models/modeling_utils.py

# 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.
"""PyTorch BERT model."""

import logging
import os
from typing import Callable, 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
from .configuration_utils import PretrainedConfig
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


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:
		return next(self.parameters()).device

	@property
	def dtype(self) -> dtype:
		return next(self.parameters()).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
		encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
		return encoder_extended_attention_mask

	def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: tuple, device: device):
		"""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, num_hidden_layers):
		"""
		# 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)
		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.
			- ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values.
			- ``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
	pretrained_model_archive_map = {}
	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):
		"""
		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=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, new_num_tokens=None):
		""" 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:
			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.Embeddings``
			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):
		""" 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.
		"""
		assert os.path.isdir(
			save_directory
		), "Saving path should be a directory where the model and configuration can be saved"

		# 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)

		# 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 pretrained_model_name_or_path in cls.pretrained_model_archive_map:
				archive_file = cls.pretrained_model_archive_map[pretrained_model_name_or_path]
			elif 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, postfix=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME),
				)

			# redirect to the cache, if necessary
			try:
				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,
				)
			except EnvironmentError:
				if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
					msg = "Couldn't reach server at '{}' to download pretrained weights.".format(archive_file)
				else:
					msg = (
						"Model name '{}' was not found in model name list ({}). "
						"We assumed '{}' was a path or url to model weight files named one of {} but "
						"couldn't find any such file at this path or url.".format(
							pretrained_model_name_or_path,
							", ".join(cls.pretrained_model_archive_map.keys()),
							archive_file,
							[WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME],
						)
					)
				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(missing_keys) > 0:
				logger.info(
					"Weights of {} not initialized from pretrained model: {}".format(
						model.__class__.__name__, missing_keys
					)
				)
			if len(unexpected_keys) > 0:
				logger.info(
					"Weights from pretrained model not used in {}: {}".format(
						model.__class__.__name__, unexpected_keys
					)
				)
			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 = model.to(xm.xla_device())

		return model

	def prepare_inputs_for_generation(self, input_ids, **kwargs):
		return {"input_ids": input_ids}

	def prepare_scores_for_generation(self, scores, **kwargs):
		return scores

	def _use_cache(self, outputs, use_cache):
		"""During generation, decide whether to pass the `past` variable to the next forward pass."""
		if len(outputs) <= 1 or use_cache is False:
			return False
		if hasattr(self.config, "mem_len") and self.config.mem_len == 0:
			return False
		return True

	def enforce_repetition_penalty_(self, lprobs, batch_size, num_beams, prev_output_tokens, repetition_penalty):
		"""repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858). """
		for i in range(batch_size * num_beams):
			for previous_token in set(prev_output_tokens[i].tolist()):
				# if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
				if lprobs[i, previous_token] < 0:
					lprobs[i, previous_token] *= repetition_penalty
				else:
					lprobs[i, previous_token] /= repetition_penalty

	@torch.no_grad()
	def generate(
		self,
		input_ids=None,
		max_length=None,
		min_length=None,
		do_sample=None,
		early_stopping=None,
		num_beams=None,
		temperature=None,
		top_k=None,
		top_p=None,
		repetition_penalty=None,
		bad_words_ids=None,
		bos_token_id=None,
		pad_token_id=None,
		eos_token_id=None,
		length_penalty=None,
		no_repeat_ngram_size=None,
		num_return_sequences=None,
		attention_mask=None,
		decoder_start_token_id=None,
		use_cache=None,
	):
		r""" Generates sequences for models with a LM head. The method currently supports greedy decoding, beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.

		Adapted in part from `Facebook's XLM beam search code`_.

		.. _`Facebook's XLM beam search code`:
		   https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529


		Parameters:

			input_ids: (`optional`) `torch.LongTensor` of shape `(batch_size, sequence_length)`
				The sequence used as a prompt for the generation. If `None` the method initializes
				it as an empty `torch.LongTensor` of shape `(1,)`.

			max_length: (`optional`) int
				The max length of the sequence to be generated.  Between `min_length` and infinity. Default to 20.

			min_length: (`optional`) int
				The min length of the sequence to be generated.  Between 0 and infinity. Default to 0.

			do_sample: (`optional`) bool
				If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.

			early_stopping: (`optional`) bool
				if set to `True` beam search is stopped when at least `num_beams` sentences finished per batch. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.

			num_beams: (`optional`) int
				Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1.

			temperature: (`optional`) float
				The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.

			top_k: (`optional`) int
				The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.

			top_p: (`optional`) float
				The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.

			repetition_penalty: (`optional`) float
				The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0.

			pad_token_id: (`optional`) int
				Padding token. Default to specicic model pad_token_id or None if it does not exist.

			bos_token_id: (`optional`) int
				BOS token. Defaults to `bos_token_id` as defined in the models config.

			eos_token_id: (`optional`) int
				EOS token. Defaults to `eos_token_id` as defined in the models config.

			length_penalty: (`optional`) float
				Exponential penalty to the length. Default to 1.

			no_repeat_ngram_size: (`optional`) int
				If set to int > 0, all ngrams of size `no_repeat_ngram_size` can only occur once.
			bad_words_ids: (`optional`) list of lists of int
				`bad_words_ids` contains tokens that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.

			num_return_sequences: (`optional`) int
				The number of independently computed returned sequences for each element in the batch. Default to 1.

			attention_mask (`optional`) obj: `torch.LongTensor` of same shape as `input_ids`
				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.
				Defaults to `None`.

