<reponame>puririshi98/benchmark
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research 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.

import inspect
import logging
import math
import warnings
import os
from typing import Callable, Dict, Iterable, List, Optional, Tuple

import torch
from torch import Tensor, device, dtype, nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F

from activations import get_activation, gelu, gelu_new, swish
from file_utils import add_start_docstrings, add_start_docstrings_to_callable
from configuration_utils import PretrainedConfig, BertConfig
from file_utils import (
	DUMMY_INPUTS,
	TF2_WEIGHTS_NAME,
	TF_WEIGHTS_NAME,
	WEIGHTS_NAME,
	cached_path,
	hf_bucket_url,
	is_remote_url,
)

logger = logging.getLogger(__name__)

try:
	from torch.nn import Identity
except ImportError:
	# Older PyTorch compatibility
	class Identity(nn.Module):
		r"""A placeholder identity operator that is argument-insensitive.
		"""

		def __init__(self, *args, **kwargs):
			super().__init__()

		def forward(self, input):
			return input


def find_pruneable_heads_and_indices(
		heads: List, n_heads: int, head_size: int, already_pruned_heads: set
) -> Tuple[set, "torch.LongTensor"]:
	mask = torch.ones(n_heads, head_size)
	heads = set(heads) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		head = head - sum(1 if h < head else 0 for h in already_pruned_heads)
		mask[head] = 0
	mask = mask.view(-1).contiguous().eq(1)
	index: torch.LongTensor = torch.arange(len(mask))[mask].long()
	return heads, index


class ModuleUtilsMixin:
	"""
	A few utilities for torch.nn.Modules, to be used as a mixin.
	"""

	def num_parameters(self, only_trainable: bool = False) -> int:
		"""
		Get number of (optionally, trainable) parameters in the module.
		"""
		params = filter(lambda x: x.requires_grad, self.parameters()) if only_trainable else self.parameters()
		return sum(p.numel() for p in params)

	@staticmethod
	def _hook_rss_memory_pre_forward(module, *args, **kwargs):
		try:
			import psutil
		except (ImportError):
			raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")

		process = psutil.Process(os.getpid())
		mem = process.memory_info()
		module.mem_rss_pre_forward = mem.rss
		return None

	@staticmethod
	def _hook_rss_memory_post_forward(module, *args, **kwargs):
		try:
			import psutil
		except (ImportError):
			raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")

		process = psutil.Process(os.getpid())
		mem = process.memory_info()
		module.mem_rss_post_forward = mem.rss
		mem_rss_diff = module.mem_rss_post_forward - module.mem_rss_pre_forward
		module.mem_rss_diff = mem_rss_diff + (module.mem_rss_diff if hasattr(module, "mem_rss_diff") else 0)
		return None

	def add_memory_hooks(self):
		""" Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.
			Increase in memory consumption is stored in a `mem_rss_diff` attribute for each module and can be reset to zero with `model.reset_memory_hooks_state()`
		"""
		for module in self.modules():
			module.register_forward_pre_hook(self._hook_rss_memory_pre_forward)
			module.register_forward_hook(self._hook_rss_memory_post_forward)
		self.reset_memory_hooks_state()

	def reset_memory_hooks_state(self):
		for module in self.modules():
			module.mem_rss_diff = 0
			module.mem_rss_post_forward = 0
			module.mem_rss_pre_forward = 0

	@property
	def device(self) -> device:
		"""
		Get torch.device from module, assuming that the whole module has one device.
		"""
		try:
			return next(self.parameters()).device
		except StopIteration:
			# For nn.DataParallel compatibility in PyTorch 1.5

			def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
				tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
				return tuples

			gen = self._named_members(get_members_fn=find_tensor_attributes)
			first_tuple = next(gen)
			return first_tuple[1].device

	@property
	def dtype(self) -> dtype:
		"""
		Get torch.dtype from module, assuming that the whole module has one dtype.
		"""
		try:
			return next(self.parameters()).dtype
		except StopIteration:
			# For nn.DataParallel compatibility in PyTorch 1.5

			def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
				tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
				return tuples

			gen = self._named_members(get_members_fn=find_tensor_attributes)
			first_tuple = next(gen)
			return first_tuple[1].dtype

	def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor:
		"""type: torch.Tensor -> torch.Tensor"""
		if encoder_attention_mask.dim() == 3:
			encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.dim() == 2:
			encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
		# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
		# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
		# /transformer/transformer_layers.py#L270
		# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
		# encoder_extended_attention_mask.transpose(-1, -2))
		encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility

		if self.dtype == torch.float16:
			encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
		elif self.dtype == torch.float32:
			encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
		else:
			raise ValueError(
				"{} not recognized. `dtype` should be set to either `torch.float32` or `torch.float16`".format(
					self.dtype
				)
			)

		return encoder_extended_attention_mask

	def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple, device: device) -> Tensor:
		"""Makes broadcastable attention mask and causal mask so that future and maked tokens are ignored.

		Arguments:
			attention_mask: torch.Tensor with 1 indicating tokens to ATTEND to
			input_shape: tuple, shape of input_ids
			device: torch.Device, usually self.device

		Returns:
			torch.Tensor with dtype of attention_mask.dtype
		"""
		# 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.
		if attention_mask.dim() == 3:
			extended_attention_mask = attention_mask[:, None, :, :]
		elif attention_mask.dim() == 2:
			# Provided a padding mask of dimensions [batch_size, seq_length]
			# - if the model is a decoder, apply a causal mask in addition to the padding mask
			# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
			if self.config.is_decoder:
				batch_size, seq_length = input_shape
				seq_ids = torch.arange(seq_length, device=device)
				causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
				# causal and attention masks must have same type with pytorch version < 1.3
				causal_mask = causal_mask.to(attention_mask.dtype)
				extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
			else:
				extended_attention_mask = attention_mask[:, None, None, :]
		else:
			raise ValueError(
				"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
					input_shape, attention_mask.shape
				)
			)

		# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
		# masked positions, this operation will create a tensor which is 0.0 for
		# positions we want to attend and -10000.0 for masked positions.
		# Since we are adding it to the raw scores before the softmax, this is
		# effectively the same as removing these entirely.
		extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
		extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
		return extended_attention_mask

	def get_head_mask(self, head_mask: Tensor, num_hidden_layers: int, is_attention_chunked: bool = False) -> Tensor:
		"""
		# 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
		Arguments:
			head_mask: torch.Tensor or None: has shape [num_heads] or [num_hidden_layers x num_heads]
			num_hidden_layers: int
		Returns:
			 Tensor of shape shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
			 or list with [None] for each layer
		"""
		if head_mask is not None:
			head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
			if is_attention_chunked is True:
				head_mask = head_mask.unsqueeze(-1)
		else:
			head_mask = [None] * num_hidden_layers

		return head_mask

	def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
		"""-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
		if head_mask.dim() == 1:
			head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
			head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
		elif head_mask.dim() == 2:
			head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
		assert head_mask.dim() == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
		head_mask = head_mask.to(dtype=self.dtype)  # switch to fload if need + fp16 compatibility
		return head_mask


class PreTrainedModel(nn.Module, ModuleUtilsMixin):
	r""" Base class for all models.

		:class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
		as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.

		Class attributes (overridden by derived classes):
			- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
			- ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:

				- ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`,
				- ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`,
				- ``path``: a path (string) to the TensorFlow checkpoint.

			- ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
	"""
	config_class = None
	base_model_prefix = ""

	@property
	def dummy_inputs(self):
		""" Dummy inputs to do a forward pass in the network.

		Returns:
			torch.Tensor with dummy inputs
		"""
		return {"input_ids": torch.tensor(DUMMY_INPUTS)}

	def __init__(self, config, *inputs, **kwargs):
		super().__init__()
		if not isinstance(config, PretrainedConfig):
			raise ValueError(
				"Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
				"To create a model from a pretrained model use "
				"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
					self.__class__.__name__, self.__class__.__name__
				)
			)
		# Save config in model
		self.config = config

	@property
	def base_model(self):
		return getattr(self, self.base_model_prefix, self)

	def get_input_embeddings(self):
		"""
		Returns the model's input embeddings.

		Returns:
			:obj:`nn.Module`:
				A torch module mapping vocabulary to hidden states.
		"""
		base_model = getattr(self, self.base_model_prefix, self)
		if base_model is not self:
			return base_model.get_input_embeddings()
		else:
			raise NotImplementedError

	def set_input_embeddings(self, value: nn.Module):
		"""
		Set model's input embeddings

		Args:
			value (:obj:`nn.Module`):
				A module mapping vocabulary to hidden states.
		"""
		base_model = getattr(self, self.base_model_prefix, self)
		if base_model is not self:
			base_model.set_input_embeddings(value)
		else:
			raise NotImplementedError

	def get_output_embeddings(self):
		"""
		Returns the model's output embeddings.

		Returns:
			:obj:`nn.Module`:
				A torch module mapping hidden states to vocabulary.
		"""
		return None  # Overwrite for models with output embeddings

	def tie_weights(self):
		"""
		Tie the weights between the input embeddings and the output embeddings.
		If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning
		the weights instead.
		"""
		output_embeddings = self.get_output_embeddings()
		if output_embeddings is not None:
			self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())

	def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
		""" Tie or clone module weights depending of whether we are using TorchScript or not
		"""
		if self.config.torchscript:
			output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone())
		else:
			output_embeddings.weight = input_embeddings.weight

		if getattr(output_embeddings, "bias", None) is not None:
			output_embeddings.bias.data = torch.nn.functional.pad(
				output_embeddings.bias.data,
				(0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],),
				"constant",
				0,
			)
		if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
			output_embeddings.out_features = input_embeddings.num_embeddings

	def resize_token_embeddings(self, new_num_tokens: Optional[int] = None):
		""" Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
		Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.

		Arguments:

			new_num_tokens: (`optional`) int:
				New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end.
				If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.

		Return: ``torch.nn.Embeddings``
			Pointer to the input tokens Embeddings Module of the model
		"""
		base_model = getattr(self, self.base_model_prefix, self)  # get the base model if needed
		model_embeds = base_model._resize_token_embeddings(new_num_tokens)
		if new_num_tokens is None:
			return model_embeds

		# Update base model and current model config
		self.config.vocab_size = new_num_tokens
		base_model.vocab_size = new_num_tokens

		# Tie weights again if needed
		self.tie_weights()

		return model_embeds

	def _resize_token_embeddings(self, new_num_tokens):
		old_embeddings = self.get_input_embeddings()
		new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
		self.set_input_embeddings(new_embeddings)
		return self.get_input_embeddings()

	def _get_resized_embeddings(
			self, old_embeddings: torch.nn.Embedding, new_num_tokens: Optional[int] = None
	) -> torch.nn.Embedding:
		""" Build a resized Embedding Module from a provided token Embedding Module.
			Increasing the size will add newly initialized vectors at the end
			Reducing the size will remove vectors from the end

		Args:
			old_embeddings: ``torch.nn.Embedding``
				Old embeddings to be resized.
			new_num_tokens: (`optional`) int
				New number of tokens in the embedding matrix.
				Increasing the size will add newly initialized vectors at the end
				Reducing the size will remove vectors from the end
				If not provided or None: return the provided token Embedding Module.
		Return: ``torch.nn.Embedding``
			Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
		"""
		if new_num_tokens is None:
			return old_embeddings

		old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
		if old_num_tokens == new_num_tokens:
			return old_embeddings

		# Build new embeddings
		new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
		new_embeddings.to(old_embeddings.weight.device)

		# initialize all new embeddings (in particular added tokens)
		self._init_weights(new_embeddings)

		# Copy token embeddings from the previous weights
		num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
		new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]

		return new_embeddings

	def init_weights(self):
		""" Initialize and prunes weights if needed. """
		# Initialize weights
		self.apply(self._init_weights)

		# Prune heads if needed
		if self.config.pruned_heads:
			self.prune_heads(self.config.pruned_heads)

		# Tie weights if needed
		self.tie_weights()

	def prune_heads(self, heads_to_prune: Dict):
		""" Prunes heads of the base model.

			Arguments:

				heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).
				E.g. {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
		"""
		# save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads
		for layer, heads in heads_to_prune.items():
			union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)
			self.config.pruned_heads[layer] = list(union_heads)  # Unfortunately we have to store it as list for JSON

		self.base_model._prune_heads(heads_to_prune)

	def save_pretrained(self, save_directory):
		""" Save a model and its configuration file to a directory, so that it
			can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.

			Arguments:
				save_directory: directory to which to save.
		"""
		if os.path.isfile(save_directory):
			logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
			return
		os.makedirs(save_directory, exist_ok=True)

		# Only save the model itself if we are using distributed training
		model_to_save = self.module if hasattr(self, "module") else self

		# Attach architecture to the config
		model_to_save.config.architectures = [model_to_save.__class__.__name__]

		# If we save using the predefined names, we can load using `from_pretrained`
		output_model_file = os.path.join(save_directory, WEIGHTS_NAME)

		if getattr(self.config, "xla_device", False):
			import torch_xla.core.xla_model as xm

			if xm.is_master_ordinal():
				# Save configuration file
				model_to_save.config.save_pretrained(save_directory)
			# xm.save takes care of saving only from master
			xm.save(model_to_save.state_dict(), output_model_file)
		else:
			model_to_save.config.save_pretrained(save_directory)
			torch.save(model_to_save.state_dict(), output_model_file)

		logger.info("Model weights saved in {}".format(output_model_file))

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
		r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.

