diff --git "a/DecompX/src/modeling_bert.py" "b/DecompX/src/modeling_bert.py"
new file mode 100644--- /dev/null
+++ "b/DecompX/src/modeling_bert.py"
@@ -0,0 +1,2452 @@
+# 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 math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from packaging import version
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from .globenc_utils import GlobencConfig, GlobencOutput
+
+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,
+    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 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 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
+
+def output_builder(input_vector, output_mode):
+    if output_mode is None:
+        return None
+    elif output_mode == "vector":
+        return (input_vector,)
+    elif output_mode == "norm":
+        return (torch.norm(input_vector, dim=-1),)
+    elif output_mode == "both":
+        return ((torch.norm(input_vector, dim=-1), input_vector),)
+    elif output_mode == "distance_based":
+        recomposed_vectors = torch.sum(input_vector, dim=-2, keepdim=True)
+        importance_matrix = -torch.nn.functional.pairwise_distance(input_vector, recomposed_vectors, p=1)
+        norm_y = torch.norm(recomposed_vectors, dim=-1, p=1)
+        maxed = torch.maximum(torch.zeros(1, device=norm_y.device), norm_y + importance_matrix)
+        return (maxed / (torch.sum(maxed, dim=-2, keepdim=True) + 1e-12),)
+
+
+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)
+        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):
+        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.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):
+        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_for_scores_for_decomposed(self, x):
+        # x: (B, N, N, H*V)
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        # x: (B, N, N, H, V)
+        x = x.view(new_x_shape)
+        # x: (B, H, N, N, V)
+        return x.permute(0, 3, 1, 2, 4)
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attribution_vectors: Optional[torch.FloatTensor] = None,
+            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,
+            globenc_ready: Optional[bool] = None,  # added by Fayyaz / Modarressi
+    ) -> 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
+        decomposed_value_layer = 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))
+            if attribution_vectors is not None:
+                decomposed_value_layer = torch.einsum("bijd,vd->bijv", attribution_vectors, self.value.weight)
+                decomposed_value_layer = self.transpose_for_scores_for_decomposed(decomposed_value_layer)
+            
+
+        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)
+
+        # 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":
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+            position_ids_r = torch.arange(seq_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, 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)
+
+        # added by Fayyaz / Modarressi
+        # -------------------------------
+        if globenc_ready:
+            outputs = (context_layer, attention_probs, value_layer, decomposed_value_layer)
+            return outputs
+        # -------------------------------
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        if self.is_decoder:
+            outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class BertSelfOutput(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,
+                globenc_ready=False):  # added by Fayyaz / Modarressi
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        # hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        pre_ln_states = hidden_states + input_tensor  # added by Fayyaz / Modarressi
+        post_ln_states = self.LayerNorm(pre_ln_states)  # added by Fayyaz / Modarressi
+        # added by Fayyaz / Modarressi
+        if globenc_ready:
+            return post_ln_states, pre_ln_states
+        else:
+            return post_ln_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config, position_embedding_type=None):
+        super().__init__()
+        self.self = BertSelfAttention(config, position_embedding_type=position_embedding_type)
+        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: torch.Tensor,
+            attribution_vectors: Optional[torch.FloatTensor] = None,
+            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,
+            globenc_ready: Optional[bool] = None,  # added by Fayyaz / Modarressi
+    ) -> Tuple[torch.Tensor]:
+        self_outputs = self.self(
+            hidden_states,
+            attribution_vectors,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+            globenc_ready=globenc_ready,  # added by Fayyaz / Modarressi
+        )
+        attention_output = self.output(
+            self_outputs[0],
