibi_tensor(size=config.max_position_embeddings),
	persistent=False,
	)

	def rebuild_alibi_tensor(
	self, size: int, device: Optional[Union[torch.device, str]] = None
	):
	# Alibi
	# Following https://github.com/ofirpress/attention_with_linear_biases/issues/5 (Implementation 1)
	# In the causal case, you can exploit the fact that softmax is invariant to a uniform translation
	# of the logits, which makes the math work out after applying causal masking. If no causal masking
	# will be applied, it is necessary to construct the diagonal mask.
	n_heads = self.num_attention_heads

	def _get_alibi_head_slopes(n_heads: int) -> List[float]:
	def get_slopes_power_of_2(n):
	start = 2 (-(2 -(math.log2(n) - 3)))
	ratio = start
	return [start * ratio**i for i in range(n)]

	if math.log2(n_heads).is_integer():
	return get_slopes_power_of_2(
	n_heads
	) # In the paper, we only train models that have 2^a heads for some a. This function has
	else: # some good properties that only occur when the input is a power of 2. To maintain that even
	closest_power_of_2 = 2 ** math.floor(
	math.log2(n_heads)
	) # when the number of heads is not a power of 2, we use this workaround.
	slope_a = get_slopes_power_of_2(closest_power_of_2)
	slope_b = _get_alibi_head_slopes(2 * closest_power_of_2)[0::2][
	: n_heads - closest_power_of_2
	]
	# quick fix on large jump at header=12
	slope_b = [x / 2 for x in slope_b]
	return slope_a + slope_b

	context_position = torch.arange(size, device=device)[:, None]
	memory_position = torch.arange(size, device=device)[None, :]
	relative_position = torch.abs(memory_position - context_position)
	# [n_heads, max_token_length, max_token_length]
	relative_position = relative_position.unsqueeze(0).expand(n_heads, -1, -1)
	slopes = torch.Tensor(_get_alibi_head_slopes(n_heads)).to(device) * -1
	alibi = slopes.unsqueeze(1).unsqueeze(1) * relative_position
	# [1, n_heads, max_token_length, max_token_length]
	alibi = alibi.unsqueeze(0)
	assert alibi.shape == torch.Size([1, n_heads, size, size])

	self._current_alibi_size = size
	return alibi

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = False,
	output_hidden_states: Optional[bool] = False,
	return_dict: Optional[bool] = True,
	) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
	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
	)

	# Add alibi matrix to extended_attention_mask
	_, seqlen, _ = hidden_states.size()
	if self._current_alibi_size < seqlen:
	# Rebuild the alibi tensor when needed
	warnings.warn(
	f'Increasing alibi size from {self._current_alibi_size} to {seqlen}.'
	)
	self.register_buffer(
	"alibi",
	self.rebuild_alibi_tensor(size=seqlen, device=hidden_states.device).to(
	hidden_states.dtype
	),
	persistent=False,
	)
	elif self.alibi.device != hidden_states.device:
	# Device catch-up
	self.alibi = self.alibi.to(hidden_states.device)

	alibi_bias = self.alibi[:, :, :seqlen, :seqlen]
	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning_once(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	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:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, past_key_value, 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,
	alibi_bias,
	)
	else:
	layer_outputs = layer_module(
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	alibi_bias,
	past_key_value,
	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 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 JinaBertPooler(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.activation = nn.Tanh()

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	# 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 JinaBertPredictionHeadTransform(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: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.transform_act_fn(hidden_states)
	hidden_states = self.LayerNorm(hidden_states)
	return hidden_states


	class JinaBertLMPredictionHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.transform = JinaBertPredictionHeadTransform(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 JinaBertOnlyMLMHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.predictions = JinaBertLMPredictionHead(config)

	def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
	prediction_scores = self.predictions(sequence_output)
	return prediction_scores


	class JinaBertOnlyNSPHead(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 JinaBertPreTrainingHeads(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.predictions = JinaBertLMPredictionHead(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 JinaBertPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = JinaBertConfig
	load_tf_weights = load_tf_weights_in_bert
	base_model_prefix = "bert"
	supports_gradient_checkpointing = True
	_no_split_modules = ["JinaBertLayer"]

	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_()
	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, JinaBertEncoder):
	module.gradient_checkpointing = value


	@dataclass
	class JinaBertForPreTrainingOutput(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.
	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
	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 [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.FloatTensor` of shape `({0})`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

	[What are attention masks?](../glossary#attention-mask)
	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 [`~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 JinaBertModel(JinaBertPreTrainedModel):
	"""

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

	self.emb_pooler = config.emb_pooler
	self._name_or_path = config._name_or_path
	if self.emb_pooler:
	from transformers import AutoTokenizer

	self.tokenizer = AutoTokenizer.from_pretrained(config._name_or_path)

	self.embeddings = JinaBertEmbeddings(config)
	self.encoder = JinaBertEncoder(config)