		The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated)
		To train the model, you should first set it back in training mode with ``model.train()``

		The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.
		It is up to you to train those weights with a downstream fine-tuning task.

		The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.

		Parameters:
			pretrained_model_name_or_path: either:
			  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
			  - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
			  - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
			  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
			  - None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``)

			model_args: (`optional`) Sequence of positional arguments:
				All remaning positional arguments will be passed to the underlying model's ``__init__`` method

			config: (`optional`) one of:
				- an instance of a class derived from :class:`~transformers.PretrainedConfig`, or
				- a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained()`
				Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
					- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
					- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
					- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.

			state_dict: (`optional`) dict:
				an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
				This option can be used if you want to create a model from a pretrained configuration but load your own weights.
				In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.

			cache_dir: (`optional`) string:
				Path to a directory in which a downloaded pre-trained model
				configuration should be cached if the standard cache should not be used.

			force_download: (`optional`) boolean, default False:
				Force to (re-)download the model weights and configuration files and override the cached versions if they exists.

			resume_download: (`optional`) boolean, default False:
				Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.

			proxies: (`optional`) dict, default None:
				A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
				The proxies are used on each request.

			output_loading_info: (`optional`) boolean:
				Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.

			kwargs: (`optional`) Remaining dictionary of keyword arguments:
				Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:

				- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
				- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.

		Examples::

			# For example purposes. Not runnable.
			model = BertModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
			model = BertModel.from_pretrained('./test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
			model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
			assert model.config.output_attention == True
			# Loading from a TF checkpoint file instead of a PyTorch model (slower)
			config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
			model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)

		"""
		config = kwargs.pop("config", None)
		state_dict = kwargs.pop("state_dict", None)
		cache_dir = kwargs.pop("cache_dir", None)
		from_tf = kwargs.pop("from_tf", False)
		force_download = kwargs.pop("force_download", False)
		resume_download = kwargs.pop("resume_download", False)
		proxies = kwargs.pop("proxies", None)
		output_loading_info = kwargs.pop("output_loading_info", False)
		local_files_only = kwargs.pop("local_files_only", False)
		use_cdn = kwargs.pop("use_cdn", True)

		# Load config if we don't provide a configuration
		if not isinstance(config, PretrainedConfig):
			config_path = config if config is not None else pretrained_model_name_or_path
			config, model_kwargs = cls.config_class.from_pretrained(
				config_path,
				*model_args,
				cache_dir=cache_dir,
				return_unused_kwargs=True,
				force_download=force_download,
				resume_download=resume_download,
				proxies=proxies,
				local_files_only=local_files_only,
				**kwargs,
			)
		else:
			model_kwargs = kwargs

		# Load model
		if pretrained_model_name_or_path is not None:
			if os.path.isdir(pretrained_model_name_or_path):
				if from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")):
					# Load from a TF 1.0 checkpoint
					archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
				elif from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
					# Load from a TF 2.0 checkpoint
					archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
				elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
					# Load from a PyTorch checkpoint
					archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
				else:
					raise EnvironmentError(
						"Error no file named {} found in directory {} or `from_tf` set to False".format(
							[WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"],
							pretrained_model_name_or_path,
						)
					)
			elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
				archive_file = pretrained_model_name_or_path
			elif os.path.isfile(pretrained_model_name_or_path + ".index"):
				assert (
					from_tf
				), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format(
					pretrained_model_name_or_path + ".index"
				)
				archive_file = pretrained_model_name_or_path + ".index"
			else:
				archive_file = hf_bucket_url(
					pretrained_model_name_or_path,
					filename=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME),
					use_cdn=use_cdn,
				)

			try:
				# Load from URL or cache if already cached
				resolved_archive_file = cached_path(
					archive_file,
					cache_dir=cache_dir,
					force_download=force_download,
					proxies=proxies,
					resume_download=resume_download,
					local_files_only=local_files_only,
				)
				if resolved_archive_file is None:
					raise EnvironmentError
			except EnvironmentError:
				msg = (
					f"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
					f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
					f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a file named one of {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME}.\n\n"
				)
				raise EnvironmentError(msg)

			if resolved_archive_file == archive_file:
				logger.info("loading weights file {}".format(archive_file))
			else:
				logger.info("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file))
		else:
			resolved_archive_file = None

		# Instantiate model.
		model = cls(config, *model_args, **model_kwargs)

		if state_dict is None and not from_tf:
			try:
				state_dict = torch.load(resolved_archive_file, map_location="cpu")
			except Exception:
				raise OSError(
					"Unable to load weights from pytorch checkpoint file. "
					"If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. "
				)

		missing_keys = []
		unexpected_keys = []
		error_msgs = []

		if from_tf:
			if resolved_archive_file.endswith(".index"):
				# Load from a TensorFlow 1.X checkpoint - provided by original authors
				model = cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
			else:
				# Load from our TensorFlow 2.0 checkpoints
				try:
					from transformers import load_tf2_checkpoint_in_pytorch_model

					model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)
				except ImportError:
					logger.error(
						"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
						"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
					)
					raise
		else:
			# Convert old format to new format if needed from a PyTorch state_dict
			old_keys = []
			new_keys = []
			for key in state_dict.keys():
				new_key = None
				if "gamma" in key:
					new_key = key.replace("gamma", "weight")
				if "beta" in key:
					new_key = key.replace("beta", "bias")
				if new_key:
					old_keys.append(key)
					new_keys.append(new_key)
			for old_key, new_key in zip(old_keys, new_keys):
				state_dict[new_key] = state_dict.pop(old_key)

			# copy state_dict so _load_from_state_dict can modify it
			metadata = getattr(state_dict, "_metadata", None)
			state_dict = state_dict.copy()
			if metadata is not None:
				state_dict._metadata = metadata

			# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
			# so we need to apply the function recursively.
			def load(module: nn.Module, prefix=""):
				local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
				module._load_from_state_dict(
					state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs,
				)
				for name, child in module._modules.items():
					if child is not None:
						load(child, prefix + name + ".")

			# Make sure we are able to load base models as well as derived models (with heads)
			start_prefix = ""
			model_to_load = model
			has_prefix_module = any(s.startswith(cls.base_model_prefix) for s in state_dict.keys())
			if not hasattr(model, cls.base_model_prefix) and has_prefix_module:
				start_prefix = cls.base_model_prefix + "."
			if hasattr(model, cls.base_model_prefix) and not has_prefix_module:
				model_to_load = getattr(model, cls.base_model_prefix)

			load(model_to_load, prefix=start_prefix)

			if model.__class__.__name__ != model_to_load.__class__.__name__:
				base_model_state_dict = model_to_load.state_dict().keys()
				head_model_state_dict_without_base_prefix = [
					key.split(cls.base_model_prefix + ".")[-1] for key in model.state_dict().keys()
				]

				missing_keys.extend(head_model_state_dict_without_base_prefix - base_model_state_dict)

			if len(unexpected_keys) > 0:
				logger.warning(
					f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when "
					f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
					f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task "
					f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n"
					f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect "
					f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
				)
			else:
				logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
			if len(missing_keys) > 0:
				logger.warning(
					f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
					f"and are newly initialized: {missing_keys}\n"
					f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
				)
			else:
				logger.info(
					f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
					f"If your task is similar to the task the model of the ckeckpoint was trained on, "
					f"you can already use {model.__class__.__name__} for predictions without further training."
				)
			if len(error_msgs) > 0:
				raise RuntimeError(
					"Error(s) in loading state_dict for {}:\n\t{}".format(
						model.__class__.__name__, "\n\t".join(error_msgs)
					)
				)
		model.tie_weights()  # make sure token embedding weights are still tied if needed

		# Set model in evaluation mode to deactivate DropOut modules by default
		model.eval()

		if output_loading_info:
			loading_info = {
				"missing_keys": missing_keys,
				"unexpected_keys": unexpected_keys,
				"error_msgs": error_msgs,
			}
			return model, loading_info

		if hasattr(config, "xla_device") and config.xla_device:
			import torch_xla.core.xla_model as xm

			model = xm.send_cpu_data_to_device(model, xm.xla_device())
			model.to(xm.xla_device())

		return model


class Conv1D(nn.Module):
	def __init__(self, nf, nx):
		""" Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)
			Basically works like a Linear layer but the weights are transposed
		"""
		super().__init__()
		self.nf = nf
		w = torch.empty(nx, nf)
		nn.init.normal_(w, std=0.02)
		self.weight = nn.Parameter(w)
		self.bias = nn.Parameter(torch.zeros(nf))

	def forward(self, x):
		size_out = x.size()[:-1] + (self.nf,)
		x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
		x = x.view(*size_out)
		return x


class SequenceSummary(nn.Module):
	r""" Compute a single vector summary of a sequence hidden states according to various possibilities:
		Args of the config class:
			summary_type:
				- 'last' => [default] take the last token hidden state (like XLNet)
				- 'first' => take the first token hidden state (like Bert)
				- 'mean' => take the mean of all tokens hidden states
				- 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
				- 'attn' => Not implemented now, use multi-head attention
			summary_use_proj: Add a projection after the vector extraction
			summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
			summary_activation: 'tanh' or another string => add an activation to the output, Other => no activation. Default
			summary_first_dropout: Add a dropout before the projection and activation
			summary_last_dropout: Add a dropout after the projection and activation
	"""

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

		self.summary_type = getattr(config, "summary_type", "last")
		if self.summary_type == "attn":
			# We should use a standard multi-head attention module with absolute positional embedding for that.
			# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
			# We can probably just use the multi-head attention module of PyTorch >=1.1.0
			raise NotImplementedError

		self.summary = Identity()
		if hasattr(config, "summary_use_proj") and config.summary_use_proj:
			if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
				num_classes = config.num_labels
			else:
				num_classes = config.hidden_size
			self.summary = nn.Linear(config.hidden_size, num_classes)

		activation_string = getattr(config, "summary_activation", None)
		self.activation: Callable = (get_activation(activation_string) if activation_string else Identity())

		self.first_dropout = Identity()
		if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
			self.first_dropout = nn.Dropout(config.summary_first_dropout)

		self.last_dropout = Identity()
		if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
			self.last_dropout = nn.Dropout(config.summary_last_dropout)

	def forward(self, hidden_states, cls_index=None):
		""" hidden_states: float Tensor in shape [bsz, ..., seq_len, hidden_size], the hidden-states of the last layer.
			cls_index: [optional] position of the classification token if summary_type == 'cls_index',
				shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states.
				if summary_type == 'cls_index' and cls_index is None:
					we take the last token of the sequence as classification token
		"""
		if self.summary_type == "last":
			output = hidden_states[:, -1]
		elif self.summary_type == "first":
			output = hidden_states[:, 0]
		elif self.summary_type == "mean":
			output = hidden_states.mean(dim=1)
		elif self.summary_type == "cls_index":
			if cls_index is None:
				cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long, )
			else:
				cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
				cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
			# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
			output = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, XX, hidden_size)
		elif self.summary_type == "attn":
			raise NotImplementedError

		output = self.first_dropout(output)
		output = self.summary(output)
		output = self.activation(output)
		output = self.last_dropout(output)

		return output


def create_position_ids_from_input_ids(input_ids, padding_idx):
	""" Replace non-padding symbols with their position numbers. Position numbers begin at
	padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
	`utils.make_positions`.

	:param torch.Tensor x:
	:return torch.Tensor:
	"""
	# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
	mask = input_ids.ne(padding_idx).int()
	incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
	return incremental_indices.long() + padding_idx


def prune_linear_layer(layer, index, dim=0):
	""" Prune a linear layer (a model parameters) to keep only entries in index.
		Return the pruned layer as a new layer with requires_grad=True.
		Used to remove heads.
	"""
	index = index.to(layer.weight.device)
	W = layer.weight.index_select(dim, index).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			b = layer.bias.clone().detach()
		else:
			b = layer.bias[index].clone().detach()
	new_size = list(layer.weight.size())
	new_size[dim] = len(index)
	new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
	new_layer.weight.requires_grad = False
	new_layer.weight.copy_(W.contiguous())
	new_layer.weight.requires_grad = True
	if layer.bias is not None:
		new_layer.bias.requires_grad = False
		new_layer.bias.copy_(b.contiguous())
		new_layer.bias.requires_grad = True
	return new_layer


def prune_conv1d_layer(layer, index, dim=1):
	""" Prune a Conv1D layer (a model parameters) to keep only entries in index.
		A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.
		Return the pruned layer as a new layer with requires_grad=True.
		Used to remove heads.
	"""
	index = index.to(layer.weight.device)
	W = layer.weight.index_select(dim, index).clone().detach()
	if dim == 0:
		b = layer.bias.clone().detach()
	else:
		b = layer.bias[index].clone().detach()
	new_size = list(layer.weight.size())
	new_size[dim] = len(index)
	new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
	new_layer.weight.requires_grad = False
	new_layer.weight.copy_(W.contiguous())
	new_layer.weight.requires_grad = True
	new_layer.bias.requires_grad = False
	new_layer.bias.copy_(b.contiguous())
	new_layer.bias.requires_grad = True
	return new_layer


def prune_layer(layer, index, dim=None):
	""" Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index.
		Return the pruned layer as a new layer with requires_grad=True.
		Used to remove heads.
	"""
	if isinstance(layer, nn.Linear):
		return prune_linear_layer(layer, index, dim=0 if dim is None else dim)
	elif isinstance(layer, Conv1D):
		return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)
	else:
		raise ValueError("Can't prune layer of class {}".format(layer.__class__))


def apply_chunking_to_forward(
		chunk_size: int, chunk_dim: int, forward_fn: Callable[..., torch.Tensor], *input_tensors
) -> torch.Tensor:
	"""
	This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension `chunk_dim`.
	It then applies a layer `forward_fn` to each chunk independently to save memory.
	If the `forward_fn` is independent across the `chunk_dim` this function will yield the
	same result as not applying it.