			`What are attention masks? <../glossary.html#attention-mask>`__

			decoder_start_token_id=None: (`optional`) int
				If an encoder-decoder model starts decoding with a different token than BOS.
				Defaults to `None` and is changed to `BOS` later.

			use_cache: (`optional`) bool
				If `use_cache` is True, past key values are used to speed up decoding if applicable to model. Defaults to `True`.

		Return:

			output: `torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`
				sequence_length is either equal to max_length or shorter if all batches finished early due to the `eos_token_id`

		Examples::

			tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
			model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
			outputs = model.generate(max_length=40)  # do greedy decoding
			print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))

			tokenizer = AutoTokenizer.from_pretrained('openai-gpt')   # Initialize tokenizer
			model = AutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from S3 and cache.
			input_context = 'The dog'
			input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
			outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)  # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
			for i in range(3): #  3 output sequences were generated
				print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

			tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
			model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
			input_context = 'The dog'
			input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
			outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3)  # 3 generate sequences using by sampling
			for i in range(3): #  3 output sequences were generated
				print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

			tokenizer = AutoTokenizer.from_pretrained('ctrl')   # Initialize tokenizer
			model = AutoModelWithLMHead.from_pretrained('ctrl')    # Download model and configuration from S3 and cache.
			input_context = 'Legal My neighbor is'  # "Legal" is one of the control codes for ctrl
			input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
			outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)  # generate sequences
			print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))

			tokenizer = AutoTokenizer.from_pretrained('gpt2')   # Initialize tokenizer
			model = AutoModelWithLMHead.from_pretrained('gpt2')    # Download model and configuration from S3 and cache.
			input_context = 'My cute dog'  # "Legal" is one of the control codes for ctrl
			bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
			input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
			outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)  # generate sequences without allowing bad_words to be generated
		"""

		# We cannot generate if the model does not have a LM head
		if self.get_output_embeddings() is None:
			raise AttributeError(
				"You tried to generate sequences with a model that does not have a LM Head."
				"Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )"
			)

		max_length = max_length if max_length is not None else self.config.max_length
		min_length = min_length if min_length is not None else self.config.min_length
		do_sample = do_sample if do_sample is not None else self.config.do_sample
		early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
		use_cache = use_cache if use_cache is not None else self.config.use_cache
		num_beams = num_beams if num_beams is not None else self.config.num_beams
		temperature = temperature if temperature is not None else self.config.temperature
		top_k = top_k if top_k is not None else self.config.top_k
		top_p = top_p if top_p is not None else self.config.top_p
		repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
		bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
		pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
		eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
		length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
		no_repeat_ngram_size = (
			no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
		)
		bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
		num_return_sequences = (
			num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
		)
		decoder_start_token_id = (
			decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
		)

		if input_ids is not None:
			batch_size = input_ids.shape[0]  # overriden by the input batch_size
		else:
			batch_size = 1

		assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
		assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
		assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
		assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
		assert isinstance(use_cache, bool), "`use_cache` should be a boolean."
		assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
		assert temperature > 0, "`temperature` should be strictly positive."
		assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
		assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
		assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
		assert input_ids is not None or (
			isinstance(bos_token_id, int) and bos_token_id >= 0
		), "If input_ids is not defined, `bos_token_id` should be a positive integer."
		assert pad_token_id is None or (
			isinstance(pad_token_id, int) and (pad_token_id >= 0)
		), "`pad_token_id` should be a positive integer."
		assert (eos_token_id is None) or (
			isinstance(eos_token_id, int) and (eos_token_id >= 0)
		), "`eos_token_id` should be a positive integer."
		assert length_penalty > 0, "`length_penalty` should be strictly positive."
		assert (
			isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0
		), "`no_repeat_ngram_size` should be a positive integer."
		assert (
			isinstance(num_return_sequences, int) and num_return_sequences > 0
		), "`num_return_sequences` should be a strictly positive integer."
		assert (
			bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
		), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"

		if input_ids is None:
			assert isinstance(bos_token_id, int) and bos_token_id >= 0, (
				"you should either supply a context to complete as `input_ids` input "
				"or a `bos_token_id` (integer >= 0) as a first token to start the generation."
			)
			input_ids = torch.full(
				(batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device,
			)
		else:
			assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)."

