latent-diffusion-text2im-large / modeling_ldmbert.py

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	# coding=utf-8
	# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. 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 LDMBERT model."""
	import copy
	import math
	import random
	import warnings
	from typing import List, Optional, Tuple, Union

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

	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import (
	BaseModelOutput,
	BaseModelOutputWithPastAndCrossAttentions,
	CausalLMOutputWithCrossAttentions,
	Seq2SeqLMOutput,
	Seq2SeqModelOutput,
	Seq2SeqQuestionAnsweringModelOutput,
	Seq2SeqSequenceClassifierOutput,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import (
	add_code_sample_docstrings,
	add_end_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from .configuration_ldmbert import LDMBertConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "ldm-bert"
	_CONFIG_FOR_DOC = "LDMBertConfig"
	_TOKENIZER_FOR_DOC = "BartTokenizer"

	# Base model docstring
	_EXPECTED_OUTPUT_SHAPE = [1, 8, 768]

	# SequenceClassification docstring
	_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "valhalla/ldmbert-large-sst2"
	_SEQ_CLASS_EXPECTED_LOSS = 0.0
	_SEQ_CLASS_EXPECTED_OUTPUT = "'POSITIVE'"

	# QuestionAsnwering docstring
	_CHECKPOINT_FOR_QA = "valhalla/ldmbert-large-finetuned-squadv1"
	_QA_EXPECTED_LOSS = 0.59
	_QA_EXPECTED_OUTPUT = "' nice puppet'"


	LDMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"ldm-bert",
	# See all LDMBert models at https://huggingface.co/models?filter=ldmbert
	]


	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

	return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


	# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->LDMBert
	class LDMBertAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(
	self,
	embed_dim: int,
	num_heads: int,
	head_dim: int,
	dropout: float = 0.0,
	is_decoder: bool = False,
	bias: bool = False,
	):
	super().__init__()
	self.embed_dim = embed_dim
	self.num_heads = num_heads
	self.dropout = dropout
	self.head_dim = head_dim
	self.inner_dim = head_dim * num_heads

	self.scaling = self.head_dim**-0.5
	self.is_decoder = is_decoder

	self.k_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
	self.v_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
	self.q_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
	self.out_proj = nn.Linear(self.inner_dim, embed_dim)

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	output_attentions: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	# if key_value_states are provided this layer is used as a cross-attention layer
	# for the decoder
	is_cross_attention = key_value_states is not None

	bsz, tgt_len, _ = hidden_states.size()

	# get query proj
	query_states = self.q_proj(hidden_states) * self.scaling
	# get key, value proj
	if is_cross_attention and past_key_value is not None:
	# reuse k,v, cross_attentions
	key_states = past_key_value[0]
	value_states = past_key_value[1]
	elif is_cross_attention:
	# cross_attentions
	key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
	value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
	elif past_key_value is not None:
	# reuse k, v, self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)
	else:
	# self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

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

	proj_shape = (bsz * self.num_heads, -1, self.head_dim)
	query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
	key_states = key_states.view(*proj_shape)
	value_states = value_states.view(*proj_shape)

	src_len = key_states.size(1)
	attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

	if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
	f" {attn_weights.size()}"
	)

	if attention_mask is not None:
	if attention_mask.size() != (bsz, 1, tgt_len, src_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
	)
	attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	attn_weights = nn.functional.softmax(attn_weights, dim=-1)

	if layer_head_mask is not None:
	if layer_head_mask.size() != (self.num_heads,):
	raise ValueError(
	f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
	f" {layer_head_mask.size()}"
	)
	attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	if output_attentions:
	# this operation is a bit awkward, but it's required to
	# make sure that attn_weights keeps its gradient.
	# In order to do so, attn_weights have to be reshaped
	# twice and have to be reused in the following
	attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
	attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
	else:
	attn_weights_reshaped = None

	attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

	attn_output = torch.bmm(attn_probs, value_states)

	if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

	attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
	attn_output = attn_output.transpose(1, 2)

	# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
	# partitioned aross GPUs when using tensor-parallelism.
	attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights_reshaped, past_key_value


	class LDMBertEncoderLayer(nn.Module):
	def __init__(self, config: LDMBertConfig):
	super().__init__()
	self.embed_dim = config.d_model
	self.self_attn = LDMBertAttention(
	embed_dim=self.embed_dim,
	num_heads=config.encoder_attention_heads,
	head_dim=config.head_dim,
	dropout=config.attention_dropout,
	)
	self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
	self.dropout = config.dropout
	self.activation_fn = ACT2FN[config.activation_function]
	self.activation_dropout = config.activation_dropout
	self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
	self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
	self.final_layer_norm = nn.LayerNorm(self.embed_dim)

	def forward(
	self,
	hidden_states: torch.FloatTensor,
	attention_mask: torch.FloatTensor,
	layer_head_mask: torch.FloatTensor,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
	attention_mask (`torch.FloatTensor`): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
	`(encoder_attention_heads,)`.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	residual = hidden_states
	hidden_states = self.self_attn_layer_norm(hidden_states)
	hidden_states, attn_weights, _ = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	residual = hidden_states
	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
	hidden_states = self.fc2(hidden_states)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	if hidden_states.dtype == torch.float16 and (
	torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
	):
	clamp_value = torch.finfo(hidden_states.dtype).max - 1000
	hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


	# Copied from transformers.models.bart.modeling_bart.BartPretrainedModel with Bart->LDMBert
	class LDMBertPreTrainedModel(PreTrainedModel):
	config_class = LDMBertConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"]

	def _init_weights(self, module):
	std = self.config.init_std
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()

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

	@property
	def dummy_inputs(self):
	pad_token = self.config.pad_token_id
	input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
	dummy_inputs = {
	"attention_mask": input_ids.ne(pad_token),
	"input_ids": input_ids,
	}
	return dummy_inputs


	LDMBERT_START_DOCSTRING = r"""
	This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)

	This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.