	Args:
		chunk_size: int - the chunk size of a chunked tensor. `num_chunks` = `len(input_tensors[0]) / chunk_size`
		chunk_dim: int - the dimension over which the input_tensors should be chunked
		forward_fn: fn - the forward fn of the model
		input_tensors: tuple(torch.Tensor) - the input tensors of `forward_fn` which are chunked
	Returns:
		a Tensor with the same shape the foward_fn would have given if applied


	Examples::

		# rename the usual forward() fn to forward_chunk()
		def forward_chunk(self, hidden_states):
			hidden_states = self.decoder(hidden_states)
			return hidden_states

		# implement a chunked forward function
		def forward(self, hidden_states):
			return apply_chunking_to_forward(self.chunk_size_lm_head, self.seq_len_dim, self.forward_chunk, hidden_states)
	"""

	assert len(input_tensors) > 0, "{} has to be a tuple/list of tensors".format(input_tensors)
	tensor_shape = input_tensors[0].shape
	assert all(
		input_tensor.shape == tensor_shape for input_tensor in input_tensors
	), "All input tenors have to be of the same shape"

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compability
	num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
	assert num_args_in_forward_chunk_fn == len(
		input_tensors
	), "forward_chunk_fn expects {} arguments, but only {} input tensors are given".format(
		num_args_in_forward_chunk_fn, len(input_tensors)
	)

	if chunk_size > 0:
		assert (
				input_tensors[0].shape[chunk_dim] % chunk_size == 0
		), "The dimension to be chunked {} has to be a multiple of the chunk size {}".format(
			input_tensors[0].shape[chunk_dim], chunk_size
		)

		num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, dim=chunk_dim) for input_tensor in input_tensors)
		# apply forward fn to every tuple
		output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))
		# concatenate output at same dimension
		return torch.cat(output_chunks, dim=chunk_dim)

	return forward_fn(*input_tensors)


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("Converting TensorFlow checkpoint from {}".format(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("Loading TF weight {} with shape {}".format(name, 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("Skipping {}".format("/".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("Skipping {}".format("/".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:
			assert pointer.shape == array.shape
		except AssertionError as e:
			e.args += (pointer.shape, array.shape)
			raise
		logger.info("Initialize PyTorch weight {}".format(name))
		pointer.data = torch.from_numpy(array)
	return model


def init_weights(module, initializer_range):
	""" Initialize the weights """
	if isinstance(module, (nn.Linear, nn.Embedding)):
		# 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=initializer_range)
	elif isinstance(module, (nn.LayerNorm, BertLayerNorm)):
		module.bias.data.zero_()
		module.weight.data.fill_(1.0)
	if isinstance(module, nn.Linear) and module.bias is not None:
		module.bias.data.zero_()


def mish(x):
	return x * torch.tanh(nn.functional.softplus(x))


ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}

# BertLayerNorm = nn.LayerNorm


try:
	import apex
	import apex.normalization
	#apex.amp.register_half_function(apex.normalization.fused_layer_norm, 'FusedLayerNorm')

	#apex.amp.register_half_function(apex.normalization.FusedLayerNorm, 'forward')
	BertLayerNorm = apex.normalization.FusedLayerNorm
	#BertLayerNorm = nn.LayerNorm
except ImportError:
	logger.warning("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.")
	BertLayerNorm = nn.LayerNorm


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 = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
		self.dropout = nn.Dropout(config.hidden_dropout_prob)

		self.apply(lambda m: init_weights(m, config.initializer_range))

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

		seq_length = input_shape[1]
		device = input_ids.device if input_ids is not None else inputs_embeds.device
		if position_ids is None:
			position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
			position_ids = position_ids.unsqueeze(0).expand(input_shape)
		if token_type_ids is None:
			token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

		if inputs_embeds is None:
			inputs_embeds = self.word_embeddings(input_ids)
		position_embeddings = self.position_embeddings(position_ids)
		token_type_embeddings = self.token_type_embeddings(token_type_ids)

		embeddings = inputs_embeds + position_embeddings + token_type_embeddings
		embeddings = self.LayerNorm(embeddings)
		embeddings = self.dropout(embeddings)
		return embeddings


class BertSelfAttention(nn.Module):
	def __init__(self, config):
		super().__init__()
		if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
			raise ValueError(
				"The hidden size (%d) is not a multiple of the number of attention "
				"heads (%d)" % (config.hidden_size, config.num_attention_heads)
			)
		self.output_attentions = config.output_attentions

		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.apply(lambda m: init_weights(m, config.initializer_range))

	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 forward(
			self,
			hidden_states,
			attention_mask=None,
			head_mask=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
	):
		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.
		if encoder_hidden_states is not None:
			mixed_key_layer = self.key(encoder_hidden_states)
			mixed_value_layer = self.value(encoder_hidden_states)
			attention_mask = encoder_attention_mask
		else:
			mixed_key_layer = self.key(hidden_states)
			mixed_value_layer = self.value(hidden_states)

		query_layer = self.transpose_for_scores(mixed_query_layer)
		key_layer = self.transpose_for_scores(mixed_key_layer)
		value_layer = self.transpose_for_scores(mixed_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))
		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.Softmax(dim=-1)(attention_scores)

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

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

		context_layer = torch.matmul(attention_probs, value_layer)

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

		outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
		return outputs


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

		self.apply(lambda m: init_weights(m, config.initializer_range))

	def forward(self, hidden_states, input_tensor):
		hidden_states = self.dense(hidden_states)
		hidden_states = self.dropout(hidden_states)
		# use fp32 for apex norm
		# with torch.cuda.amp.autocast(enabled=False):
		#hidden_states = self.LayerNorm(hidden_states.float() + input_tensor.float())
		hidden_states = self.LayerNorm(hidden_states + input_tensor)
		return hidden_states


class BertAttention(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.self = BertSelfAttention(config)
		self.output = BertSelfOutput(config)
		self.pruned_heads = set()

		self.apply(lambda m: init_weights(m, config.initializer_range))

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

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

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

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


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

		self.apply(lambda m: init_weights(m, config.initializer_range))

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


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

		self.apply(lambda m: init_weights(m, config.initializer_range))

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


class BertLayer(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.attention = BertAttention(config)
		self.is_decoder = config.is_decoder
		if self.is_decoder:
			self.crossattention = BertAttention(config)
		self.intermediate = BertIntermediate(config)
		self.output = BertOutput(config)

		self.apply(lambda m: init_weights(m, config.initializer_range))

	def forward(
			self,
			hidden_states,
			attention_mask=None,
			head_mask=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
	):
		self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
		attention_output = self_attention_outputs[0]
		outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

		if self.is_decoder and encoder_hidden_states is not None:
			cross_attention_outputs = self.crossattention(
				attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask
			)
			attention_output = cross_attention_outputs[0]
			outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights

		intermediate_output = self.intermediate(attention_output)
		layer_output = self.output(intermediate_output, attention_output)
		outputs = (layer_output,) + outputs
		return outputs


class BertEncoder(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.output_attentions = config.output_attentions
		self.output_hidden_states = config.output_hidden_states
		self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])

		self.apply(lambda m: init_weights(m, config.initializer_range))

	def forward(
			self,
			hidden_states,
			attention_mask=None,
			head_mask=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
	):
		all_hidden_states = ()
		all_attentions = ()
		for i, layer_module in enumerate(self.layer):
			if self.output_hidden_states:
				all_hidden_states = all_hidden_states + (hidden_states,)

			layer_outputs = layer_module(
				hidden_states, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask
			)
			hidden_states = layer_outputs[0]

			if self.output_attentions:
				all_attentions = all_attentions + (layer_outputs[1],)

		# Add last layer
		if self.output_hidden_states:
			all_hidden_states = all_hidden_states + (hidden_states,)

		outputs = (hidden_states,)
		if self.output_hidden_states:
			outputs = outputs + (all_hidden_states,)
		if self.output_attentions:
			outputs = outputs + (all_attentions,)
		return outputs  # last-layer hidden state, (all hidden states), (all attentions)


class BertPooler(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.dense = nn.Linear(config.hidden_size, config.hidden_size)
		self.activation = nn.Tanh()

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


class BertPredictionHeadTransform(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.dense = nn.Linear(config.hidden_size, config.hidden_size)
		if isinstance(config.hidden_act, str):
			self.transform_act_fn = ACT2FN[config.hidden_act]
		else:
			self.transform_act_fn = config.hidden_act
		self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)

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


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

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

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

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

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


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

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


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

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


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

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


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

	config_class = BertConfig
	load_tf_weights = load_tf_weights_in_bert
	base_model_prefix = "bert"

	def _init_weights(self, module):
		""" Initialize the weights """
		if isinstance(module, (nn.Linear, nn.Embedding)):
			# 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)
		elif isinstance(module, (nn.LayerNorm, BertLayerNorm)):
			module.bias.data.zero_()
			module.weight.data.fill_(1.0)
		if isinstance(module, nn.Linear) and module.bias is not None:
			module.bias.data.zero_()


BERT_START_DOCSTRING = r"""
	This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
	usage and behavior.

	Parameters:
		config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

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

			Indices can be obtained using :class:`transformers.BertTokenizer`.
			See :func:`transformers.PreTrainedTokenizer.encode` and
			:func:`transformers.PreTrainedTokenizer.encode_plus` for details.

			`What are input IDs? <../glossary.html#input-ids>`__
		attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
			Mask to avoid performing attention on padding token indices.
			Mask values selected in ``[0, 1]``:
			``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

			`What are attention masks? <../glossary.html#attention-mask>`__
		token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
			Segment token indices to indicate first and second portions of the inputs.
			Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
			corresponds to a `sentence B` token

			`What are token type IDs? <../glossary.html#token-type-ids>`_
		position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
			Indices of positions of each input sequence tokens in the position embeddings.
			Selected in the range ``[0, config.max_position_embeddings - 1]``.

			`What are position IDs? <../glossary.html#position-ids>`_
		head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
			Mask to nullify selected heads of the self-attention modules.
			Mask values selected in ``[0, 1]``:
			:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
		inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
			Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
			This is useful if you want more control over how to convert `input_ids` indices into associated vectors
			than the model's internal embedding lookup matrix.
		encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
			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 (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			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 MASKED tokens.
"""


@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`_ by <NAME>,
	<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME> and <NAME>.

	To behave as an decoder the model needs to be initialized with the
	:obj:`is_decoder` argument of the configuration set to :obj:`True`; an
	:obj:`encoder_hidden_states` is expected as an input to the forward pass.

	.. _`Attention is all you need`:
		https://arxiv.org/abs/1706.03762

	"""

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

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

		self.init_weights()

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

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

	def _prune_heads(self, heads_to_prune):
		""" Prunes heads of the model.
			heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
			See base class PreTrainedModel
		"""
		for layer, heads in heads_to_prune.items():
			self.encoder.layer[layer].attention.prune_heads(heads)

	@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
	def forward(
			self,
			input_ids=None,
			attention_mask=None,
			token_type_ids=None,
			position_ids=None,
			head_mask=None,
			inputs_embeds=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
	):
		r"""
	Return:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
		last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
			Sequence of hidden-states at the output of the last layer of the model.
		pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
			Last layer hidden-state of the first token of the sequence (classification token)
			further processed by a Linear layer and a Tanh activation function. The Linear
			layer weights are trained from the next sentence prediction (classification)
			objective during pre-training.