		# not allow to duplicate outputs when greedy decoding
		if do_sample is False:
			if num_beams == 1:
				# no_beam_search greedy generation conditions
				assert (
					num_return_sequences == 1
				), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1"

			else:
				# beam_search greedy generation conditions
				assert (
					num_beams >= num_return_sequences
				), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences"

		# create attention mask if necessary
		# TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140
		if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids):
			attention_mask = input_ids.ne(pad_token_id).long()
		elif attention_mask is None:
			attention_mask = input_ids.new_ones(input_ids.shape)

		# set pad_token_id to eos_token_id if not set. Important that this is done after
		# attention_mask is created
		if pad_token_id is None and eos_token_id is not None:
			logger.warning(
				"Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id)
			)
			pad_token_id = eos_token_id

		# current position and vocab size
		if hasattr(self.config, "vocab_size"):
			vocab_size = self.config.vocab_size
		elif (
			self.config.is_encoder_decoder
			and hasattr(self.config, "decoder")
			and hasattr(self.config.decoder, "vocab_size")
		):
			vocab_size = self.config.decoder.vocab_size

		# set effective batch size and effective batch multiplier according to do_sample
		if do_sample:
			effective_batch_size = batch_size * num_return_sequences
			effective_batch_mult = num_return_sequences
		else:
			effective_batch_size = batch_size
			effective_batch_mult = 1

		if self.config.is_encoder_decoder:
			if decoder_start_token_id is None:
				decoder_start_token_id = bos_token_id

			assert (
				decoder_start_token_id is not None
			), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
			assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
			assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)

			# get encoder and store encoder outputs
			encoder = self.get_encoder()

			encoder_outputs: tuple = encoder(input_ids, attention_mask=attention_mask)

		# Expand input ids if num_beams > 1 or num_return_sequences > 1
		if num_return_sequences > 1 or num_beams > 1:
			input_ids_len = input_ids.shape[-1]
			input_ids = input_ids.unsqueeze(1).expand(batch_size, effective_batch_mult * num_beams, input_ids_len)
			attention_mask = attention_mask.unsqueeze(1).expand(
				batch_size, effective_batch_mult * num_beams, input_ids_len
			)

			input_ids = input_ids.contiguous().view(
				effective_batch_size * num_beams, input_ids_len
			)  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
			attention_mask = attention_mask.contiguous().view(
				effective_batch_size * num_beams, input_ids_len
			)  # shape: (batch_size * num_return_sequences * num_beams, cur_len)

		if self.config.is_encoder_decoder:
			# create empty decoder_input_ids
			input_ids = torch.full(
				(effective_batch_size * num_beams, 1),
				decoder_start_token_id,
				dtype=torch.long,
				device=next(self.parameters()).device,
			)
			cur_len = 1

			assert (
				batch_size == encoder_outputs[0].shape[0]
			), f"expected encoder_outputs[0] to have 1st dimension bs={batch_size}, got {encoder_outputs[0].shape[0]} "

			# expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1)
			expanded_batch_idxs = (
				torch.arange(batch_size)
				.view(-1, 1)
				.repeat(1, num_beams * effective_batch_mult)
				.view(-1)
				.to(input_ids.device)
			)
			# expand encoder_outputs
			encoder_outputs = (encoder_outputs[0].index_select(0, expanded_batch_idxs), *encoder_outputs[1:])

		else:
			encoder_outputs = None
			cur_len = input_ids.shape[-1]

		if num_beams > 1:
			output = self._generate_beam_search(
				input_ids,
				cur_len=cur_len,
				max_length=max_length,
				min_length=min_length,
				do_sample=do_sample,
				early_stopping=early_stopping,
				temperature=temperature,
				top_k=top_k,
				top_p=top_p,
				repetition_penalty=repetition_penalty,
				no_repeat_ngram_size=no_repeat_ngram_size,
				bad_words_ids=bad_words_ids,
				bos_token_id=bos_token_id,
				pad_token_id=pad_token_id,
				decoder_start_token_id=decoder_start_token_id,
				eos_token_id=eos_token_id,
				batch_size=effective_batch_size,
				num_return_sequences=num_return_sequences,
				length_penalty=length_penalty,
				num_beams=num_beams,
				vocab_size=vocab_size,
				encoder_outputs=encoder_outputs,
				attention_mask=attention_mask,
				use_cache=use_cache,
			)
		else:
			output = self._generate_no_beam_search(
				input_ids,
				cur_len=cur_len,
				max_length=max_length,
				min_length=min_length,
				do_sample=do_sample,
				temperature=temperature,
				top_k=top_k,
				top_p=top_p,
				repetition_penalty=repetition_penalty,
				no_repeat_ngram_size=no_repeat_ngram_size,
				bad_words_ids=bad_words_ids,
				bos_token_id=bos_token_id,
				pad_token_id=pad_token_id,
				decoder_start_token_id=decoder_start_token_id,
				eos_token_id=eos_token_id,
				batch_size=effective_batch_size,
				encoder_outputs=encoder_outputs,
				attention_mask=attention_mask,
				use_cache=use_cache,
			)