+            hidden_states,
+            globenc_ready=globenc_ready,  # added by Goro Kobayashi (Edited by Fayyaz / Modarressi)
+        )
+
+        # Added by Fayyaz / Modarressi
+        # -------------------------------
+        if globenc_ready:
+            _, attention_probs, value_layer, decomposed_value_layer = self_outputs
+            attention_output, pre_ln_states = attention_output
+            outputs = (attention_output, attention_probs,) + (value_layer, decomposed_value_layer, pre_ln_states)  # add attentions and norms if we output them
+            return outputs
+        # -------------------------------
+
+        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: torch.Tensor, globenc_ready: Optional[bool] = False) -> torch.Tensor:
+        pre_act_hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(pre_act_hidden_states)
+        if globenc_ready:
+            return hidden_states, pre_act_hidden_states
+        return hidden_states, None
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, globenc_ready: Optional[bool] = False):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        # hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        # return hidden_states
+        # Added by Fayyaz / Modarressi
+        # -------------------------------
+        pre_ln_states = hidden_states + input_tensor
+        hidden_states = self.LayerNorm(pre_ln_states)
+        if globenc_ready:
+            return hidden_states, pre_ln_states
+        return hidden_states, None
+        # -------------------------------
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = BertAttention(config)
+        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)
+        self.similarity_fn = torch.nn.CosineSimilarity(dim=-1)
+
+        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
+
+    def bias_decomposer(self, bias, attribution_vectors, bias_decomp_type="absdot"):
+        # Decomposes the input bias based on similarity to the attribution vectors
+        # Args:
+        #   bias: a bias vector (all_head_size)
+        #   attribution_vectors: the attribution vectors from token j to i (b, i, j, all_head_size) :: (batch, seq_length, seq_length, all_head_size) 
+
+        if bias_decomp_type == "absdot":
+            weights = torch.abs(torch.einsum("bskd,d->bsk", attribution_vectors, bias))
+        elif bias_decomp_type == "abssim":
+            weights = torch.abs(torch.nn.functional.cosine_similarity(attribution_vectors, bias, dim=-1))
+            weights = (torch.norm(attribution_vectors, dim=-1) != 0) * weights
+        elif bias_decomp_type == "norm":
+            weights = torch.norm(attribution_vectors, dim=-1)
+        elif bias_decomp_type == "equal":
+            weights = (torch.norm(attribution_vectors, dim=-1) != 0) * 1.0
+        elif bias_decomp_type == "cls":
+            weights = torch.zeros(attribution_vectors.shape[:-1], device=attribution_vectors.device)
+            weights[:,:,0] = 1.0
+        elif bias_decomp_type == "dot":
+            weights = torch.einsum("bskd,d->bsk", attribution_vectors, bias)
+        elif bias_decomp_type == "biastoken":
+            attrib_shape = attribution_vectors.shape
+            if attrib_shape[1] == attrib_shape[2]:
+                attribution_vectors = torch.concat([attribution_vectors, torch.zeros((attrib_shape[0], attrib_shape[1], 1, attrib_shape[3]), device=attribution_vectors.device)], dim=-2)
+            attribution_vectors[:,:,-1] = attribution_vectors[:,:,-1] + bias
+            return attribution_vectors
+
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12)
+        weighted_bias = torch.matmul(weights.unsqueeze(dim=-1), bias.unsqueeze(dim=0))
+        return attribution_vectors + weighted_bias
+
+
+    def ln_decomposer(self, attribution_vectors, pre_ln_states, gamma, beta, eps, include_biases=True, bias_decomp_type="absdot"):
+        mean = pre_ln_states.mean(-1, keepdim=True)  # (batch, seq_len, 1) m(y=Σy_j)
+        var = (pre_ln_states - mean).pow(2).mean(-1, keepdim=True).unsqueeze(dim=2)  # (batch, seq_len, 1, 1)  s(y)
+
+        each_mean = attribution_vectors.mean(-1, keepdim=True)  # (batch, seq_len, seq_len, 1) m(y_j)
+
+        normalized_layer = torch.div(attribution_vectors - each_mean,
+                                         (var + eps) ** (1 / 2))  # (batch, seq_len, seq_len, all_head_size)
+
+        post_ln_layer = torch.einsum('bskd,d->bskd', normalized_layer,
+                                         gamma)  # (batch, seq_len, seq_len, all_head_size)
+        
+        if include_biases:
+            return self.bias_decomposer(beta, post_ln_layer, bias_decomp_type=bias_decomp_type)
+        else:
+            return post_ln_layer 
+
+
+    def gelu_linear_approximation(self, intermediate_hidden_states, intermediate_output):
+        def phi(x):
+            return (1 + torch.erf(x / math.sqrt(2))) / 2.