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

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

	@torch.inference_mode()
	def encode(
	self: 'JinaBertModel',
	sentences: Union[str, List[str]],
	batch_size: int = 32,
	show_progress_bar: Optional[bool] = None,
	output_value: str = 'sentence_embedding',
	convert_to_numpy: bool = True,
	convert_to_tensor: bool = False,
	device: Optional[torch.device] = None,
	normalize_embeddings: bool = False,
	**tokenizer_kwargs,
	) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
	"""
	Computes sentence embeddings

	Args:
	sentences(`str` or `List[str]`):
	Sentence or sentences to be encoded
	batch_size(`int`, optional, defaults to 32):
	Batch size for the computation
	show_progress_bar(`bool`, optional, defaults to None):
	Show a progress bar when encoding sentences.
	If set to None, progress bar is only shown when `logger.level == logging.INFO` or `logger.level == logging.DEBUG`.
	output_value(`str`, optional, defaults to 'sentence_embedding'):
	Default sentence_embedding, to get sentence embeddings.
	Can be set to token_embeddings to get wordpiece token embeddings.
	Set to None, to get all output values
	convert_to_numpy(`bool`, optional, defaults to True):
	If true, the output is a list of numpy vectors.
	Else, it is a list of pytorch tensors.
	convert_to_tensor(`bool`, optional, defaults to False):
	If true, you get one large tensor as return.
	Overwrites any setting from convert_to_numpy
	device(`torch.device`, optional, defaults to None):
	Which torch.device to use for the computation
	normalize_embeddings(`bool`, optional, defaults to False):
	If set to true, returned vectors will have length 1. In that case, the faster dot-product (util.dot_score) instead of cosine similarity can be used.
	tokenizer_kwargs(`Dict[str, Any]`, optional, defaults to {}):
	Keyword arguments for the tokenizer

	Returns:
	By default, a list of tensors is returned.
	If convert_to_tensor, a stacked tensor is returned.
	If convert_to_numpy, a numpy matrix is returned.
	"""
	if not self.emb_pooler:
	warnings.warn("No emb_pooler specified, defaulting to mean pooling.")
	self.emb_pooler = 'mean'
	from transformers import AutoTokenizer

	self.tokenizer = AutoTokenizer.from_pretrained(self._name_or_path)
	is_training = self.training
	self.eval()

	if show_progress_bar is None:
	show_progress_bar = (
	logger.getEffectiveLevel() == logging.INFO
	or logger.getEffectiveLevel() == logging.DEBUG
	)

	if convert_to_tensor:
	convert_to_numpy = False

	if output_value != 'sentence_embedding':
	convert_to_tensor = False
	convert_to_numpy = False

	input_was_string = False
	if isinstance(sentences, str) or not hasattr(sentences, '__len__'):
	sentences = [sentences]
	input_was_string = True

	if device is not None:
	self.to(device)

	# TODO: Maybe use better length heuristic?
	permutation = np.argsort([-len(i) for i in sentences])
	inverse_permutation = np.argsort(permutation)
	sentences = [sentences[idx] for idx in permutation]

	tokenizer_kwargs['padding'] = tokenizer_kwargs.get('padding', True)
	tokenizer_kwargs['max_length'] = tokenizer_kwargs.get('max_length', 8192)
	tokenizer_kwargs['truncation'] = tokenizer_kwargs.get('truncation', True)

	all_embeddings = []

	if has_tqdm:
	range_iter = trange(
	0,
	len(sentences),
	batch_size,
	desc="Encoding",
	disable=not show_progress_bar,
	)
	else:
	range_iter = range(0, len(sentences), batch_size)

	for i in range_iter:
	encoded_input = self.tokenizer(
	sentences[i : i + batch_size],
	return_tensors='pt',
	**tokenizer_kwargs,
	).to(self.device)
	token_embs = self.forward(**encoded_input)[0]

	# Accumulate in fp32 to avoid overflow
	token_embs = token_embs.float()

	if output_value == 'token_embeddings':
	raise NotImplementedError
	elif output_value is None:
	raise NotImplementedError
	else:
	embeddings = self.mean_pooling(
	token_embs, encoded_input['attention_mask']
	)

	if normalize_embeddings:
	embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)

	if convert_to_numpy:
	embeddings = embeddings.cpu()
	all_embeddings.extend(embeddings)

	all_embeddings = [all_embeddings[idx] for idx in inverse_permutation]

	if convert_to_tensor:
	all_embeddings = torch.stack(all_embeddings)
	elif convert_to_numpy:
	all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])

	if input_was_string:
	all_embeddings = all_embeddings[0]

	self.train(is_training)
	return all_embeddings

	def mean_pooling(
	self, token_embeddings: torch.Tensor, attention_mask: torch.Tensor
	):
	input_mask_expanded = (
	attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
	)
	return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
	input_mask_expanded.sum(1), min=1e-9
	)