		return output

	def _generate_no_beam_search(
		self,
		input_ids,
		cur_len,
		max_length,
		min_length,
		do_sample,
		temperature,
		top_k,
		top_p,
		repetition_penalty,
		no_repeat_ngram_size,
		bad_words_ids,
		bos_token_id,
		pad_token_id,
		eos_token_id,
		decoder_start_token_id,
		batch_size,
		encoder_outputs,
		attention_mask,
		use_cache,
	):
		""" Generate sequences for each example without beam search (num_beams == 1).
			All returned sequence are generated independantly.
		"""
		# length of generated sentences / unfinished sentences
		unfinished_sents = input_ids.new(batch_size).fill_(1)
		sent_lengths = input_ids.new(batch_size).fill_(max_length)

		past = encoder_outputs  # defined for encoder-decoder models, None for decoder-only models

		while cur_len < max_length:
			model_inputs = self.prepare_inputs_for_generation(
				input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache
			)

			outputs = self(**model_inputs)
			next_token_logits = outputs[0][:, -1, :]

			# if model has past, then set the past variable to speed up decoding
			if self._use_cache(outputs, use_cache):
				past = outputs[1]

			# repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
			if repetition_penalty != 1.0:
				self.enforce_repetition_penalty_(next_token_logits, batch_size, 1, input_ids, repetition_penalty)

			if no_repeat_ngram_size > 0:
				# calculate a list of banned tokens to prevent repetitively generating the same ngrams
				# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
				banned_tokens = calc_banned_ngram_tokens(input_ids, batch_size, no_repeat_ngram_size, cur_len)
				for batch_idx in range(batch_size):
					next_token_logits[batch_idx, banned_tokens[batch_idx]] = -float("inf")

			if bad_words_ids is not None:
				# calculate a list of banned tokens according to bad words
				banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)

				for batch_idx in range(batch_size):
					next_token_logits[batch_idx, banned_tokens[batch_idx]] = -float("inf")

			# set eos token prob to zero if min_length is not reached
			if eos_token_id is not None and cur_len < min_length:
				next_token_logits[:, eos_token_id] = -float("inf")

			if do_sample:
				# Temperature (higher temperature => more likely to sample low probability tokens)
				if temperature != 1.0:
					next_token_logits = next_token_logits / temperature
				# Top-p/top-k filtering
				next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
				# Sample
				probs = F.softmax(next_token_logits, dim=-1)
				next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
			else:
				# Greedy decoding
				next_token = torch.argmax(next_token_logits, dim=-1)

			# update generations and finished sentences
			if eos_token_id is not None:
				# pad finished sentences if eos_token_id exist
				tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents)
			else:
				tokens_to_add = next_token

			input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1)

			if eos_token_id is not None:
				eos_in_sents = tokens_to_add == eos_token_id
				# if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length
				is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool()
				sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len + 1)
				# unfinished_sents is set to zero if eos in sentence
				unfinished_sents.mul_((~eos_in_sents).long())

			# stop when there is a </s> in each sentence, or if we exceed the maximul length
			if unfinished_sents.max() == 0:
				break

			# extend attention_mask for new generated input if only decoder
			if self.config.is_encoder_decoder is False:
				attention_mask = torch.cat(
					[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
				)

			cur_len = cur_len + 1

		# if there are different sentences lengths in the batch, some batches have to be padded
		if sent_lengths.min().item() != sent_lengths.max().item():
			assert pad_token_id is not None, "`Pad_token_id` has to be defined if batches have different lengths"
			# finished sents are filled with pad_token
			decoded = input_ids.new(batch_size, sent_lengths.max().item()).fill_(pad_token_id)
		else:
			decoded = input_ids

		for hypo_idx, hypo in enumerate(input_ids):
			decoded[hypo_idx, : sent_lengths[hypo_idx]] = hypo[: sent_lengths[hypo_idx]]

		return decoded

	def _generate_beam_search(
		self,
		input_ids,
		cur_len,
		max_length,
		min_length,
		do_sample,
		early_stopping,
		temperature,
		top_k,
		top_p,
		repetition_penalty,
		no_repeat_ngram_size,
		bad_words_ids,
		bos_token_id,
		pad_token_id,
		eos_token_id,
		decoder_start_token_id,
		batch_size,
		num_return_sequences,
		length_penalty,
		num_beams,
		vocab_size,
		encoder_outputs,
		attention_mask,
		use_cache,
	):
		""" Generate sequences for each example with beam search.
		"""

		# generated hypotheses
		generated_hyps = [
			BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
			for _ in range(batch_size)
		]

		# scores for each sentence in the beam
		beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)

		# for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
		if do_sample is False:
			beam_scores[:, 1:] = -1e9
		beam_scores = beam_scores.view(-1)  # shape (batch_size * num_beams,)