+        
+        def normal_pdf(x):
+            return torch.exp(-(x**2) / 2) / math.sqrt(2. * math.pi)
+
+        def gelu_deriv(x):
+            return phi(x)+x*normal_pdf(x)
+        
+        m = gelu_deriv(intermediate_hidden_states)
+        b = intermediate_output - m * intermediate_hidden_states
+        return m, b
+
+
+    def gelu_decomposition(self, attribution_vectors, intermediate_hidden_states, intermediate_output, bias_decomp_type):
+        m, b = self.gelu_linear_approximation(intermediate_hidden_states, intermediate_output)
+        mx = attribution_vectors * m.unsqueeze(dim=-2)
+
+        if bias_decomp_type == "absdot":
+            weights = torch.abs(torch.einsum("bskl,bsl->bsk", mx, b))
+        elif bias_decomp_type == "abssim":
+            weights = torch.abs(torch.nn.functional.cosine_similarity(mx, b))
+            weights = (torch.norm(mx, dim=-1) != 0) * weights
+        elif bias_decomp_type == "norm":
+            weights = torch.norm(mx, dim=-1)
+        elif bias_decomp_type == "equal":
+            weights = (torch.norm(mx, dim=-1) != 0) * 1.0
+        elif bias_decomp_type == "cls":
+            weights = torch.zeros(mx.shape[:-1], device=mx.device)
+            weights[:,:,0] = 1.0
+
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12)
+        weighted_bias = torch.einsum("bsl,bsk->bskl", b, weights)
+        return mx + weighted_bias
+
+    def gelu_zo_decomposition(self, attribution_vectors, intermediate_hidden_states, intermediate_output):
+        m = intermediate_output / (intermediate_hidden_states + 1e-12)
+        mx = attribution_vectors * m.unsqueeze(dim=-2)
+        return mx
+    
+    def ffn_decomposer(self, attribution_vectors, intermediate_hidden_states, intermediate_output, include_biases=True, approximation_type="GeLU_LA", bias_decomp_type="absdot"):
+        post_first_layer = torch.einsum("ld,bskd->bskl", self.intermediate.dense.weight, attribution_vectors)
+        if include_biases:
+            post_first_layer = self.bias_decomposer(self.intermediate.dense.bias, post_first_layer, bias_decomp_type=bias_decomp_type)
+
+        if approximation_type == "ReLU":
+            mask_for_gelu_approx = (intermediate_hidden_states > 0)
+            post_act_first_layer = torch.einsum("bskl, bsl->bskl", post_first_layer, mask_for_gelu_approx)
+            post_act_first_layer = post_first_layer * mask_for_gelu_approx.unsqueeze(dim=-2)
+        elif approximation_type == "GeLU_LA":
+            post_act_first_layer = self.gelu_decomposition(post_first_layer, intermediate_hidden_states, intermediate_output, bias_decomp_type=bias_decomp_type)
+        elif approximation_type == "GeLU_ZO":
+            post_act_first_layer = self.gelu_zo_decomposition(post_first_layer, intermediate_hidden_states, intermediate_output)
+
+        post_second_layer = torch.einsum("bskl, dl->bskd", post_act_first_layer, self.output.dense.weight)
+        if include_biases:
+            post_second_layer = self.bias_decomposer(self.output.dense.bias, post_second_layer, bias_decomp_type=bias_decomp_type)
+
+        return post_second_layer
+
+    def ffn_decomposer_fast(self, attribution_vectors, intermediate_hidden_states, intermediate_output, include_biases=True, approximation_type="GeLU_LA", bias_decomp_type="absdot"):
+        if approximation_type == "ReLU":
+            theta = (intermediate_hidden_states > 0)
+        elif approximation_type == "GeLU_ZO":
+            theta = intermediate_output / (intermediate_hidden_states + 1e-12)
+        
+        scaled_W1 = torch.einsum("bsl,ld->bsld", theta, self.intermediate.dense.weight)
+        W_equiv = torch.einsum("bsld, zl->bszd", scaled_W1, self.output.dense.weight)
+
+        post_ffn_layer = torch.einsum("bszd,bskd->bskz", W_equiv, attribution_vectors)
+
+        if include_biases:
+            scaled_b1 = torch.einsum("bsl,l->bsl", theta, self.intermediate.dense.bias)
+            b_equiv = torch.einsum("bsl, dl->bsd", scaled_b1, self.output.dense.weight)
+            b_equiv = b_equiv + self.output.dense.bias
+
+            if bias_decomp_type == "absdot":
+                weights = torch.abs(torch.einsum("bskd,bsd->bsk", post_ffn_layer, b_equiv))
+            elif bias_decomp_type == "abssim":
+                weights = torch.abs(torch.nn.functional.cosine_similarity(post_ffn_layer, b_equiv))
+                weights = (torch.norm(post_ffn_layer, dim=-1) != 0) * weights
+            elif bias_decomp_type == "norm":
+                weights = torch.norm(post_ffn_layer, dim=-1)
+            elif bias_decomp_type == "equal":
+                weights = (torch.norm(post_ffn_layer, dim=-1) != 0) * 1.0
+
+            weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12)