			This output is usually *not* a good summary
			of the semantic content of the input, you're often better with averaging or pooling
			the sequence of hidden-states for the whole input sequence.
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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

	Examples::

		from transformers import BertModel, BertTokenizer
		import torch

		tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
		model = BertModel.from_pretrained('bert-base-uncased')

		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
		outputs = model(input_ids)

		last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple

		"""

		if input_ids is not None and inputs_embeds is not None:
			raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
		elif input_ids is not None:
			input_shape = input_ids.size()
		elif inputs_embeds is not None:
			input_shape = inputs_embeds.size()[:-1]
		else:
			raise ValueError("You have to specify either input_ids or inputs_embeds")

		device = input_ids.device if input_ids is not None else inputs_embeds.device

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

		# 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 ou 3D attention mask is provided for the cross-attention
		# we need to make broadcastabe 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
		)
		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,
		)
		sequence_output = encoder_outputs[0]
		pooled_output = self.pooler(sequence_output)

		outputs = (sequence_output, pooled_output,) + encoder_outputs[
													  1:
													  ]  # add hidden_states and attentions if they are here
		return outputs  # sequence_output, pooled_output, (hidden_states), (attentions)


@add_start_docstrings(
	"""Bert Model with two heads on top as done during the pre-training: 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)

		self.init_weights()

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

	@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
	def forward(
			self,
			input_ids=None,
			attention_mask=None,
			token_type_ids=None,
			position_ids=None,
			head_mask=None,
			inputs_embeds=None,
			labels=None,
			next_sentence_label=None,
			**kwargs
	):
		r"""
		labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):
			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`, defaults to :obj:`None`):
			Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see :obj:`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 (:obj:`Dict[str, any]`, optional, defaults to `{}`):
			Used to hide legacy arguments that have been deprecated.

	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
		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_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
			Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
		seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
			Prediction scores of the next sequence prediction (classification) head (scores of True/False
			continuation before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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


	Examples::

		from transformers import BertTokenizer, BertForPreTraining
		import torch

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

		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
		outputs = model(input_ids)

		prediction_scores, seq_relationship_scores = outputs[:2]

		"""
		if "masked_lm_labels" in kwargs:
			warnings.warn(
				"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
				DeprecationWarning,
			)
			labels = kwargs.pop("masked_lm_labels")
		assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

		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,
		)

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

		outputs = (prediction_scores, seq_relationship_score,) + outputs[
																 2:
																 ]  # add hidden states and attention if they are here

		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
			outputs = (total_loss,) + outputs

		return outputs  # (loss), prediction_scores, seq_relationship_score, (hidden_states), (attentions)


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

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

		self.init_weights()

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

	@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
	def forward(
			self,
			input_ids=None,
			attention_mask=None,
			token_type_ids=None,
			position_ids=None,
			head_mask=None,
			inputs_embeds=None,
			labels=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
			lm_labels=None,
			**kwargs
	):
		r"""
		labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			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]``
		lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			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
			in ``[0, ..., config.vocab_size]``
		kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
			Used to hide legacy arguments that have been deprecated.

	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
		masked_lm_loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
			Masked language modeling loss.
		ltr_lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`lm_labels` is provided):
				Next token prediction loss.
		prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
			Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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

		Examples::

			from transformers import BertTokenizer, BertForMaskedLM
			import torch

			tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
			model = BertForMaskedLM.from_pretrained('bert-base-uncased')

			input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
			outputs = model(input_ids, labels=input_ids)

			loss, prediction_scores = outputs[:2]

		"""
		if "masked_lm_labels" in kwargs:
			warnings.warn(
				"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
				DeprecationWarning,
			)
			labels = kwargs.pop("masked_lm_labels")
		assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

		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,
		)

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

		outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here

		# Although this may seem awkward, BertForMaskedLM supports two scenarios:
		# 1. If a tensor that contains the indices of masked labels is provided,
		#    the cross-entropy is the MLM cross-entropy that measures the likelihood
		#    of predictions for masked words.
		# 2. If `lm_labels` is provided we are in a causal scenario where we
		#    try to predict the next token for each input in the decoder.
		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))
			outputs = (masked_lm_loss,) + outputs

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

		return outputs  # (ltr_lm_loss), (masked_lm_loss), prediction_scores, (hidden_states), (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]

		# 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)

		# if model is does not use a causal mask then add a dummy token
		if self.config.is_decoder is False:
			assert self.config.pad_token_id is not None, "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)

		self.init_weights()

	@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
	def forward(
			self,
			input_ids=None,
			attention_mask=None,
			token_type_ids=None,
			position_ids=None,
			head_mask=None,
			inputs_embeds=None,
			next_sentence_label=None,
	):
		r"""
		next_sentence_label (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
			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:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
		loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided):
			Next sequence prediction (classification) loss.
		seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
			Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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

	Examples::

		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.encode_plus(prompt, next_sentence, return_tensors='pt')

		loss, logits = model(**encoding, next_sentence_label=torch.LongTensor([1]))
		assert logits[0, 0] < logits[0, 1] # next sentence was random
		"""

		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,
		)

		pooled_output = outputs[1]

		seq_relationship_score = self.cls(pooled_output)

		outputs = (seq_relationship_score,) + outputs[2:]  # add hidden states and attention if they are here
		if next_sentence_label is not None:
			loss_fct = CrossEntropyLoss()
			next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
			outputs = (next_sentence_loss,) + outputs

		return outputs  # (next_sentence_loss), seq_relationship_score, (hidden_states), (attentions)

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research 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.

import inspect
import logging
import math
import warnings
import os
from typing import Callable, Dict, Iterable, List, Optional, Tuple

import torch
from torch import Tensor, device, dtype, nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F

from activations import get_activation, gelu, gelu_new, swish
from file_utils import add_start_docstrings, add_start_docstrings_to_callable
from configuration_utils import PretrainedConfig, BertConfig
from file_utils import (
	DUMMY_INPUTS,
	TF2_WEIGHTS_NAME,
	TF_WEIGHTS_NAME,
	WEIGHTS_NAME,
	cached_path,
	hf_bucket_url,
	is_remote_url,
)

logger = logging.getLogger(__name__)

try:
	from torch.nn import Identity
except ImportError:
	# Older PyTorch compatibility
	class Identity(nn.Module):
		r"""A placeholder identity operator that is argument-insensitive.
		"""

		def __init__(self, *args, **kwargs):
			super().__init__()

		def forward(self, input):
			return input


def find_pruneable_heads_and_indices(
		heads: List, n_heads: int, head_size: int, already_pruned_heads: set
) -> Tuple[set, "torch.LongTensor"]:
	mask = torch.ones(n_heads, head_size)
	heads = set(heads) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		head = head - sum(1 if h < head else 0 for h in already_pruned_heads)
		mask[head] = 0
	mask = mask.view(-1).contiguous().eq(1)
	index: torch.LongTensor = torch.arange(len(mask))[mask].long()
	return heads, index


class ModuleUtilsMixin:
	"""
	A few utilities for torch.nn.Modules, to be used as a mixin.
	"""

	def num_parameters(self, only_trainable: bool = False) -> int:
		"""
		Get number of (optionally, trainable) parameters in the module.
		"""
		params = filter(lambda x: x.requires_grad, self.parameters()) if only_trainable else self.parameters()
		return sum(p.numel() for p in params)

	@staticmethod
	def _hook_rss_memory_pre_forward(module, *args, **kwargs):
		try:
			import psutil
		except (ImportError):
			raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")

		process = psutil.Process(os.getpid())
		mem = process.memory_info()
		module.mem_rss_pre_forward = mem.rss
		return None

	@staticmethod
	def _hook_rss_memory_post_forward(module, *args, **kwargs):
		try:
			import psutil
		except (ImportError):
			raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")

		process = psutil.Process(os.getpid())
		mem = process.memory_info()
		module.mem_rss_post_forward = mem.rss
		mem_rss_diff = module.mem_rss_post_forward - module.mem_rss_pre_forward
		module.mem_rss_diff = mem_rss_diff + (module.mem_rss_diff if hasattr(module, "mem_rss_diff") else 0)
		return None

	def add_memory_hooks(self):
		""" Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.
			Increase in memory consumption is stored in a `mem_rss_diff` attribute for each module and can be reset to zero with `model.reset_memory_hooks_state()`
		"""
		for module in self.modules():
			module.register_forward_pre_hook(self._hook_rss_memory_pre_forward)
			module.register_forward_hook(self._hook_rss_memory_post_forward)
		self.reset_memory_hooks_state()

	def reset_memory_hooks_state(self):
		for module in self.modules():
			module.mem_rss_diff = 0
			module.mem_rss_post_forward = 0
			module.mem_rss_pre_forward = 0

	@property
	def device(self) -> device:
		"""
		Get torch.device from module, assuming that the whole module has one device.
		"""
		try:
			return next(self.parameters()).device
		except StopIteration:
			# For nn.DataParallel compatibility in PyTorch 1.5

			def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
				tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
				return tuples

			gen = self._named_members(get_members_fn=find_tensor_attributes)
			first_tuple = next(gen)
			return first_tuple[1].device

	@property
	def dtype(self) -> dtype:
		"""
		Get torch.dtype from module, assuming that the whole module has one dtype.
		"""
		try:
			return next(self.parameters()).dtype
		except StopIteration:
			# For nn.DataParallel compatibility in PyTorch 1.5

			def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
				tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
				return tuples

			gen = self._named_members(get_members_fn=find_tensor_attributes)
			first_tuple = next(gen)
			return first_tuple[1].dtype

	def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor:
		"""type: torch.Tensor -> torch.Tensor"""
		if encoder_attention_mask.dim() == 3:
			encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.dim() == 2:
			encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
		# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
		# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
		# /transformer/transformer_layers.py#L270
		# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
		# encoder_extended_attention_mask.transpose(-1, -2))
		encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility

		if self.dtype == torch.float16:
			encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
		elif self.dtype == torch.float32:
			encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
		else:
			raise ValueError(
				"{} not recognized. `dtype` should be set to either `torch.float32` or `torch.float16`".format(
					self.dtype
				)
			)

		return encoder_extended_attention_mask

	def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple, device: device) -> Tensor:
		"""Makes broadcastable attention mask and causal mask so that future and maked tokens are ignored.

		Arguments:
			attention_mask: torch.Tensor with 1 indicating tokens to ATTEND to
			input_shape: tuple, shape of input_ids
			device: torch.Device, usually self.device

		Returns:
			torch.Tensor with dtype of attention_mask.dtype
		"""
		# 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.
		if attention_mask.dim() == 3:
			extended_attention_mask = attention_mask[:, None, :, :]
		elif attention_mask.dim() == 2:
			# Provided a padding mask of dimensions [batch_size, seq_length]
			# - if the model is a decoder, apply a causal mask in addition to the padding mask
			# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
			if self.config.is_decoder:
				batch_size, seq_length = input_shape
				seq_ids = torch.arange(seq_length, device=device)
				causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
				# causal and attention masks must have same type with pytorch version < 1.3
				causal_mask = causal_mask.to(attention_mask.dtype)
				extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
			else:
				extended_attention_mask = attention_mask[:, None, None, :]
		else:
			raise ValueError(
				"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
					input_shape, attention_mask.shape
				)
			)

		# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
		# masked positions, this operation will create a tensor which is 0.0 for
		# positions we want to attend and -10000.0 for masked positions.
		# Since we are adding it to the raw scores before the softmax, this is
		# effectively the same as removing these entirely.
		extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
		extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
		return extended_attention_mask

	def get_head_mask(self, head_mask: Tensor, num_hidden_layers: int, is_attention_chunked: bool = False) -> Tensor:
		"""
		# 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
		Arguments:
			head_mask: torch.Tensor or None: has shape [num_heads] or [num_hidden_layers x num_heads]
			num_hidden_layers: int
		Returns:
			 Tensor of shape shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
			 or list with [None] for each layer
		"""
		if head_mask is not None:
			head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
			if is_attention_chunked is True:
				head_mask = head_mask.unsqueeze(-1)
		else:
			head_mask = [None] * num_hidden_layers

		return head_mask

	def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
		"""-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
		if head_mask.dim() == 1:
			head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
			head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
		elif head_mask.dim() == 2:
			head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
		assert head_mask.dim() == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
		head_mask = head_mask.to(dtype=self.dtype)  # switch to fload if need + fp16 compatibility
		return head_mask


class PreTrainedModel(nn.Module, ModuleUtilsMixin):
	r""" Base class for all models.

		:class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
		as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.

		Class attributes (overridden by derived classes):
			- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
			- ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:

				- ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`,
				- ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`,
				- ``path``: a path (string) to the TensorFlow checkpoint.

			- ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
	"""
	config_class = None
	base_model_prefix = ""

	@property
	def dummy_inputs(self):
		""" Dummy inputs to do a forward pass in the network.

		Returns:
			torch.Tensor with dummy inputs
		"""
		return {"input_ids": torch.tensor(DUMMY_INPUTS)}

	def __init__(self, config, *inputs, **kwargs):
		super().__init__()
		if not isinstance(config, PretrainedConfig):
			raise ValueError(
				"Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
				"To create a model from a pretrained model use "
				"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
					self.__class__.__name__, self.__class__.__name__
				)
			)
		# Save config in model
		self.config = config

	@property
	def base_model(self):
		return getattr(self, self.base_model_prefix, self)

	def get_input_embeddings(self):
		"""
		Returns the model's input embeddings.

		Returns:
			:obj:`nn.Module`:
				A torch module mapping vocabulary to hidden states.
		"""
		base_model = getattr(self, self.base_model_prefix, self)
		if base_model is not self:
			return base_model.get_input_embeddings()
		else:
			raise NotImplementedError

	def set_input_embeddings(self, value: nn.Module):
		"""
		Set model's input embeddings

		Args:
			value (:obj:`nn.Module`):
				A module mapping vocabulary to hidden states.
		"""
		base_model = getattr(self, self.base_model_prefix, self)
		if base_model is not self:
			base_model.set_input_embeddings(value)
		else:
			raise NotImplementedError

	def get_output_embeddings(self):
		"""
		Returns the model's output embeddings.