		# cache compute states
		past = encoder_outputs  # defined for encoder-decoder models, None for decoder-only models

		# done sentences
		done = [False for _ in range(batch_size)]

		while cur_len < max_length:
			model_inputs = self.prepare_inputs_for_generation(
				input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache
			)
			outputs = self(**model_inputs)  # (batch_size * num_beams, cur_len, vocab_size)
			next_token_logits = outputs[0][:, -1, :]  # (batch_size * num_beams, vocab_size)

			# if model has past, then set the past variable to speed up decoding
			if self._use_cache(outputs, use_cache):
				past = outputs[1]

			# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
			if repetition_penalty != 1.0:
				self.enforce_repetition_penalty_(
					next_token_logits, batch_size, num_beams, input_ids, repetition_penalty,
				)

			if temperature != 1.0:
				next_token_logits = next_token_logits / temperature

			scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
			if self.config.is_encoder_decoder and do_sample is False:
				# TODO (PVP) still a bit hacky here - there might be a better solutino
				scores = self.prepare_scores_for_generation(scores, cur_len=cur_len, max_length=max_length)

			# set eos token prob to zero if min_length is not reached
			if eos_token_id is not None and cur_len < min_length:
				scores[:, eos_token_id] = -float("inf")

			if no_repeat_ngram_size > 0:
				# calculate a list of banned tokens to prevent repetitively generating the same ngrams
				num_batch_hypotheses = batch_size * num_beams
				# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
				banned_batch_tokens = calc_banned_ngram_tokens(
					input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len
				)
				for i, banned_tokens in enumerate(banned_batch_tokens):
					scores[i, banned_tokens] = -float("inf")

			if bad_words_ids is not None:
				# calculate a list of banned tokens according to bad words
				banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)

				for i, banned_tokens in enumerate(banned_tokens):
					scores[i, banned_tokens] = -float("inf")

			assert scores.shape == (batch_size * num_beams, vocab_size), "Shapes of scores: {} != {}".format(
				scores.shape, (batch_size * num_beams, vocab_size)
			)

			if do_sample:
				_scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
				# Top-p/top-k filtering
				_scores = top_k_top_p_filtering(
					_scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
				)  # (batch_size * num_beams, vocab_size)
				# re-organize to group the beam together to sample from all beam_idxs
				_scores = _scores.contiguous().view(
					batch_size, num_beams * vocab_size
				)  # (batch_size, num_beams * vocab_size)

				# Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
				probs = F.softmax(_scores, dim=-1)
				next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)  # (batch_size, num_beams * 2)
				# Compute next scores
				next_scores = torch.gather(_scores, -1, next_tokens)  # (batch_size, num_beams * 2)
				# sort the sampled vector to make sure that the first num_beams samples are the best
				next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1)
				next_tokens = torch.gather(next_tokens, -1, next_scores_indices)  # (batch_size, num_beams * 2)

			else:
				next_scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)

				# re-organize to group the beam together (we are keeping top hypothesis accross beams)
				next_scores = next_scores.view(
					batch_size, num_beams * vocab_size
				)  # (batch_size, num_beams * vocab_size)

				next_scores, next_tokens = torch.topk(next_scores, 2 * num_beams, dim=1, largest=True, sorted=True)

			assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams)

			# next batch beam content
			next_batch_beam = []

			# for each sentence
			for batch_idx in range(batch_size):

				# if we are done with this sentence
				if done[batch_idx]:
					assert (
						len(generated_hyps[batch_idx]) >= num_beams
					), "Batch can only be done if at least {} beams have been generated".format(num_beams)
					assert (
						eos_token_id is not None and pad_token_id is not None
					), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
					next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams)  # pad the batch
					continue

				# next sentence beam content
				next_sent_beam = []

				# next tokens for this sentence
				for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
					zip(next_tokens[batch_idx], next_scores[batch_idx])
				):
					# get beam and token IDs
					beam_id = beam_token_id // vocab_size
					token_id = beam_token_id % vocab_size

					effective_beam_id = batch_idx * num_beams + beam_id
					# add to generated hypotheses if end of sentence or last iteration
					if (eos_token_id is not None) and (token_id.item() == eos_token_id):
						# if beam_token does not belong to top num_beams tokens, it should not be added
						is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
						if is_beam_token_worse_than_top_num_beams:
							continue
						generated_hyps[batch_idx].add(
							input_ids[effective_beam_id].clone(), beam_token_score.item(),
						)
					else:
						# add next predicted token if it is not eos_token
						next_sent_beam.append((beam_token_score, token_id, effective_beam_id))

					# the beam for next step is full
					if len(next_sent_beam) == num_beams:
						break

				# Check if were done so that we can save a pad step if all(done)
				done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
					next_scores[batch_idx].max().item(), cur_len=cur_len
				)

				# update next beam content
				assert len(next_sent_beam) == num_beams, "Beam should always be full"
				next_batch_beam.extend(next_sent_beam)
				assert len(next_batch_beam) == num_beams * (batch_idx + 1)