+            weighted_bias = torch.einsum("bsd,bsk->bskd", b_equiv, weights)
+
+            post_ffn_layer = post_ffn_layer + weighted_bias
+
+        return post_ffn_layer
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attribution_vectors: Optional[torch.FloatTensor] = None,
+            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,
+            globenc_config: Optional[GlobencConfig] = None, # added by Fayyaz / Modarressi
+    ) -> 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,
+        # )
+        globenc_ready = globenc_config is not None
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attribution_vectors,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            globenc_ready=globenc_ready,
+        )  # changed by Goro Kobayashi
+        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
+
+        # layer_output = apply_chunking_to_forward(
+        #     self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+        # )
+
+        # Added by Fayyaz / Modarressi
+        # -------------------------------
+        bias_decomp_type = "biastoken" if globenc_config.include_bias_token else globenc_config.bias_decomp_type
+        intermediate_output, pre_act_hidden_states = self.intermediate(attention_output, globenc_ready=globenc_ready)
+        layer_output, pre_ln2_states = self.output(intermediate_output, attention_output, globenc_ready=globenc_ready)
+        if globenc_ready:
+            attention_probs, value_layer, decomposed_value_layer, pre_ln_states = outputs
+
+            headmixing_weight = self.attention.output.dense.weight.view(self.all_head_size, self.num_attention_heads,
+                                      self.attention_head_size)
+
+            if decomposed_value_layer is None or globenc_config.aggregation != "vector":
+                transformed_layer = torch.einsum('bhsv,dhv->bhsd', value_layer, headmixing_weight)  # V * W^o  (z=(qk)v)
+                # Make weighted vectors αf(x) from transformed vectors (transformed_layer)
+                # and attention weights (attentions):
+                # (batch, num_heads, seq_length, seq_length, all_head_size)
+                weighted_layer = torch.einsum('bhks,bhsd->bhksd', attention_probs,
+                                          transformed_layer)  # attention_probs(Q*K^t) * V * W^o
+                # Sum each weighted vectors αf(x) over all heads:
+                # (batch, seq_length, seq_length, all_head_size)
+                summed_weighted_layer = weighted_layer.sum(dim=1)  # sum over heads
+
+                # Make residual matrix (batch, seq_length, seq_length, all_head_size)
+                hidden_shape = hidden_states.size()  # (batch, seq_length, all_head_size)
+                device = hidden_states.device
+                residual = torch.einsum('sk,bsd->bskd', torch.eye(hidden_shape[1]).to(device),
+                                        hidden_states)  # diagonal representations (hidden states)
+
+                # Make matrix of summed weighted vector + residual vectors
+                residual_weighted_layer = summed_weighted_layer + residual
+                accumulated_bias = self.attention.output.dense.bias
+            else:
+                transformed_layer = torch.einsum('bhsqv,dhv->bhsqd', decomposed_value_layer, headmixing_weight)
+
+                weighted_layer = torch.einsum('bhks,bhsqd->bhkqd', attention_probs,
+                                          transformed_layer)  # attention_probs(Q*K^t) * V * W^o
+
+                summed_weighted_layer = weighted_layer.sum(dim=1)  # sum over heads
+
+                residual_weighted_layer = summed_weighted_layer + attribution_vectors
+                accumulated_bias = torch.matmul(self.attention.output.dense.weight, self.attention.self.value.bias) + self.attention.output.dense.bias
+
+            if globenc_config.include_biases:
+                residual_weighted_layer = self.bias_decomposer(accumulated_bias, residual_weighted_layer, bias_decomp_type)
+
+            if globenc_config.include_LN1:
+                post_ln_layer = self.ln_decomposer(
+                    attribution_vectors=residual_weighted_layer,
+                    pre_ln_states=pre_ln_states,
+                    gamma=self.attention.output.LayerNorm.weight.data,
+                    beta=self.attention.output.LayerNorm.bias.data,
+                    eps=self.attention.output.LayerNorm.eps,
+                    include_biases=globenc_config.include_biases,
+                    bias_decomp_type=bias_decomp_type
+                )
+            else:
+                post_ln_layer = residual_weighted_layer
+
+            if globenc_config.include_FFN:
+                post_ffn_layer = self.ffn_decomposer_fast if globenc_config.FFN_fast_mode else self.ffn_decomposer(
+                    attribution_vectors=post_ln_layer,
+                    intermediate_hidden_states=pre_act_hidden_states,
+                    intermediate_output=intermediate_output,
+                    approximation_type=globenc_config.FFN_approx_type,
+                    include_biases=globenc_config.include_biases,
+                    bias_decomp_type=bias_decomp_type
+                )
+                pre_ln2_layer = post_ln_layer + post_ffn_layer
+            else:
+                pre_ln2_layer = post_ln_layer
+                post_ffn_layer = None
+
+            if globenc_config.include_LN2:
+                post_ln2_layer = self.ln_decomposer(
+                    attribution_vectors=pre_ln2_layer,
+                    pre_ln_states=pre_ln2_states,
+                    gamma=self.output.LayerNorm.weight.data,
+                    beta=self.output.LayerNorm.bias.data,
+                    eps=self.output.LayerNorm.eps,
+                    include_biases=globenc_config.include_biases,
+                    bias_decomp_type=bias_decomp_type
+                )
+            else:
+                post_ln2_layer = pre_ln2_layer
+
+            new_outputs = GlobencOutput(
+                attention=output_builder(summed_weighted_layer, globenc_config.output_attention),
+                res1=output_builder(residual_weighted_layer, globenc_config.output_res1),
+                LN1=output_builder(post_ln_layer, globenc_config.output_res2),
+                FFN=output_builder(post_ffn_layer, globenc_config.output_FFN),
+                res2=output_builder(pre_ln2_layer, globenc_config.output_res2),
+                encoder=output_builder(post_ln2_layer, "both")
+            )
+            return (layer_output,) + (new_outputs,)
+        # -------------------------------
+        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 BertEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    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,
+            globenc_config: Optional[GlobencConfig] = None,  # added by Fayyaz / Modarressi
+    ) -> 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
+
+        next_decoder_cache = () if use_cache else None
+
+        aggregated_encoder_norms = None # added by Fayyaz / Modarressi
+        aggregated_encoder_vectors = None # added by Fayyaz / Modarressi
+
+        # -- added by Fayyaz / Modarressi
+        if globenc_config and globenc_config.output_all_layers:
+            all_globenc_outputs = GlobencOutput(
+                attention=() if globenc_config.output_attention else None,
+                res1=() if globenc_config.output_res1 else None,
+                LN1=() if globenc_config.output_LN1 else None,
+                FFN=() if globenc_config.output_LN1 else None,
+                res2=() if globenc_config.output_res2 else None,
+                encoder=() if globenc_config.output_encoder else None,
+                aggregated=() if globenc_config.output_aggregated and globenc_config.aggregation else None,
+            )
+        else:
+            all_globenc_outputs = None
+        # -- added by Fayyaz / Modarressi
+
+        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, 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,
+                    aggregated_encoder_vectors,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    globenc_config # added by Fayyaz / Modarressi
+                )
+
+            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],)
+            
+            # added by Fayyaz / Modarressi
+            if globenc_config:
+                globenc_output = layer_outputs[1]
+                if globenc_config.aggregation == "rollout":
+                    if globenc_config.include_classifier_w_pooler:
+                        raise Exception("Classifier and pooler could be included in vector aggregation mode")
+
+                    encoder_norms = globenc_output.encoder[0][0]
+
+                    if aggregated_encoder_norms is None:
+                        aggregated_encoder_norms = encoder_norms * torch.exp(attention_mask).view((-1, attention_mask.shape[-1], 1))
+                    else:
+                        aggregated_encoder_norms = torch.einsum("ijk,ikm->ijm", encoder_norms, aggregated_encoder_norms)
+                        
+                    if globenc_config.output_aggregated == "norm":
+                        globenc_output.aggregated = (aggregated_encoder_norms,)
+                    elif globenc_config.output_aggregated is not None:
+                        raise Exception("Rollout aggregated values are only available in norms. Set output_aggregated to 'norm'.")