		Returns:
			:obj:`nn.Module`:
				A torch module mapping hidden states to vocabulary.
		"""
		return None  # Overwrite for models with output embeddings

	def tie_weights(self):
		"""
		Tie the weights between the input embeddings and the output embeddings.
		If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning
		the weights instead.
		"""
		output_embeddings = self.get_output_embeddings()
		if output_embeddings is not None:
			self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())

	def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
		""" Tie or clone module weights depending of whether we are using TorchScript or not
		"""
		if self.config.torchscript:
			output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone())
		else:
			output_embeddings.weight = input_embeddings.weight

		if getattr(output_embeddings, "bias", None) is not None:
			output_embeddings.bias.data = torch.nn.functional.pad(
				output_embeddings.bias.data,
				(0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],),
				"constant",
				0,
			)
		if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
			output_embeddings.out_features = input_embeddings.num_embeddings

	def resize_token_embeddings(self, new_num_tokens: Optional[int] = None):
		""" Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
		Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.

		Arguments:

			new_num_tokens: (`optional`) int:
				New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end.
				If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.

		Return: ``torch.nn.Embeddings``
			Pointer to the input tokens Embeddings Module of the model
		"""
		base_model = getattr(self, self.base_model_prefix, self)  # get the base model if needed
		model_embeds = base_model._resize_token_embeddings(new_num_tokens)
		if new_num_tokens is None:
			return model_embeds

		# Update base model and current model config
		self.config.vocab_size = new_num_tokens
		base_model.vocab_size = new_num_tokens

		# Tie weights again if needed
		self.tie_weights()

		return model_embeds

	def _resize_token_embeddings(self, new_num_tokens):
		old_embeddings = self.get_input_embeddings()
		new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
		self.set_input_embeddings(new_embeddings)
		return self.get_input_embeddings()

	def _get_resized_embeddings(
			self, old_embeddings: torch.nn.Embedding, new_num_tokens: Optional[int] = None
	) -> torch.nn.Embedding:
		""" Build a resized Embedding Module from a provided token Embedding Module.
			Increasing the size will add newly initialized vectors at the end
			Reducing the size will remove vectors from the end

		Args:
			old_embeddings: ``torch.nn.Embedding``
				Old embeddings to be resized.
			new_num_tokens: (`optional`) int
				New number of tokens in the embedding matrix.
				Increasing the size will add newly initialized vectors at the end
				Reducing the size will remove vectors from the end
				If not provided or None: return the provided token Embedding Module.
		Return: ``torch.nn.Embedding``
			Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
		"""
		if new_num_tokens is None:
			return old_embeddings

		old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
		if old_num_tokens == new_num_tokens:
			return old_embeddings

		# Build new embeddings
		new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
		new_embeddings.to(old_embeddings.weight.device)

		# initialize all new embeddings (in particular added tokens)
		self._init_weights(new_embeddings)

		# Copy token embeddings from the previous weights
		num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
		new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]

		return new_embeddings

	def init_weights(self):
		""" Initialize and prunes weights if needed. """
		# Initialize weights
		self.apply(self._init_weights)

		# Prune heads if needed
		if self.config.pruned_heads:
			self.prune_heads(self.config.pruned_heads)

		# Tie weights if needed
		self.tie_weights()

	def prune_heads(self, heads_to_prune: Dict):
		""" Prunes heads of the base model.

			Arguments:

				heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).
				E.g. {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
		"""
		# save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads
		for layer, heads in heads_to_prune.items():
			union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)
			self.config.pruned_heads[layer] = list(union_heads)  # Unfortunately we have to store it as list for JSON

		self.base_model._prune_heads(heads_to_prune)

	def save_pretrained(self, save_directory):
		""" Save a model and its configuration file to a directory, so that it
			can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.

			Arguments:
				save_directory: directory to which to save.
		"""
		if os.path.isfile(save_directory):
			logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
			return
		os.makedirs(save_directory, exist_ok=True)

		# Only save the model itself if we are using distributed training
		model_to_save = self.module if hasattr(self, "module") else self

		# Attach architecture to the config
		model_to_save.config.architectures = [model_to_save.__class__.__name__]

		# If we save using the predefined names, we can load using `from_pretrained`
		output_model_file = os.path.join(save_directory, WEIGHTS_NAME)

		if getattr(self.config, "xla_device", False):
			import torch_xla.core.xla_model as xm

			if xm.is_master_ordinal():
				# Save configuration file
				model_to_save.config.save_pretrained(save_directory)
			# xm.save takes care of saving only from master
			xm.save(model_to_save.state_dict(), output_model_file)
		else:
			model_to_save.config.save_pretrained(save_directory)
			torch.save(model_to_save.state_dict(), output_model_file)

		logger.info("Model weights saved in {}".format(output_model_file))

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
		r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.

		The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated)
		To train the model, you should first set it back in training mode with ``model.train()``

		The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.
		It is up to you to train those weights with a downstream fine-tuning task.

		The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.

		Parameters:
			pretrained_model_name_or_path: either:
			  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
			  - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
			  - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
			  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
			  - None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``)

			model_args: (`optional`) Sequence of positional arguments:
				All remaning positional arguments will be passed to the underlying model's ``__init__`` method

			config: (`optional`) one of:
				- an instance of a class derived from :class:`~transformers.PretrainedConfig`, or
				- a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained()`
				Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
					- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
					- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
					- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.

			state_dict: (`optional`) dict:
				an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
				This option can be used if you want to create a model from a pretrained configuration but load your own weights.
				In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.

			cache_dir: (`optional`) string:
				Path to a directory in which a downloaded pre-trained model
				configuration should be cached if the standard cache should not be used.

			force_download: (`optional`) boolean, default False:
				Force to (re-)download the model weights and configuration files and override the cached versions if they exists.

			resume_download: (`optional`) boolean, default False:
				Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.

			proxies: (`optional`) dict, default None:
				A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
				The proxies are used on each request.

			output_loading_info: (`optional`) boolean:
				Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.

			kwargs: (`optional`) Remaining dictionary of keyword arguments:
				Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:

				- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
				- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.

		Examples::

			# For example purposes. Not runnable.
			model = BertModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
			model = BertModel.from_pretrained('./test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
			model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
			assert model.config.output_attention == True
			# Loading from a TF checkpoint file instead of a PyTorch model (slower)
			config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
			model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)

		"""
		config = kwargs.pop("config", None)
		state_dict = kwargs.pop("state_dict", None)
		cache_dir = kwargs.pop("cache_dir", None)
		from_tf = kwargs.pop("from_tf", False)
		force_download = kwargs.pop("force_download", False)
		resume_download = kwargs.pop("resume_download", False)
		proxies = kwargs.pop("proxies", None)
		output_loading_info = kwargs.pop("output_loading_info", False)
		local_files_only = kwargs.pop("local_files_only", False)
		use_cdn = kwargs.pop("use_cdn", True)

		# Load config if we don't provide a configuration
		if not isinstance(config, PretrainedConfig):
			config_path = config if config is not None else pretrained_model_name_or_path
			config, model_kwargs = cls.config_class.from_pretrained(
				config_path,
				*model_args,
				cache_dir=cache_dir,
				return_unused_kwargs=True,
				force_download=force_download,
				resume_download=resume_download,
				proxies=proxies,
				local_files_only=local_files_only,
				**kwargs,
			)
		else:
			model_kwargs = kwargs

		# Load model
		if pretrained_model_name_or_path is not None:
			if os.path.isdir(pretrained_model_name_or_path):
				if from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")):
					# Load from a TF 1.0 checkpoint
					archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
				elif from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
					# Load from a TF 2.0 checkpoint
					archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
				elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
					# Load from a PyTorch checkpoint
					archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
				else:
					raise EnvironmentError(
						"Error no file named {} found in directory {} or `from_tf` set to False".format(
							[WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"],
							pretrained_model_name_or_path,
						)
					)
			elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
				archive_file = pretrained_model_name_or_path
			elif os.path.isfile(pretrained_model_name_or_path + ".index"):
				assert (
					from_tf
				), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format(
					pretrained_model_name_or_path + ".index"
				)
				archive_file = pretrained_model_name_or_path + ".index"
			else:
				archive_file = hf_bucket_url(
					pretrained_model_name_or_path,
					filename=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME),
					use_cdn=use_cdn,
				)

			try:
				# Load from URL or cache if already cached
				resolved_archive_file = cached_path(
					archive_file,
					cache_dir=cache_dir,
					force_download=force_download,
					proxies=proxies,
					resume_download=resume_download,
					local_files_only=local_files_only,
				)
				if resolved_archive_file is None:
					raise EnvironmentError
			except EnvironmentError:
				msg = (
					f"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
					f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
					f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a file named one of {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME}.\n\n"
				)
				raise EnvironmentError(msg)

			if resolved_archive_file == archive_file:
				logger.info("loading weights file {}".format(archive_file))
			else:
				logger.info("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file))
		else:
			resolved_archive_file = None

		# Instantiate model.
		model = cls(config, *model_args, **model_kwargs)

		if state_dict is None and not from_tf:
			try:
				state_dict = torch.load(resolved_archive_file, map_location="cpu")
			except Exception:
				raise OSError(
					"Unable to load weights from pytorch checkpoint file. "
					"If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. "
				)

		missing_keys = []
		unexpected_keys = []
		error_msgs = []

		if from_tf:
			if resolved_archive_file.endswith(".index"):
				# Load from a TensorFlow 1.X checkpoint - provided by original authors
				model = cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
			else:
				# Load from our TensorFlow 2.0 checkpoints
				try:
					from transformers import load_tf2_checkpoint_in_pytorch_model

					model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)
				except ImportError:
					logger.error(
						"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
						"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
					)
					raise
		else:
			# Convert old format to new format if needed from a PyTorch state_dict
			old_keys = []
			new_keys = []
			for key in state_dict.keys():
				new_key = None
				if "gamma" in key:
					new_key = key.replace("gamma", "weight")
				if "beta" in key:
					new_key = key.replace("beta", "bias")
				if new_key:
					old_keys.append(key)
					new_keys.append(new_key)
			for old_key, new_key in zip(old_keys, new_keys):
				state_dict[new_key] = state_dict.pop(old_key)

			# copy state_dict so _load_from_state_dict can modify it
			metadata = getattr(state_dict, "_metadata", None)
			state_dict = state_dict.copy()
			if metadata is not None:
				state_dict._metadata = metadata

			# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
			# so we need to apply the function recursively.
			def load(module: nn.Module, prefix=""):
				local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
				module._load_from_state_dict(
					state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs,
				)
				for name, child in module._modules.items():
					if child is not None:
						load(child, prefix + name + ".")

			# Make sure we are able to load base models as well as derived models (with heads)
			start_prefix = ""
			model_to_load = model
			has_prefix_module = any(s.startswith(cls.base_model_prefix) for s in state_dict.keys())
			if not hasattr(model, cls.base_model_prefix) and has_prefix_module:
				start_prefix = cls.base_model_prefix + "."
			if hasattr(model, cls.base_model_prefix) and not has_prefix_module:
				model_to_load = getattr(model, cls.base_model_prefix)

			load(model_to_load, prefix=start_prefix)

			if model.__class__.__name__ != model_to_load.__class__.__name__:
				base_model_state_dict = model_to_load.state_dict().keys()
				head_model_state_dict_without_base_prefix = [
					key.split(cls.base_model_prefix + ".")[-1] for key in model.state_dict().keys()
				]

				missing_keys.extend(head_model_state_dict_without_base_prefix - base_model_state_dict)

			if len(unexpected_keys) > 0:
				logger.warning(
					f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when "
					f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
					f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task "
					f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n"
					f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect "
					f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
				)
			else:
				logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
			if len(missing_keys) > 0:
				logger.warning(
					f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
					f"and are newly initialized: {missing_keys}\n"
					f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
				)
			else:
				logger.info(
					f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
					f"If your task is similar to the task the model of the ckeckpoint was trained on, "
					f"you can already use {model.__class__.__name__} for predictions without further training."
				)
			if len(error_msgs) > 0:
				raise RuntimeError(
					"Error(s) in loading state_dict for {}:\n\t{}".format(
						model.__class__.__name__, "\n\t".join(error_msgs)
					)
				)
		model.tie_weights()  # make sure token embedding weights are still tied if needed