			# stop when we are done with each sentence
			if all(done):
				break

			# sanity check / prepare next batch
			assert len(next_batch_beam) == batch_size * num_beams
			beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
			beam_tokens = input_ids.new([x[1] for x in next_batch_beam])
			beam_idx = input_ids.new([x[2] for x in next_batch_beam])

			# re-order batch
			input_ids = input_ids[beam_idx, :]
			input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1)
			# re-order internal states
			if past is not None:
				past = self._reorder_cache(past, beam_idx)

			# extend attention_mask for new generated input if only decoder
			if self.config.is_encoder_decoder is False:
				attention_mask = torch.cat(
					[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
				)

			# update current length
			cur_len = cur_len + 1

		# finalize all open beam hypotheses and end to generated hypotheses
		for batch_idx in range(batch_size):
			if done[batch_idx]:
				continue

			# test that beam scores match previously calculated scores if not eos and batch_idx not done
			if eos_token_id is not None and all(
				(token_id % vocab_size).item() is not eos_token_id for token_id in next_tokens[batch_idx]
			):
				assert torch.all(
					next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx]
				), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
					next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx],
				)

			# need to add best num_beams hypotheses to generated hyps
			for beam_id in range(num_beams):
				effective_beam_id = batch_idx * num_beams + beam_id
				final_score = beam_scores[effective_beam_id].item()
				final_tokens = input_ids[effective_beam_id]
				generated_hyps[batch_idx].add(final_tokens, final_score)

		# depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
		output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
		output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences

		# select the best hypotheses
		sent_lengths = input_ids.new(output_batch_size)
		best = []

		# retrieve best hypotheses
		for i, hypotheses in enumerate(generated_hyps):
			sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])
			for j in range(output_num_return_sequences_per_batch):
				effective_batch_idx = output_num_return_sequences_per_batch * i + j
				best_hyp = sorted_hyps.pop()[1]
				sent_lengths[effective_batch_idx] = len(best_hyp)
				best.append(best_hyp)

		# shorter batches are filled with pad_token
		if sent_lengths.min().item() != sent_lengths.max().item():
			assert pad_token_id is not None, "`Pad_token_id` has to be defined"
			sent_max_len = min(sent_lengths.max().item() + 1, max_length)
			decoded = input_ids.new(output_batch_size, sent_max_len).fill_(pad_token_id)

			# fill with hypothesis and eos_token_id if necessary
			for i, hypo in enumerate(best):
				decoded[i, : sent_lengths[i]] = hypo
				if sent_lengths[i] < max_length:
					decoded[i, sent_lengths[i]] = eos_token_id
		else:
			# none of the hypotheses have an eos_token
			assert (len(hypo) == max_length for hypo in best)
			decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device)

		return decoded

	# force one of token_ids to be generated by setting prob of all other tokens to 0.
	def _force_token_ids_generation(self, scores, token_ids) -> None:
		if isinstance(token_ids, int):
			token_ids = [token_ids]
		all_but_token_ids_mask = torch.tensor(
			[x for x in range(self.config.vocab_size) if x not in token_ids],
			dtype=torch.long,
			device=next(self.parameters()).device,
		)
		assert len(scores.shape) == 2, "scores should be of rank 2 with shape: [batch_size, vocab_size]"
		scores[:, all_but_token_ids_mask] = -float("inf")

	@staticmethod
	def _reorder_cache(past: Tuple, beam_idx: Tensor) -> Tuple[Tensor]:
		return tuple(layer_past.index_select(1, beam_idx) for layer_past in past)


def calc_banned_ngram_tokens(prev_input_ids: Tensor, num_hypos: int, no_repeat_ngram_size: int, cur_len: int) -> None:
	"""Copied from fairseq for no_repeat_ngram in beam_search"""
	if cur_len + 1 < no_repeat_ngram_size:
		# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
		return [[] for _ in range(num_hypos)]
	generated_ngrams = [{} for _ in range(num_hypos)]
	for idx in range(num_hypos):
		gen_tokens = prev_input_ids[idx].tolist()
		generated_ngram = generated_ngrams[idx]
		for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
			prev_ngram_tuple = tuple(ngram[:-1])
			generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]

	def _get_generated_ngrams(hypo_idx):
		# Before decoding the next token, prevent decoding of ngrams that have already appeared
		start_idx = cur_len + 1 - no_repeat_ngram_size
		ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].tolist())
		return generated_ngrams[hypo_idx].get(ngram_idx, [])

	banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
	return banned_tokens


def calc_banned_bad_words_ids(prev_input_ids, bad_words_ids):
	banned_tokens = []

	def _tokens_match(prev_tokens, tokens):
		if len(tokens) == 0:
			# if bad word tokens is just one token always ban it
			return True
		if len(tokens) > len(prev_input_ids):
			# if bad word tokens are longer then prev input_ids they can't be equal
			return False

		if prev_tokens[-len(tokens) :] == tokens:
			# if tokens match
			return True
		else:
			return False

	for prev_input_ids_slice in prev_input_ids:
		banned_tokens_slice = []