+
+
+                elif globenc_config.aggregation == "vector":
+                    aggregated_encoder_vectors = globenc_output.encoder[0][1]
+
+                    if globenc_config.include_classifier_w_pooler:
+                        globenc_output.aggregated = (aggregated_encoder_vectors,)
+                    else:
+                        globenc_output.aggregated = output_builder(aggregated_encoder_vectors, globenc_config.output_aggregated)
+
+                globenc_output.encoder = output_builder(globenc_output.encoder[0][1], globenc_config.output_encoder)
+
+                if globenc_config.output_all_layers:
+                    all_globenc_outputs.attention = all_globenc_outputs.attention + globenc_output.attention if globenc_config.output_attention else None
+                    all_globenc_outputs.res1 = all_globenc_outputs.res1 + globenc_output.res1 if globenc_config.output_res1 else None
+                    all_globenc_outputs.LN1 = all_globenc_outputs.LN1 + globenc_output.LN1 if globenc_config.output_LN1 else None
+                    all_globenc_outputs.FFN = all_globenc_outputs.FFN + globenc_output.FFN if globenc_config.output_FFN else None
+                    all_globenc_outputs.res2 = all_globenc_outputs.res2 + globenc_output.res2 if globenc_config.output_res2 else None
+                    all_globenc_outputs.encoder = all_globenc_outputs.encoder + globenc_output.encoder if globenc_config.output_encoder else None
+
+                    if globenc_config.include_classifier_w_pooler and globenc_config.aggregation == "vector":
+                        all_globenc_outputs.aggregated = all_globenc_outputs.aggregated + output_builder(aggregated_encoder_vectors, globenc_config.output_aggregated) if globenc_config.output_aggregated else None
+                    else:
+                        all_globenc_outputs.aggregated = all_globenc_outputs.aggregated + globenc_output.aggregated if globenc_config.output_aggregated else None
+
+        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,
+                    globenc_output if globenc_config else None,
+                    all_globenc_outputs
+                ]
+                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: torch.Tensor, globenc_ready=False) -> torch.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pre_pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pre_pooled_output)
+        if globenc_ready:
+            return pooled_output, pre_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: 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 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: torch.Tensor) -> torch.Tensor:
+        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
+            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, 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.
+        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 [`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 [`~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
+
+        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)
+
+    def bias_decomposer(self, bias, attribution_vectors, bias_decomp_type="absdot"):
+        # Decomposes the input bias based on similarity to the attribution vectors
+        # Args:
+        #   bias: a bias vector (all_head_size)
+        #   attribution_vectors: the attribution vectors from token j to i (b, i, j, all_head_size) :: (batch, seq_length, seq_length, all_head_size)
+
+        if bias_decomp_type == "absdot":
+            weights = torch.abs(torch.einsum("bkd,d->bk", attribution_vectors, bias))
+        elif bias_decomp_type == "abssim":
+            weights = torch.abs(torch.nn.functional.cosine_similarity(attribution_vectors, bias, dim=-1))
+            weights = (torch.norm(attribution_vectors, dim=-1) != 0) * weights
+        elif bias_decomp_type == "norm":
+            weights = torch.norm(attribution_vectors, dim=-1)
+        elif bias_decomp_type == "equal":
+            weights = (torch.norm(attribution_vectors, dim=-1) != 0) * 1.0
+        elif bias_decomp_type == "cls":
+            weights = torch.zeros(attribution_vectors.shape[:-1], device=attribution_vectors.device)
+            weights[:,0] = 1.0
+        elif bias_decomp_type == "dot":
+            weights = torch.einsum("bkd,d->bk", attribution_vectors, bias)
+        elif bias_decomp_type == "biastoken":
+            attribution_vectors[:,-1] = attribution_vectors[:,-1] + bias
+            return attribution_vectors
+        
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12)