		# Set model in evaluation mode to deactivate DropOut modules by default
		model.eval()

		if output_loading_info:
			loading_info = {
				"missing_keys": missing_keys,
				"unexpected_keys": unexpected_keys,
				"error_msgs": error_msgs,
			}
			return model, loading_info

		if hasattr(config, "xla_device") and config.xla_device:
			import torch_xla.core.xla_model as xm

			model = xm.send_cpu_data_to_device(model, xm.xla_device())
			model.to(xm.xla_device())

		return model


class Conv1D(nn.Module):
	def __init__(self, nf, nx):
		""" Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)
			Basically works like a Linear layer but the weights are transposed
		"""
		super().__init__()
		self.nf = nf
		w = torch.empty(nx, nf)
		nn.init.normal_(w, std=0.02)
		self.weight = nn.Parameter(w)
		self.bias = nn.Parameter(torch.zeros(nf))

	def forward(self, x):
		size_out = x.size()[:-1] + (self.nf,)
		x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
		x = x.view(*size_out)
		return x


class SequenceSummary(nn.Module):
	r""" Compute a single vector summary of a sequence hidden states according to various possibilities:
		Args of the config class:
			summary_type:
				- 'last' => [default] take the last token hidden state (like XLNet)
				- 'first' => take the first token hidden state (like Bert)
				- 'mean' => take the mean of all tokens hidden states
				- 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
				- 'attn' => Not implemented now, use multi-head attention
			summary_use_proj: Add a projection after the vector extraction
			summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
			summary_activation: 'tanh' or another string => add an activation to the output, Other => no activation. Default
			summary_first_dropout: Add a dropout before the projection and activation
			summary_last_dropout: Add a dropout after the projection and activation
	"""

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

		self.summary_type = getattr(config, "summary_type", "last")
		if self.summary_type == "attn":
			# We should use a standard multi-head attention module with absolute positional embedding for that.
			# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
			# We can probably just use the multi-head attention module of PyTorch >=1.1.0
			raise NotImplementedError

		self.summary = Identity()
		if hasattr(config, "summary_use_proj") and config.summary_use_proj:
			if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
				num_classes = config.num_labels
			else:
				num_classes = config.hidden_size
			self.summary = nn.Linear(config.hidden_size, num_classes)

		activation_string = getattr(config, "summary_activation", None)
		self.activation: Callable = (get_activation(activation_string) if activation_string else Identity())

		self.first_dropout = Identity()
		if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
			self.first_dropout = nn.Dropout(config.summary_first_dropout)

		self.last_dropout = Identity()
		if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
			self.last_dropout = nn.Dropout(config.summary_last_dropout)

	def forward(self, hidden_states, cls_index=None):
		""" hidden_states: float Tensor in shape [bsz, ..., seq_len, hidden_size], the hidden-states of the last layer.
			cls_index: [optional] position of the classification token if summary_type == 'cls_index',
				shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states.
				if summary_type == 'cls_index' and cls_index is None:
					we take the last token of the sequence as classification token
		"""
		if self.summary_type == "last":
			output = hidden_states[:, -1]
		elif self.summary_type == "first":
			output = hidden_states[:, 0]
		elif self.summary_type == "mean":
			output = hidden_states.mean(dim=1)
		elif self.summary_type == "cls_index":
			if cls_index is None:
				cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long, )
			else:
				cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
				cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
			# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
			output = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, XX, hidden_size)
		elif self.summary_type == "attn":
			raise NotImplementedError

		output = self.first_dropout(output)
		output = self.summary(output)
		output = self.activation(output)
		output = self.last_dropout(output)

		return output


def create_position_ids_from_input_ids(input_ids, padding_idx):
	""" Replace non-padding symbols with their position numbers. Position numbers begin at
	padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
	`utils.make_positions`.

	:param torch.Tensor x:
	:return torch.Tensor:
	"""
	# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
	mask = input_ids.ne(padding_idx).int()
	incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
	return incremental_indices.long() + padding_idx


def prune_linear_layer(layer, index, dim=0):
	""" Prune a linear layer (a model parameters) to keep only entries in index.
		Return the pruned layer as a new layer with requires_grad=True.
		Used to remove heads.
	"""
	index = index.to(layer.weight.device)
	W = layer.weight.index_select(dim, index).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			b = layer.bias.clone().detach()
		else:
			b = layer.bias[index].clone().detach()
	new_size = list(layer.weight.size())
	new_size[dim] = len(index)
	new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
	new_layer.weight.requires_grad = False
	new_layer.weight.copy_(W.contiguous())
	new_layer.weight.requires_grad = True
	if layer.bias is not None:
		new_layer.bias.requires_grad = False
		new_layer.bias.copy_(b.contiguous())
		new_layer.bias.requires_grad = True
	return new_layer


def prune_conv1d_layer(layer, index, dim=1):
	""" Prune a Conv1D layer (a model parameters) to keep only entries in index.
		A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.
		Return the pruned layer as a new layer with requires_grad=True.
		Used to remove heads.
	"""
	index = index.to(layer.weight.device)
	W = layer.weight.index_select(dim, index).clone().detach()
	if dim == 0:
		b = layer.bias.clone().detach()
	else:
		b = layer.bias[index].clone().detach()
	new_size = list(layer.weight.size())
	new_size[dim] = len(index)
	new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
	new_layer.weight.requires_grad = False
	new_layer.weight.copy_(W.contiguous())
	new_layer.weight.requires_grad = True
	new_layer.bias.requires_grad = False
	new_layer.bias.copy_(b.contiguous())
	new_layer.bias.requires_grad = True
	return new_layer


def prune_layer(layer, index, dim=None):
	""" Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index.
		Return the pruned layer as a new layer with requires_grad=True.
		Used to remove heads.
	"""
	if isinstance(layer, nn.Linear):
		return prune_linear_layer(layer, index, dim=0 if dim is None else dim)
	elif isinstance(layer, Conv1D):
		return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)
	else:
		raise ValueError("Can't prune layer of class {}".format(layer.__class__))


def apply_chunking_to_forward(
		chunk_size: int, chunk_dim: int, forward_fn: Callable[..., torch.Tensor], *input_tensors
) -> torch.Tensor:
	"""
	This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension `chunk_dim`.
	It then applies a layer `forward_fn` to each chunk independently to save memory.
	If the `forward_fn` is independent across the `chunk_dim` this function will yield the
	same result as not applying it.

	Args:
		chunk_size: int - the chunk size of a chunked tensor. `num_chunks` = `len(input_tensors[0]) / chunk_size`
		chunk_dim: int - the dimension over which the input_tensors should be chunked
		forward_fn: fn - the forward fn of the model
		input_tensors: tuple(torch.Tensor) - the input tensors of `forward_fn` which are chunked
	Returns:
		a Tensor with the same shape the foward_fn would have given if applied


	Examples::

		# rename the usual forward() fn to forward_chunk()
		def forward_chunk(self, hidden_states):
			hidden_states = self.decoder(hidden_states)
			return hidden_states

		# implement a chunked forward function
		def forward(self, hidden_states):
			return apply_chunking_to_forward(self.chunk_size_lm_head, self.seq_len_dim, self.forward_chunk, hidden_states)
	"""

	assert len(input_tensors) > 0, "{} has to be a tuple/list of tensors".format(input_tensors)
	tensor_shape = input_tensors[0].shape
	assert all(
		input_tensor.shape == tensor_shape for input_tensor in input_tensors
	), "All input tenors have to be of the same shape"

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compability
	num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
	assert num_args_in_forward_chunk_fn == len(
		input_tensors
	), "forward_chunk_fn expects {} arguments, but only {} input tensors are given".format(
		num_args_in_forward_chunk_fn, len(input_tensors)
	)

	if chunk_size > 0:
		assert (
				input_tensors[0].shape[chunk_dim] % chunk_size == 0
		), "The dimension to be chunked {} has to be a multiple of the chunk size {}".format(
			input_tensors[0].shape[chunk_dim], chunk_size
		)

		num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, dim=chunk_dim) for input_tensor in input_tensors)
		# apply forward fn to every tuple
		output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))
		# concatenate output at same dimension
		return torch.cat(output_chunks, dim=chunk_dim)

	return forward_fn(*input_tensors)


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("Converting TensorFlow checkpoint from {}".format(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("Loading TF weight {} with shape {}".format(name, 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("Skipping {}".format("/".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("Skipping {}".format("/".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:
			assert pointer.shape == array.shape
		except AssertionError as e:
			e.args += (pointer.shape, array.shape)
			raise
		logger.info("Initialize PyTorch weight {}".format(name))
		pointer.data = torch.from_numpy(array)
	return model


def init_weights(module, initializer_range):
	""" Initialize the weights """
	if isinstance(module, (nn.Linear, nn.Embedding)):
		# 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=initializer_range)
	elif isinstance(module, (nn.LayerNorm, BertLayerNorm)):
		module.bias.data.zero_()
		module.weight.data.fill_(1.0)
	if isinstance(module, nn.Linear) and module.bias is not None:
		module.bias.data.zero_()


def mish(x):
	return x * torch.tanh(nn.functional.softplus(x))


ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}

# BertLayerNorm = nn.LayerNorm


try:
	import apex
	import apex.normalization
	#apex.amp.register_half_function(apex.normalization.fused_layer_norm, 'FusedLayerNorm')

	#apex.amp.register_half_function(apex.normalization.FusedLayerNorm, 'forward')
	BertLayerNorm = apex.normalization.FusedLayerNorm
	#BertLayerNorm = nn.LayerNorm
except ImportError:
	logger.warning("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.")
	BertLayerNorm = nn.LayerNorm


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 = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
		self.dropout = nn.Dropout(config.hidden_dropout_prob)

		self.apply(lambda m: init_weights(m, config.initializer_range))

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

		seq_length = input_shape[1]
		device = input_ids.device if input_ids is not None else inputs_embeds.device
		if position_ids is None:
			position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
			position_ids = position_ids.unsqueeze(0).expand(input_shape)
		if token_type_ids is None:
			token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

		if inputs_embeds is None:
			inputs_embeds = self.word_embeddings(input_ids)
		position_embeddings = self.position_embeddings(position_ids)
		token_type_embeddings = self.token_type_embeddings(token_type_ids)

		embeddings = inputs_embeds + position_embeddings + token_type_embeddings
		embeddings = self.LayerNorm(embeddings)
		embeddings = self.dropout(embeddings)
		return embeddings


class BertSelfAttention(nn.Module):
	def __init__(self, config):
		super().__init__()
		if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
			raise ValueError(
				"The hidden size (%d) is not a multiple of the number of attention "
				"heads (%d)" % (config.hidden_size, config.num_attention_heads)
			)
		self.output_attentions = config.output_attentions

		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.apply(lambda m: init_weights(m, config.initializer_range))

	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 forward(
			self,
			hidden_states,
			attention_mask=None,
			head_mask=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
	):
		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.
		if encoder_hidden_states is not None:
			mixed_key_layer = self.key(encoder_hidden_states)
			mixed_value_layer = self.value(encoder_hidden_states)
			attention_mask = encoder_attention_mask
		else:
			mixed_key_layer = self.key(hidden_states)
			mixed_value_layer = self.value(hidden_states)

		query_layer = self.transpose_for_scores(mixed_query_layer)
		key_layer = self.transpose_for_scores(mixed_key_layer)
		value_layer = self.transpose_for_scores(mixed_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))
		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.Softmax(dim=-1)(attention_scores)

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

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

		context_layer = torch.matmul(attention_probs, value_layer)

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

		outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
		return outputs


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

		self.apply(lambda m: init_weights(m, config.initializer_range))

	def forward(self, hidden_states, input_tensor):
		hidden_states = self.dense(hidden_states)
		hidden_states = self.dropout(hidden_states)
		# use fp32 for apex norm
		# with torch.cuda.amp.autocast(enabled=False):
		#hidden_states = self.LayerNorm(hidden_states.float() + input_tensor.float())
		hidden_states = self.LayerNorm(hidden_states + input_tensor)
		return hidden_states


class BertAttention(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.self = BertSelfAttention(config)
		self.output = BertSelfOutput(config)
		self.pruned_heads = set()

		self.apply(lambda m: init_weights(m, config.initializer_range))

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

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

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

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


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

		self.apply(lambda m: init_weights(m, config.initializer_range))

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


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

		self.apply(lambda m: init_weights(m, config.initializer_range))

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


class BertLayer(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.attention = BertAttention(config)
		self.is_decoder = config.is_decoder
		if self.is_decoder:
			self.crossattention = BertAttention(config)
		self.intermediate = BertIntermediate(config)
		self.output = BertOutput(config)

		self.apply(lambda m: init_weights(m, config.initializer_range))

	def forward(
			self,
			hidden_states,
			attention_mask=None,
			head_mask=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
	):
		self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
		attention_output = self_attention_outputs[0]
		outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

		if self.is_decoder and encoder_hidden_states is not None:
			cross_attention_outputs = self.crossattention(
				attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask
			)
			attention_output = cross_attention_outputs[0]
			outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights

		intermediate_output = self.intermediate(attention_output)
		layer_output = self.output(intermediate_output, attention_output)
		outputs = (layer_output,) + outputs
		return outputs


class BertEncoder(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.output_attentions = config.output_attentions
		self.output_hidden_states = config.output_hidden_states
		self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])

		self.apply(lambda m: init_weights(m, config.initializer_range))

	def forward(
			self,
			hidden_states,
			attention_mask=None,
			head_mask=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
	):
		all_hidden_states = ()
		all_attentions = ()
		for i, layer_module in enumerate(self.layer):
			if self.output_hidden_states:
				all_hidden_states = all_hidden_states + (hidden_states,)

			layer_outputs = layer_module(
				hidden_states, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask
			)
			hidden_states = layer_outputs[0]

			if self.output_attentions:
				all_attentions = all_attentions + (layer_outputs[1],)

		# Add last layer
		if self.output_hidden_states:
			all_hidden_states = all_hidden_states + (hidden_states,)

		outputs = (hidden_states,)
		if self.output_hidden_states:
			outputs = outputs + (all_hidden_states,)
		if self.output_attentions:
			outputs = outputs + (all_attentions,)
		return outputs  # last-layer hidden state, (all hidden states), (all attentions)


class BertPooler(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.dense = nn.Linear(config.hidden_size, config.hidden_size)
		self.activation = nn.Tanh()

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


class BertPredictionHeadTransform(nn.Module):
	def __init__(self, config):
		super().__init__()
		self.dense = nn.Linear(config.hidden_size, config.hidden_size)
		if isinstance(config.hidden_act, str):
			self.transform_act_fn = ACT2FN[config.hidden_act]
		else:
			self.transform_act_fn = config.hidden_act
		self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)

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


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

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

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

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

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


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

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


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

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


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

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


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

	config_class = BertConfig
	load_tf_weights = load_tf_weights_in_bert
	base_model_prefix = "bert"

	def _init_weights(self, module):
		""" Initialize the weights """
		if isinstance(module, (nn.Linear, nn.Embedding)):
			# 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)
		elif isinstance(module, (nn.LayerNorm, BertLayerNorm)):
			module.bias.data.zero_()
			module.weight.data.fill_(1.0)
		if isinstance(module, nn.Linear) and module.bias is not None:
			module.bias.data.zero_()


BERT_START_DOCSTRING = r"""
	This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
	usage and behavior.