	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(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=BaseModelOutputWithPoolingAndCrossAttentions,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
	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:
	# self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
	input_shape = input_ids.size()
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

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

	# 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
	)

	# 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 JinaBertForPreTraining(JinaBertPreTrainedModel):
	_tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]

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

	self.bert = JinaBertModel(config)
	self.cls = JinaBertPreTrainingHeads(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=JinaBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	next_sentence_label: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], JinaBertForPreTrainingOutput]:
	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:
	"""
	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
	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 JinaBertForPreTrainingOutput(
	loss=total_loss,
	prediction_logits=prediction_scores,
	seq_relationship_logits=seq_relationship_score,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""JinaBert Model with a `language modeling` head on top for CLM fine-tuning.""",
	BERT_START_DOCSTRING,
	)
	class JinaBertLMHeadModel(JinaBertPreTrainedModel):
	_tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]

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

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

	self.bert = JinaBertModel(config, add_pooling_layer=False)
	self.cls = JinaBertOnlyMLMHead(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(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=CausalLMOutputWithCrossAttentions,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.Tensor]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
	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`).
	"""
	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_key_values=None,
	attention_mask=None,
	use_cache=True,
	**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_key_values is used
	if past_key_values is not None:
	input_ids = input_ids[:, -1:]

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

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


	@add_start_docstrings(
	"""JinaBert Model with a `language modeling` head on top.""", BERT_START_DOCSTRING
	)
	class JinaBertForMaskedLM(JinaBertPreTrainedModel):
	_tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]

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

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

	self.bert = JinaBertModel(config, add_pooling_layer=False)
	self.cls = JinaBertOnlyMLMHead(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(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=MaskedLMOutput,
	config_class=_CONFIG_FOR_DOC,
	expected_output="'paris'",
	expected_loss=0.88,
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
	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(
	"""JinaBert Model with a `next sentence prediction (classification)` head on top.""",
	BERT_START_DOCSTRING,
	)
	class JinaBertForNextSentencePrediction(JinaBertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.bert = JinaBertModel(config)
	self.cls = JinaBertOnlyNSPHead(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: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	**kwargs,
	) -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]:
	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:
	"""

	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(
	"""
	JinaBert 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 JinaBertForSequenceClassification(JinaBertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.config = config

	self._name_or_path = config._name_or_path

	self.bert = JinaBertModel(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(
	checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
	output_type=SequenceClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
	expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
	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()
	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,
	)

	@torch.inference_mode()
	def compute_score(
	self,
	sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],
	batch_size: int = 32,
	device: Optional[torch.device] = None,
	**tokenizer_kwargs,
	):
	assert isinstance(sentence_pairs, list)
	if isinstance(sentence_pairs[0], str):
	sentence_pairs = [sentence_pairs]

	if not hasattr(self, 'tokenizer'):
	from transformers import AutoTokenizer

	self._tokenizer = AutoTokenizer.from_pretrained(self._name_or_path)

	is_training = self.training
	self.eval()

	if device is not None:
	self.to(device)

	all_scores = []
	for start_index in range(
	0, len(sentence_pairs), batch_size
	):
	sentences_batch = sentence_pairs[
	start_index : start_index + (batch_size or self._eval_batch_size)
	]
	inputs = self._tokenizer(
	sentences_batch,
	padding=True,
	truncation=True,
	return_tensors='pt',
	**tokenizer_kwargs,
	).to(self.device)

	scores = (
	self.forward(**inputs, return_dict=True)
	.logits.view(
	-1,
	)
	.float()
	)
	scores = torch.sigmoid(scores)
	all_scores.extend(scores.cpu().numpy().tolist())

	if len(all_scores) == 1:
	return all_scores[0]
	return all_scores


	@add_start_docstrings(
	"""
	JinaBert 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 JinaBertForMultipleChoice(JinaBertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.bert = JinaBertModel(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(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=MultipleChoiceModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
	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(
	"""
	JinaBert 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 JinaBertForTokenClassification(JinaBertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels

	self.bert = JinaBertModel(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(
	checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
	output_type=TokenClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
	expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
	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:
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

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

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


	@add_start_docstrings(
	"""
	JinaBert 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 JinaBertForQuestionAnswering(JinaBertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels

	self.bert = JinaBertModel(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(
	checkpoint=_CHECKPOINT_FOR_QA,
	output_type=QuestionAnsweringModelOutput,
	config_class=_CONFIG_FOR_DOC,
	qa_target_start_index=_QA_TARGET_START_INDEX,
	qa_target_end_index=_QA_TARGET_END_INDEX,
	expected_output=_QA_EXPECTED_OUTPUT,
	expected_loss=_QA_EXPECTED_LOSS,
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	start_positions: Optional[torch.Tensor] = None,
	end_positions: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
	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,
	)