		for banned_token_seq in bad_words_ids:
			assert len(banned_token_seq) > 0, "Banned words token sequences {} cannot have an empty list".format(
				bad_words_ids
			)

			if _tokens_match(prev_input_ids_slice.tolist(), banned_token_seq[:-1]) is False:
				# if tokens do not match continue
				continue

			banned_tokens_slice.append(banned_token_seq[-1])

		banned_tokens.append(banned_tokens_slice)

	return banned_tokens


def top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
	""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
		Args:
			logits: logits distribution shape (batch size, vocabulary size)
			if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
			if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
				Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
			Make sure we keep at least min_tokens_to_keep per batch example in the output
		From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
	"""
	if top_k > 0:
		top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))  # Safety check
		# Remove all tokens with a probability less than the last token of the top-k
		indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
		logits[indices_to_remove] = filter_value

	if top_p < 1.0:
		sorted_logits, sorted_indices = torch.sort(logits, descending=True)
		cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)

		# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
		sorted_indices_to_remove = cumulative_probs > top_p
		if min_tokens_to_keep > 1:
			# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
			sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
		# Shift the indices to the right to keep also the first token above the threshold
		sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
		sorted_indices_to_remove[..., 0] = 0

		# scatter sorted tensors to original indexing
		indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
		logits[indices_to_remove] = filter_value
	return logits


class BeamHypotheses(object):
	def __init__(self, num_beams, max_length, length_penalty, early_stopping):
		"""
		Initialize n-best list of hypotheses.
		"""
		self.max_length = max_length - 1  # ignoring bos_token
		self.length_penalty = length_penalty
		self.early_stopping = early_stopping
		self.num_beams = num_beams
		self.beams = []
		self.worst_score = 1e9

	def __len__(self):
		"""
		Number of hypotheses in the list.
		"""
		return len(self.beams)

	def add(self, hyp, sum_logprobs):
		"""
		Add a new hypothesis to the list.
		"""
		score = sum_logprobs / len(hyp) ** self.length_penalty
		if len(self) < self.num_beams or score > self.worst_score:
			self.beams.append((score, hyp))
			if len(self) > self.num_beams:
				sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])
				del self.beams[sorted_scores[0][1]]
				self.worst_score = sorted_scores[1][0]
			else:
				self.worst_score = min(score, self.worst_score)

	def is_done(self, best_sum_logprobs, cur_len=None):
		"""
		If there are enough hypotheses and that none of the hypotheses being generated
		can become better than the worst one in the heap, then we are done with this sentence.
		"""

		if len(self) < self.num_beams:
			return False
		elif self.early_stopping:
			return True
		else:
			if cur_len is None:
				cur_len = self.max_length
			cur_score = best_sum_logprobs / cur_len ** self.length_penalty
			ret = self.worst_score >= cur_score
			return ret


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 PoolerStartLogits(nn.Module):
	""" Compute SQuAD start_logits from sequence hidden states. """

	def __init__(self, config):
		super().__init__()
		self.dense = nn.Linear(config.hidden_size, 1)

	def forward(self, hidden_states, p_mask=None):
		""" Args:
			**p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)`
				invalid position mask such as query and special symbols (PAD, SEP, CLS)
				1.0 means token should be masked.
		"""
		x = self.dense(hidden_states).squeeze(-1)

		if p_mask is not None:
			if next(self.parameters()).dtype == torch.float16:
				x = x * (1 - p_mask) - 65500 * p_mask
			else:
				x = x * (1 - p_mask) - 1e30 * p_mask

		return x


class PoolerEndLogits(nn.Module):
	""" Compute SQuAD end_logits from sequence hidden states and start token hidden state.
	"""

	def __init__(self, config):
		super().__init__()
		self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
		self.activation = nn.Tanh()
		self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
		self.dense_1 = nn.Linear(config.hidden_size, 1)

	def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None):
		""" Args:
			One of ``start_states``, ``start_positions`` should be not None.
			If both are set, ``start_positions`` overrides ``start_states``.

			**start_states**: ``torch.LongTensor`` of shape identical to hidden_states
				hidden states of the first tokens for the labeled span.
			**start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
				position of the first token for the labeled span:
			**p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
				Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
				1.0 means token should be masked.
		"""
		assert (
			start_states is not None or start_positions is not None
		), "One of start_states, start_positions should be not None"
		if start_positions is not None:
			slen, hsz = hidden_states.shape[-2:]
			start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
			start_states = hidden_states.gather(-2, start_positions)  # shape (bsz, 1, hsz)
			start_states = start_states.expand(-1, slen, -1)  # shape (bsz, slen, hsz)

		x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))
		x = self.activation(x)
		x = self.LayerNorm(x)
		x = self.dense_1(x).squeeze(-1)

		if p_mask is not None:
			if next(self.parameters()).dtype == torch.float16:
				x = x * (1 - p_mask) - 65500 * p_mask
			else:
				x = x * (1 - p_mask) - 1e30 * p_mask

		return x


class PoolerAnswerClass(nn.Module):
	""" Compute SQuAD 2.0 answer class from classification and start tokens hidden states. """

	def __init__(self, config):
		super().__init__()
		self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
		self.activation = nn.Tanh()
		self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)

	def forward(self, hidden_states, start_states=None, start_positions=None, cls_index=None):
		"""
		Args:
			One of ``start_states``, ``start_positions`` should be not None.
			If both are set, ``start_positions`` overrides ``start_states``.