	Parameters:
		config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

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

			Indices can be obtained using :class:`transformers.BertTokenizer`.
			See :func:`transformers.PreTrainedTokenizer.encode` and
			:func:`transformers.PreTrainedTokenizer.encode_plus` for details.

			`What are input IDs? <../glossary.html#input-ids>`__
		attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
			Mask to avoid performing attention on padding token indices.
			Mask values selected in ``[0, 1]``:
			``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

			`What are attention masks? <../glossary.html#attention-mask>`__
		token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
			Segment token indices to indicate first and second portions of the inputs.
			Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
			corresponds to a `sentence B` token

			`What are token type IDs? <../glossary.html#token-type-ids>`_
		position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
			Indices of positions of each input sequence tokens in the position embeddings.
			Selected in the range ``[0, config.max_position_embeddings - 1]``.

			`What are position IDs? <../glossary.html#position-ids>`_
		head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
			Mask to nullify selected heads of the self-attention modules.
			Mask values selected in ``[0, 1]``:
			:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
		inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
			Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
			This is useful if you want more control over how to convert `input_ids` indices into associated vectors
			than the model's internal embedding lookup matrix.
		encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
			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 (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			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 MASKED tokens.
"""


@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`_ by <NAME>,
	<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME> and <NAME>.

	To behave as an decoder the model needs to be initialized with the
	:obj:`is_decoder` argument of the configuration set to :obj:`True`; an
	:obj:`encoder_hidden_states` is expected as an input to the forward pass.

	.. _`Attention is all you need`:
		https://arxiv.org/abs/1706.03762

	"""

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

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

		self.init_weights()

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

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

	def _prune_heads(self, heads_to_prune):
		""" Prunes heads of the model.
			heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
			See base class PreTrainedModel
		"""
		for layer, heads in heads_to_prune.items():
			self.encoder.layer[layer].attention.prune_heads(heads)

	@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
	def forward(
			self,
			input_ids=None,
			attention_mask=None,
			token_type_ids=None,
			position_ids=None,
			head_mask=None,
			inputs_embeds=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
	):
		r"""
	Return:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
		last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
			Sequence of hidden-states at the output of the last layer of the model.
		pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
			Last layer hidden-state of the first token of the sequence (classification token)
			further processed by a Linear layer and a Tanh activation function. The Linear
			layer weights are trained from the next sentence prediction (classification)
			objective during pre-training.

			This output is usually *not* a good summary
			of the semantic content of the input, you're often better with averaging or pooling
			the sequence of hidden-states for the whole input sequence.
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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

	Examples::

		from transformers import BertModel, BertTokenizer
		import torch

		tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
		model = BertModel.from_pretrained('bert-base-uncased')

		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
		outputs = model(input_ids)

		last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple

		"""

		if input_ids is not None and inputs_embeds is not None:
			raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
		elif input_ids is not None:
			input_shape = input_ids.size()
		elif inputs_embeds is not None:
			input_shape = inputs_embeds.size()[:-1]
		else:
			raise ValueError("You have to specify either input_ids or inputs_embeds")

		device = input_ids.device if input_ids is not None else inputs_embeds.device

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

		# 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 ou 3D attention mask is provided for the cross-attention
		# we need to make broadcastabe 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
		)
		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,
		)
		sequence_output = encoder_outputs[0]
		pooled_output = self.pooler(sequence_output)

		outputs = (sequence_output, pooled_output,) + encoder_outputs[
													  1:
													  ]  # add hidden_states and attentions if they are here
		return outputs  # sequence_output, pooled_output, (hidden_states), (attentions)


@add_start_docstrings(
	"""Bert Model with two heads on top as done during the pre-training: 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)

		self.init_weights()

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

	@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
	def forward(
			self,
			input_ids=None,
			attention_mask=None,
			token_type_ids=None,
			position_ids=None,
			head_mask=None,
			inputs_embeds=None,
			labels=None,
			next_sentence_label=None,
			**kwargs
	):
		r"""
		labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):
			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`, defaults to :obj:`None`):
			Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see :obj:`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 (:obj:`Dict[str, any]`, optional, defaults to `{}`):
			Used to hide legacy arguments that have been deprecated.

	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
		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_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
			Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
		seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
			Prediction scores of the next sequence prediction (classification) head (scores of True/False
			continuation before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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


	Examples::

		from transformers import BertTokenizer, BertForPreTraining
		import torch

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

		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
		outputs = model(input_ids)

		prediction_scores, seq_relationship_scores = outputs[:2]

		"""
		if "masked_lm_labels" in kwargs:
			warnings.warn(
				"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
				DeprecationWarning,
			)
			labels = kwargs.pop("masked_lm_labels")
		assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

		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,
		)

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

		outputs = (prediction_scores, seq_relationship_score,) + outputs[
																 2:
																 ]  # add hidden states and attention if they are here

		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
			outputs = (total_loss,) + outputs

		return outputs  # (loss), prediction_scores, seq_relationship_score, (hidden_states), (attentions)


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

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

		self.init_weights()

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

	@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
	def forward(
			self,
			input_ids=None,
			attention_mask=None,
			token_type_ids=None,
			position_ids=None,
			head_mask=None,
			inputs_embeds=None,
			labels=None,
			encoder_hidden_states=None,
			encoder_attention_mask=None,
			lm_labels=None,
			**kwargs
	):
		r"""
		labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			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]``
		lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			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
			in ``[0, ..., config.vocab_size]``
		kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
			Used to hide legacy arguments that have been deprecated.

	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
		masked_lm_loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
			Masked language modeling loss.
		ltr_lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`lm_labels` is provided):
				Next token prediction loss.
		prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
			Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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

		Examples::

			from transformers import BertTokenizer, BertForMaskedLM
			import torch

			tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
			model = BertForMaskedLM.from_pretrained('bert-base-uncased')

			input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
			outputs = model(input_ids, labels=input_ids)

			loss, prediction_scores = outputs[:2]

		"""
		if "masked_lm_labels" in kwargs:
			warnings.warn(
				"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
				DeprecationWarning,
			)
			labels = kwargs.pop("masked_lm_labels")
		assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

		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,
		)

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

		outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here

		# Although this may seem awkward, BertForMaskedLM supports two scenarios:
		# 1. If a tensor that contains the indices of masked labels is provided,
		#    the cross-entropy is the MLM cross-entropy that measures the likelihood
		#    of predictions for masked words.
		# 2. If `lm_labels` is provided we are in a causal scenario where we
		#    try to predict the next token for each input in the decoder.
		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))
			outputs = (masked_lm_loss,) + outputs

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

		return outputs  # (ltr_lm_loss), (masked_lm_loss), prediction_scores, (hidden_states), (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]

		# 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)

		# if model is does not use a causal mask then add a dummy token
		if self.config.is_decoder is False:
			assert self.config.pad_token_id is not None, "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)

		self.init_weights()

	@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
	def forward(
			self,
			input_ids=None,
			attention_mask=None,
			token_type_ids=None,
			position_ids=None,
			head_mask=None,
			inputs_embeds=None,
			next_sentence_label=None,
	):
		r"""
		next_sentence_label (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
			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:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
		loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided):
			Next sequence prediction (classification) loss.
		seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
			Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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

	Examples::

		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.encode_plus(prompt, next_sentence, return_tensors='pt')

		loss, logits = model(**encoding, next_sentence_label=torch.LongTensor([1]))
		assert logits[0, 0] < logits[0, 1] # next sentence was random
		"""

		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,
		)

		pooled_output = outputs[1]

		seq_relationship_score = self.cls(pooled_output)

		outputs = (seq_relationship_score,) + outputs[2:]  # add hidden states and attention if they are here
		if next_sentence_label is not None:
			loss_fct = CrossEntropyLoss()
			next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
			outputs = (next_sentence_loss,) + outputs