	Parameters:
	config ([`LDMBertConfig`]):
	Model configuration class with all the parameters of the model. Initializing with a config file does not
	load the weights associated with the model, only the configuration. Check out the
	[`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""

	LDMBERT_GENERATION_EXAMPLE = r"""
	Summarization example:

	```python
	>>> from transformers import BartTokenizer, LDMBertForConditionalGeneration

	>>> model = LDMBertForConditionalGeneration.from_pretrained("facebook/ldmbert-large-cnn")
	>>> tokenizer = BartTokenizer.from_pretrained("facebook/ldmbert-large-cnn")

	>>> ARTICLE_TO_SUMMARIZE = (
	... "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
	... "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
	... "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
	... )
	>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="pt")

	>>> # Generate Summary
	>>> summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=20)
	>>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	'PG&E scheduled the blackouts in response to forecasts for high winds amid dry conditions'
	```

	Mask filling example:

	```python
	>>> from transformers import BartTokenizer, LDMBertForConditionalGeneration

	>>> tokenizer = BartTokenizer.from_pretrained("ldm-bert")
	>>> model = LDMBertForConditionalGeneration.from_pretrained("ldm-bert")

	>>> TXT = "My friends are <mask> but they eat too many carbs."
	>>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
	>>> logits = model(input_ids).logits

	>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
	>>> probs = logits[0, masked_index].softmax(dim=0)
	>>> values, predictions = probs.topk(5)

	>>> tokenizer.decode(predictions).split()
	['not', 'good', 'healthy', 'great', 'very']
	```
	"""

	LDMBERT_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

	Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional):
	Indices of decoder input sequence tokens in the vocabulary.

	Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are decoder input IDs?](../glossary#decoder-input-ids)

	LDMBert uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
	`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
	`past_key_values`).

	For translation and summarization training, `decoder_input_ids` should be provided. If no
	`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
	for denoising pre-training following the paper.
	decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional):
	Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
	be used by default.

	If you want to change padding behavior, you should read
	[`modeling_ldmbert._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the
	paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

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

	decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

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

	cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
	1]`:

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

	encoder_outputs (`tuple(tuple(torch.FloatTensor)`, optional):
	Tuple consists of (`last_hidden_state`, optional: `hidden_states`, optional: `attentions`)
	`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, optional) is a sequence of
	hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
	`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
	`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
	`(batch_size, sequence_length, hidden_size)`, optional): Optionally, instead of passing `input_ids` you
	can choose to directly pass an embedded representation. This is useful if you want more control over how to
	convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
	decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
	representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
	input (see `past_key_values`). This is useful if you want more control over how to convert
	`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

	If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
	of `inputs_embeds`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""


	class LDMBertEncoder(LDMBertPreTrainedModel):
	"""
	Transformer encoder consisting of config.encoder_layers self attention layers. Each layer is a
	[`LDMBertEncoderLayer`].

	Args:
	config: LDMBertConfig
	embed_tokens (nn.Embedding): output embedding
	"""

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

	self.dropout = config.dropout

	embed_dim = config.d_model
	self.padding_idx = config.pad_token_id
	self.max_source_positions = config.max_position_embeddings

	self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim)
	self.embed_positions = nn.Embedding(config.max_position_embeddings, embed_dim)
	self.layers = nn.ModuleList([LDMBertEncoderLayer(config) for _ in range(config.encoder_layers)])
	self.layer_norm = nn.LayerNorm(embed_dim)

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

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, value):
	self.embed_tokens = value

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutput]:
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

	Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert `input_ids` indices into associated vectors
	than the model's internal embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# retrieve input_ids and inputs_embeds
	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()
	input_ids = input_ids.view(-1, input_shape[-1])
	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")

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids)

	seq_len = input_shape[1]
	if position_ids is None:
	position_ids = torch.arange(seq_len, dtype=torch.long, device=inputs_embeds.device).expand((1, -1))
	embed_pos = self.embed_positions(position_ids)

	hidden_states = inputs_embeds + embed_pos
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

	# expand attention_mask
	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)

	encoder_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	# check if head_mask has a correct number of layers specified if desired
	if head_mask is not None:
	if head_mask.size()[0] != (len(self.layers)):
	raise ValueError(
	f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
	f" {head_mask.size()[0]}."
	)

	for idx, encoder_layer in enumerate(self.layers):
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)
	if self.gradient_checkpointing and self.training:

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

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(encoder_layer),
	hidden_states,
	attention_mask,
	(head_mask[idx] if head_mask is not None else None),
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	layer_head_mask=(head_mask[idx] if head_mask is not None else None),
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

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

	hidden_states = self.layer_norm(hidden_states)

	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)

	if not return_dict:
	return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
	return BaseModelOutput(
	last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
	)


	class LDMBertModel(LDMBertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.model = LDMBertEncoder(config)
	self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)

	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):

	outputs = self.model(
	input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	sequence_output = outputs[0]
	# logits = self.to_logits(sequence_output)
	# outputs = (logits,) + outputs[1:]

	# if labels is not None:
	# loss_fct = CrossEntropyLoss()
	# loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
	# outputs = (loss,) + outputs

	# if not return_dict:
	# return outputs

	return BaseModelOutput(
	last_hidden_state=sequence_output,
	# hidden_states=outputs[1],
	# attentions=outputs[2],
	)