+
+        weighted_bias = torch.matmul(weights.unsqueeze(dim=-1), bias.unsqueeze(dim=0))
+        return attribution_vectors + weighted_bias
+
+    def tanh_linear_approximation(self, pre_act_pooled, post_act_pooled):
+        def tanh_deriv(x):
+            return 1 - torch.tanh(x)**2.0
+        
+        m = tanh_deriv(pre_act_pooled)
+        b = post_act_pooled - m * pre_act_pooled
+        return m, b
+
+    def tanh_la_decomposition(self, attribution_vectors, pre_act_pooled, post_act_pooled, bias_decomp_type):
+        m, b = self.tanh_linear_approximation(pre_act_pooled, post_act_pooled)
+        mx = attribution_vectors * m.unsqueeze(dim=-2)
+
+        if bias_decomp_type == "absdot":
+            weights = torch.abs(torch.einsum("bkd,bd->bk", mx, b))
+        elif bias_decomp_type == "abssim":
+            weights = torch.abs(torch.nn.functional.cosine_similarity(mx, b, dim=-1))
+            weights = (torch.norm(mx, dim=-1) != 0) * weights
+        elif bias_decomp_type == "norm":
+            weights = torch.norm(mx, dim=-1)
+        elif bias_decomp_type == "equal":
+            weights = (torch.norm(mx, dim=-1) != 0) * 1.0
+        elif bias_decomp_type == "cls":
+            weights = torch.zeros(mx.shape[:-1], device=mx.device)
+            weights[:,0] = 1.0
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12)
+        weighted_bias = torch.einsum("bd,bk->bkd", b, weights)
+        return mx + weighted_bias
+
+    def tanh_zo_decomposition(self, attribution_vectors, pre_act_pooled, post_act_pooled):
+        m = post_act_pooled / (pre_act_pooled + 1e-12)
+        mx = attribution_vectors * m.unsqueeze(dim=-2)
+        return mx
+    
+    def ffn_decomposer(self, attribution_vectors, pre_act_pooled, post_act_pooled, include_biases=True, bias_decomp_type="absdot", tanh_approx_type="LA"):
+        post_pool = torch.einsum("ld,bsd->bsl", self.pooler.dense.weight, attribution_vectors)
+        if include_biases:
+            post_pool = self.bias_decomposer(self.pooler.dense.bias, post_pool, bias_decomp_type=bias_decomp_type)
+
+        if tanh_approx_type == "LA":
+            post_act_pool = self.tanh_la_decomposition(post_pool, pre_act_pooled, post_act_pooled, bias_decomp_type=bias_decomp_type)
+        else:
+            post_act_pool = self.tanh_zo_decomposition(post_pool, pre_act_pooled, post_act_pooled)
+
+        return post_act_pool
+
+    @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: 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,
+            globenc_config: Optional[GlobencConfig] = None,  # added by Fayyaz / Modarressi
+    ) -> 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:
+            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, 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,
+            globenc_config=globenc_config, # added by Fayyaz / Modarressi
+        )
+        sequence_output = encoder_outputs[0]
+        globenc_ready = globenc_config is not None
+        pooled_output = self.pooler(sequence_output, globenc_ready=globenc_ready) if self.pooler is not None else None
+
+        if globenc_ready:
+            pre_act_pooled = pooled_output[1]
+            pooled_output = pooled_output[0]
+
+            if globenc_config.include_classifier_w_pooler:
+                globenc_idx = -2 if globenc_config.output_all_layers else -1
+                aggregated_attribution_vectors = encoder_outputs[globenc_idx].aggregated[0]
+
+                encoder_outputs[globenc_idx].aggregated = output_builder(aggregated_attribution_vectors, globenc_config.output_aggregated)
+
+                pooler_decomposed = self.ffn_decomposer(
+                    attribution_vectors=aggregated_attribution_vectors[:, 0], 
+                    pre_act_pooled=pre_act_pooled, 
+                    post_act_pooled=pooled_output, 
+                    include_biases=globenc_config.include_biases,
+                    bias_decomp_type="biastoken" if globenc_config.include_bias_token else globenc_config.bias_decomp_type,
+                    tanh_approx_type=globenc_config.tanh_approx_type
+                )
+
+                encoder_outputs[globenc_idx].pooler = pooler_decomposed
+
+        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: 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], BertForPreTrainingOutput]:
+        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
+        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,
+            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: 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`).