			**start_states**: ``torch.LongTensor`` of shape identical to ``hidden_states``.
				hidden states of the first tokens for the labeled span.
			**start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
				position of the first token for the labeled span.
			**cls_index**: torch.LongTensor of shape ``(batch_size,)``
				position of the CLS token. If None, take the last token.

			note(Original repo):
				no dependency on end_feature so that we can obtain one single `cls_logits`
				for each sample
		"""
		hsz = hidden_states.shape[-1]
		assert (
			start_states is not None or start_positions is not None
		), "One of start_states, start_positions should be not None"
		if start_positions is not None:
			start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
			start_states = hidden_states.gather(-2, start_positions).squeeze(-2)  # shape (bsz, hsz)

		if cls_index is not None:
			cls_index = cls_index[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
			cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, hsz)
		else:
			cls_token_state = hidden_states[:, -1, :]  # shape (bsz, hsz)

		x = self.dense_0(torch.cat([start_states, cls_token_state], dim=-1))
		x = self.activation(x)
		x = self.dense_1(x).squeeze(-1)

		return x


class SQuADHead(nn.Module):
	r""" A SQuAD head inspired by XLNet.

	Parameters:
		config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model.

	Inputs:
		**hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)``
			hidden states of sequence tokens
		**start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
			position of the first token for the labeled span.
		**end_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
			position of the last token for the labeled span.
		**cls_index**: torch.LongTensor of shape ``(batch_size,)``
			position of the CLS token. If None, take the last token.
		**is_impossible**: ``torch.LongTensor`` of shape ``(batch_size,)``
			Whether the question has a possible answer in the paragraph or not.
		**p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
			Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
			1.0 means token should be masked.

	Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
		**loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
			Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
		**start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
			``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
			Log probabilities for the top config.start_n_top start token possibilities (beam-search).
		**start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
			``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
			Indices for the top config.start_n_top start token possibilities (beam-search).
		**end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
			``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
			Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
		**end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
			``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
			Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
		**cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
			``torch.FloatTensor`` of shape ``(batch_size,)``
			Log probabilities for the ``is_impossible`` label of the answers.
	"""

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

		self.start_logits = PoolerStartLogits(config)
		self.end_logits = PoolerEndLogits(config)
		self.answer_class = PoolerAnswerClass(config)

	def forward(
		self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None,
	):
		outputs = ()

		start_logits = self.start_logits(hidden_states, p_mask=p_mask)

		if start_positions is not None and end_positions is not None:
			# If we are on multi-GPU, let's remove the dimension added by batch splitting
			for x in (start_positions, end_positions, cls_index, is_impossible):
				if x is not None and x.dim() > 1:
					x.squeeze_(-1)

			# during training, compute the end logits based on the ground truth of the start position
			end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)

			loss_fct = CrossEntropyLoss()
			start_loss = loss_fct(start_logits, start_positions)
			end_loss = loss_fct(end_logits, end_positions)
			total_loss = (start_loss + end_loss) / 2

			if cls_index is not None and is_impossible is not None:
				# Predict answerability from the representation of CLS and START
				cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
				loss_fct_cls = nn.BCEWithLogitsLoss()
				cls_loss = loss_fct_cls(cls_logits, is_impossible)

				# note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
				total_loss += cls_loss * 0.5

			outputs = (total_loss,) + outputs

		else:
			# during inference, compute the end logits based on beam search
			bsz, slen, hsz = hidden_states.size()
			start_log_probs = F.softmax(start_logits, dim=-1)  # shape (bsz, slen)

			start_top_log_probs, start_top_index = torch.topk(
				start_log_probs, self.start_n_top, dim=-1
			)  # shape (bsz, start_n_top)
			start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz)  # shape (bsz, start_n_top, hsz)
			start_states = torch.gather(hidden_states, -2, start_top_index_exp)  # shape (bsz, start_n_top, hsz)
			start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1)  # shape (bsz, slen, start_n_top, hsz)

			hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(
				start_states
			)  # shape (bsz, slen, start_n_top, hsz)
			p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
			end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
			end_log_probs = F.softmax(end_logits, dim=1)  # shape (bsz, slen, start_n_top)

			end_top_log_probs, end_top_index = torch.topk(
				end_log_probs, self.end_n_top, dim=1
			)  # shape (bsz, end_n_top, start_n_top)
			end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
			end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)

			start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
			cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)

			outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits,) + outputs

		# return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
		# or (if labels are provided) (total_loss,)
		return outputs


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__))