		return outputs  # (next_sentence_loss), seq_relationship_score, (hidden_states), (attentions)
# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors, Facebook AI Research 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. # Older PyTorch compatibility A placeholder identity operator that is argument-insensitive. # Convert to set and remove already pruned heads # Compute how many pruned heads are before the head and move the index accordingly A few utilities for torch.nn.Modules, to be used as a mixin. Get number of (optionally, trainable) parameters in the module. Add a memory hook before and after each sub-module forward pass to record increase in memory consumption. 			Increase in memory consumption is stored in a `mem_rss_diff` attribute for each module and can be reset to zero with `model.reset_memory_hooks_state()` Get torch.device from module, assuming that the whole module has one device. # For nn.DataParallel compatibility in PyTorch 1.5 Get torch.dtype from module, assuming that the whole module has one dtype. # For nn.DataParallel compatibility in PyTorch 1.5 type: torch.Tensor -> torch.Tensor # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow # /transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = (encoder_extended_attention_mask == # encoder_extended_attention_mask.transpose(-1, -2)) # fp16 compatibility Makes broadcastable attention mask and causal mask so that future and maked tokens are ignored.  		Arguments: 			attention_mask: torch.Tensor with 1 indicating tokens to ATTEND to 			input_shape: tuple, shape of input_ids 			device: torch.Device, usually self.device  		Returns: 			torch.Tensor with dtype of attention_mask.dtype # 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. # Provided a padding mask of dimensions [batch_size, seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] # causal and attention masks must have same type with pytorch version < 1.3 # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. # fp16 compatibility # 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 		Arguments: 			head_mask: torch.Tensor or None: has shape [num_heads] or [num_hidden_layers x num_heads] 			num_hidden_layers: int 		Returns: 			 Tensor of shape shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] 			 or list with [None] for each layer -> [num_hidden_layers x batch x num_heads x seq_length x seq_length] # We can specify head_mask for each layer # switch to fload if need + fp16 compatibility Base class for all models.  		:class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models 		as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.  		Class attributes (overridden by derived classes): 			- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture. 			- ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:  				- ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`, 				- ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`, 				- ``path``: a path (string) to the TensorFlow checkpoint.  			- ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model. Dummy inputs to do a forward pass in the network.  		Returns: 			torch.Tensor with dummy inputs # Save config in model Returns the model's input embeddings.  		Returns: 			:obj:`nn.Module`: 				A torch module mapping vocabulary to hidden states. Set model's input embeddings  		Args: 			value (:obj:`nn.Module`): 				A module mapping vocabulary to hidden states. Returns the model's output embeddings.  		Returns: 			:obj:`nn.Module`: 				A torch module mapping hidden states to vocabulary. # Overwrite for models with output embeddings Tie the weights between the input embeddings and the output embeddings. 		If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning 		the weights instead. Tie or clone module weights depending of whether we are using TorchScript or not Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size. 		Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.  		Arguments:  			new_num_tokens: (`optional`) int: 				New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. 				If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.  		Return: ``torch.nn.Embeddings`` 			Pointer to the input tokens Embeddings Module of the model # get the base model if needed # Update base model and current model config # Tie weights again if needed Build a resized Embedding Module from a provided token Embedding Module. 			Increasing the size will add newly initialized vectors at the end 			Reducing the size will remove vectors from the end  		Args: 			old_embeddings: ``torch.nn.Embedding`` 				Old embeddings to be resized. 			new_num_tokens: (`optional`) int 				New number of tokens in the embedding matrix. 				Increasing the size will add newly initialized vectors at the end 				Reducing the size will remove vectors from the end 				If not provided or None: return the provided token Embedding Module. 		Return: ``torch.nn.Embedding`` 			Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None # Build new embeddings # initialize all new embeddings (in particular added tokens) # Copy token embeddings from the previous weights Initialize and prunes weights if needed. # Initialize weights # Prune heads if needed # Tie weights if needed Prunes heads of the base model.  			Arguments:  				heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`). 				E.g. {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2. # save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads # Unfortunately we have to store it as list for JSON Save a model and its configuration file to a directory, so that it 			can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.  			Arguments: 				save_directory: directory to which to save. # Only save the model itself if we are using distributed training # Attach architecture to the config # If we save using the predefined names, we can load using `from_pretrained` # Save configuration file # xm.save takes care of saving only from master Instantiate a pretrained pytorch model from a pre-trained model configuration.  		The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated) 		To train the model, you should first set it back in training mode with ``model.train()``  		The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model. 		It is up to you to train those weights with a downstream fine-tuning task.  		The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.  		Parameters: 			pretrained_model_name_or_path: either: 			  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. 			  - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``. 			  - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``. 			  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. 			  - None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``)  			model_args: (`optional`) Sequence of positional arguments: 				All remaning positional arguments will be passed to the underlying model's ``__init__`` method  			config: (`optional`) one of: 				- an instance of a class derived from :class:`~transformers.PretrainedConfig`, or 				- a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained()` 				Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when: 					- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or 					- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory. 					- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.  			state_dict: (`optional`) dict: 				an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file. 				This option can be used if you want to create a model from a pretrained configuration but load your own weights. 				In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.  			cache_dir: (`optional`) string: 				Path to a directory in which a downloaded pre-trained model 				configuration should be cached if the standard cache should not be used.  			force_download: (`optional`) boolean, default False: 				Force to (re-)download the model weights and configuration files and override the cached versions if they exists.  			resume_download: (`optional`) boolean, default False: 				Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.  			proxies: (`optional`) dict, default None: 				A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. 				The proxies are used on each request.  			output_loading_info: (`optional`) boolean: 				Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.  			kwargs: (`optional`) Remaining dictionary of keyword arguments: 				Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:  				- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done) 				- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.  		Examples::  			# For example purposes. Not runnable. 			model = BertModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache. 			model = BertModel.from_pretrained('./test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')` 			model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading 			assert model.config.output_attention == True 			# Loading from a TF checkpoint file instead of a PyTorch model (slower) 			config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json') 			model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config) # Load config if we don't provide a configuration # Load model # Load from a TF 1.0 checkpoint # Load from a TF 2.0 checkpoint # Load from a PyTorch checkpoint # Load from URL or cache if already cached # Instantiate model. # Load from a TensorFlow 1.X checkpoint - provided by original authors # Remove the '.index' # Load from our TensorFlow 2.0 checkpoints # Convert old format to new format if needed from a PyTorch state_dict # copy state_dict so _load_from_state_dict can modify it # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants # so we need to apply the function recursively. # Make sure we are able to load base models as well as derived models (with heads) # make sure token embedding weights are still tied if needed # Set model in evaluation mode to deactivate DropOut modules by default Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2) 			Basically works like a Linear layer but the weights are transposed Compute a single vector summary of a sequence hidden states according to various possibilities: 		Args of the config class: 			summary_type: 				- 'last' => [default] take the last token hidden state (like XLNet) 				- 'first' => take the first token hidden state (like Bert) 				- 'mean' => take the mean of all tokens hidden states 				- 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2) 				- 'attn' => Not implemented now, use multi-head attention 			summary_use_proj: Add a projection after the vector extraction 			summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False. 			summary_activation: 'tanh' or another string => add an activation to the output, Other => no activation. Default 			summary_first_dropout: Add a dropout before the projection and activation 			summary_last_dropout: Add a dropout after the projection and activation # We should use a standard multi-head attention module with absolute positional embedding for that. # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276 # We can probably just use the multi-head attention module of PyTorch >=1.1.0 hidden_states: float Tensor in shape [bsz, ..., seq_len, hidden_size], the hidden-states of the last layer. 			cls_index: [optional] position of the classification token if summary_type == 'cls_index', 				shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states. 				if summary_type == 'cls_index' and cls_index is None: 					we take the last token of the sequence as classification token # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states # shape (bsz, XX, hidden_size) Replace non-padding symbols with their position numbers. Position numbers begin at 	padding_idx+1. Padding symbols are ignored. This is modified from fairseq's 	`utils.make_positions`.  	:param torch.Tensor x: 	:return torch.Tensor: # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. Prune a linear layer (a model parameters) to keep only entries in index. 		Return the pruned layer as a new layer with requires_grad=True. 		Used to remove heads. Prune a Conv1D layer (a model parameters) to keep only entries in index. 		A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed. 		Return the pruned layer as a new layer with requires_grad=True. 		Used to remove heads. Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index. 		Return the pruned layer as a new layer with requires_grad=True. 		Used to remove heads. This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension `chunk_dim`. 	It then applies a layer `forward_fn` to each chunk independently to save memory. 	If the `forward_fn` is independent across the `chunk_dim` this function will yield the 	same result as not applying it.  	Args: 		chunk_size: int - the chunk size of a chunked tensor. `num_chunks` = `len(input_tensors[0]) / chunk_size` 		chunk_dim: int - the dimension over which the input_tensors should be chunked 		forward_fn: fn - the forward fn of the model 		input_tensors: tuple(torch.Tensor) - the input tensors of `forward_fn` which are chunked 	Returns: 		a Tensor with the same shape the foward_fn would have given if applied   	Examples::  		# rename the usual forward() fn to forward_chunk() 		def forward_chunk(self, hidden_states): 			hidden_states = self.decoder(hidden_states) 			return hidden_states  		# implement a chunked forward function 		def forward(self, hidden_states): 			return apply_chunking_to_forward(self.chunk_size_lm_head, self.seq_len_dim, self.forward_chunk, hidden_states) # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compability # chunk input tensor into tuples # apply forward fn to every tuple # concatenate output at same dimension # See all BERT models at https://huggingface.co/models?filter=bert Load tf checkpoints in a pytorch model. # Load weights from TF model # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model Initialize the weights # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 # BertLayerNorm = nn.LayerNorm #apex.amp.register_half_function(apex.normalization.fused_layer_norm, 'FusedLayerNorm') #apex.amp.register_half_function(apex.normalization.FusedLayerNorm, 'forward') #BertLayerNorm = nn.LayerNorm Construct the embeddings from word, position and token_type embeddings. # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file # 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. # Take the dot product between "query" and "key" to get the raw attention scores. # Apply the attention mask is (precomputed for all layers in BertModel forward() function) # Normalize the attention scores to probabilities. # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. # Mask heads if we want to # use fp32 for apex norm # with torch.cuda.amp.autocast(enabled=False): #hidden_states = self.LayerNorm(hidden_states.float() + input_tensor.float()) # Prune linear layers # Update hyper params and store pruned heads # add attentions if we output them # add self attentions if we output attention weights # add cross attentions if we output attention weights # Add last layer # last-layer hidden state, (all hidden states), (all attentions) # We "pool" the model by simply taking the hidden state corresponding # to the first token. # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` An abstract class to handle weights initialization and 		a simple interface for downloading and loading pretrained models. Initialize the weights # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. 	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general 	usage and behavior.  	Parameters: 		config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. Args: 		input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`): 			Indices of input sequence tokens in the vocabulary.  			Indices can be obtained using :class:`transformers.BertTokenizer`. 			See :func:`transformers.PreTrainedTokenizer.encode` and 			:func:`transformers.PreTrainedTokenizer.encode_plus` for details.  			`What are input IDs? <../glossary.html#input-ids>`__ 		attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`): 			Mask to avoid performing attention on padding token indices. 			Mask values selected in ``[0, 1]``: 			``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.  			`What are attention masks? <../glossary.html#attention-mask>`__ 		token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`): 			Segment token indices to indicate first and second portions of the inputs. 			Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1`` 			corresponds to a `sentence B` token  			`What are token type IDs? <../glossary.html#token-type-ids>`_ 		position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`): 			Indices of positions of each input sequence tokens in the position embeddings. 			Selected in the range ``[0, config.max_position_embeddings - 1]``.  			`What are position IDs? <../glossary.html#position-ids>`_ 		head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`): 			Mask to nullify selected heads of the self-attention modules. 			Mask values selected in ``[0, 1]``: 			:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**. 		inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`): 			Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. 			This is useful if you want more control over how to convert `input_ids` indices into associated vectors 			than the model's internal embedding lookup matrix. 		encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`): 			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 (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): 			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 MASKED tokens. 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`_ by <NAME>, 	<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME> and <NAME>.  	To behave as an decoder the model needs to be initialized with the 	:obj:`is_decoder` argument of the configuration set to :obj:`True`; an 	:obj:`encoder_hidden_states` is expected as an input to the forward pass.  	.. _`Attention is all you need`: 		https://arxiv.org/abs/1706.03762 Prunes heads of the model. 			heads_to_prune: dict of {layer_num: list of heads to prune in this layer} 			See base class PreTrainedModel Return: 		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: 		last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): 			Sequence of hidden-states at the output of the last layer of the model. 		pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`): 			Last layer hidden-state of the first token of the sequence (classification token) 			further processed by a Linear layer and a Tanh activation function. The Linear 			layer weights are trained from the next sentence prediction (classification) 			objective during pre-training.  			This output is usually *not* a good summary 			of the semantic content of the input, you're often better with averaging or pooling 			the sequence of hidden-states for the whole input sequence. 		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): 			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 			of shape :obj:`(batch_size, sequence_length, hidden_size)`.  			Hidden-states of the model at the output of each layer plus the initial embedding outputs. 		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): 			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape 			:obj:`(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.  	Examples::  		from transformers import BertModel, BertTokenizer 		import torch  		tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') 		model = BertModel.from_pretrained('bert-base-uncased')  		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1 		outputs = model(input_ids)  		last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple # 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. # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length] # 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] # add hidden_states and attentions if they are here # sequence_output, pooled_output, (hidden_states), (attentions) Bert Model with two heads on top as done during the pre-training: a `masked language modeling` head and 	a `next sentence prediction (classification)` head. labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`): 			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`, defaults to :obj:`None`): 			Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see :obj:`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 (:obj:`Dict[str, any]`, optional, defaults to `{}`): 			Used to hide legacy arguments that have been deprecated.  	Returns: 		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: 		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_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`) 			Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). 		seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`): 			Prediction scores of the next sequence prediction (classification) head (scores of True/False 			continuation before SoftMax). 		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): 			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 			of shape :obj:`(batch_size, sequence_length, hidden_size)`.  			Hidden-states of the model at the output of each layer plus the initial embedding outputs. 		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): 			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape 			:obj:`(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.   	Examples::  		from transformers import BertTokenizer, BertForPreTraining 		import torch  		tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') 		model = BertForPreTraining.from_pretrained('bert-base-uncased')  		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1 		outputs = model(input_ids)  		prediction_scores, seq_relationship_scores = outputs[:2] # add hidden states and attention if they are here # (loss), prediction_scores, seq_relationship_score, (hidden_states), (attentions) Bert Model with a `language modeling` head on top. labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): 			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]`` 		lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): 			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 			in ``[0, ..., config.vocab_size]`` 		kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`): 			Used to hide legacy arguments that have been deprecated.  	Returns: 		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: 		masked_lm_loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: 			Masked language modeling loss. 		ltr_lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`lm_labels` is provided): 				Next token prediction loss. 		prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`) 			Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). 		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): 			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 			of shape :obj:`(batch_size, sequence_length, hidden_size)`.  			Hidden-states of the model at the output of each layer plus the initial embedding outputs. 		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): 			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape 			:obj:`(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.  		Examples::  			from transformers import BertTokenizer, BertForMaskedLM 			import torch  			tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') 			model = BertForMaskedLM.from_pretrained('bert-base-uncased')  			input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1 			outputs = model(input_ids, labels=input_ids)  			loss, prediction_scores = outputs[:2] # Add hidden states and attention if they are here # Although this may seem awkward, BertForMaskedLM supports two scenarios: # 1. If a tensor that contains the indices of masked labels is provided, #    the cross-entropy is the MLM cross-entropy that measures the likelihood #    of predictions for masked words. # 2. If `lm_labels` is provided we are in a causal scenario where we #    try to predict the next token for each input in the decoder. # -100 index = padding token # we are doing next-token prediction; shift prediction scores and input ids by one # (ltr_lm_loss), (masked_lm_loss), prediction_scores, (hidden_states), (attentions) # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly # if model is does not use a causal mask then add a dummy token Bert Model with a `next sentence prediction (classification)` head on top. next_sentence_label (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): 			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: 		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: 		loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided): 			Next sequence prediction (classification) loss. 		seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`): 			Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). 		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): 			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 			of shape :obj:`(batch_size, sequence_length, hidden_size)`.  			Hidden-states of the model at the output of each layer plus the initial embedding outputs. 		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): 			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape 			:obj:`(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.  	Examples::  		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.encode_plus(prompt, next_sentence, return_tensors='pt')  		loss, logits = model(**encoding, next_sentence_label=torch.LongTensor([1])) 		assert logits[0, 0] < logits[0, 1] # next sentence was random # add hidden states and attention if they are here # (next_sentence_loss), seq_relationship_score, (hidden_states), (attentions)