+
+        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: 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(
+    """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: 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:
+
+        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()
+
+    def bias_decomposer(self, bias, attribution_vectors, bias_decomp_type="absdot"):
+        # Decomposes the input bias based on similarity to the attribution vectors
+        # Args:
+        #   bias: a bias vector (all_head_size)
+        #   attribution_vectors: the attribution vectors from token j to i (b, i, j, all_head_size) :: (batch, seq_length, seq_length, all_head_size) 
+        if bias_decomp_type == "absdot":
+            weights = torch.abs(torch.einsum("bkd,d->bk", attribution_vectors, bias))
+        elif bias_decomp_type == "abssim":
+            weights = torch.abs(torch.nn.functional.cosine_similarity(attribution_vectors, bias, dim=-1))
+            weights = (torch.norm(attribution_vectors, dim=-1) != 0) * weights
+        elif bias_decomp_type == "norm":
+            weights = torch.norm(attribution_vectors, dim=-1)
+        elif bias_decomp_type == "equal":
+            weights = (torch.norm(attribution_vectors, dim=-1) != 0) * 1.0
+        elif bias_decomp_type == "cls":
+            weights = torch.zeros(attribution_vectors.shape[:-1], device=attribution_vectors.device)
+            weights[:,0] = 1.0
+        elif bias_decomp_type == "dot":
+            weights = torch.einsum("bkd,d->bk", attribution_vectors, bias)
+        elif bias_decomp_type == "biastoken":
+            attribution_vectors[:,-1] = attribution_vectors[:,-1] + bias
+            return attribution_vectors
+
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12)
+        weighted_bias = torch.matmul(weights.unsqueeze(dim=-1), bias.unsqueeze(dim=0))
+        return attribution_vectors + weighted_bias
+
+    def biastoken_decomposer(self, biastoken, attribution_vectors, bias_decomp_type="absdot"):
+        # Decomposes the input bias based on similarity to the attribution vectors
+        # Args:
+        #   bias: a bias vector (all_head_size)
+        #   attribution_vectors: the attribution vectors from token j to i (b, i, j, all_head_size) :: (batch, seq_length, seq_length, all_head_size) 
+        if bias_decomp_type == "absdot":
+            weights = torch.abs(torch.einsum("bkd,bd->bk", attribution_vectors, biastoken))
+        elif bias_decomp_type == "abssim":
+            weights = torch.abs(torch.nn.functional.cosine_similarity(attribution_vectors, biastoken, dim=-1))
+            weights = (torch.norm(attribution_vectors, dim=-1) != 0) * weights
+        elif bias_decomp_type == "norm":
+            weights = torch.norm(attribution_vectors, dim=-1)
+        elif bias_decomp_type == "equal":
+            weights = (torch.norm(attribution_vectors, dim=-1) != 0) * 1.0
+        elif bias_decomp_type == "cls":
+            weights = torch.zeros(attribution_vectors.shape[:-1], device=attribution_vectors.device)
+            weights[:,0] = 1.0
+        elif bias_decomp_type == "dot":
+            weights = torch.einsum("bkd,d->bk", attribution_vectors, biastoken)
+
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12)
+        weighted_bias = torch.matmul(weights.unsqueeze(dim=-1), biastoken.unsqueeze(dim=1))
+        return attribution_vectors + weighted_bias
+
+    def ffn_decomposer(self, attribution_vectors, include_biases=True, bias_decomp_type="absdot"):
+        post_classifier = torch.einsum("ld,bkd->bkl", self.classifier.weight, attribution_vectors)
+        if include_biases:
+            post_classifier = self.bias_decomposer(self.classifier.bias, post_classifier, bias_decomp_type=bias_decomp_type)
+
+        return post_classifier
+
+    @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: 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,
+            globenc_config: Optional[GlobencConfig] = None,  # added by Fayyaz / Modarressi
+    ) -> 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,
+            globenc_config=globenc_config
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        if globenc_config and globenc_config.include_classifier_w_pooler:
+            globenc_idx = -2 if globenc_config.output_all_layers else -1
+            aggregated_attribution_vectors = outputs[globenc_idx].pooler
+
+            outputs[globenc_idx].pooler = output_builder(aggregated_attribution_vectors, globenc_config.output_pooler)
+
+            classifier_decomposed = self.ffn_decomposer(
+                attribution_vectors=aggregated_attribution_vectors, 
+                include_biases=globenc_config.include_biases,
+                bias_decomp_type="biastoken" if globenc_config.include_bias_token else globenc_config.bias_decomp_type
+            )
+            
+            if globenc_config.include_bias_token and globenc_config.bias_decomp_type is not None:
+                bias_token = classifier_decomposed[:,-1,:].detach().clone()
+                classifier_decomposed = classifier_decomposed[:,:-1,:]
+                classifier_decomposed = self.biastoken_decomposer(
+                    bias_token, 
+                    classifier_decomposed, 
+                    bias_decomp_type=globenc_config.bias_decomp_type
+                )
+                
+
+            outputs[globenc_idx].classifier = classifier_decomposed if globenc_config.output_classifier else None
+
+        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  # (loss), logits, (hidden_states), (attentions)
+
+        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: 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(
+    """
+    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.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: 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(
+    """
+    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: 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,
+        )