ChineseBERT-base / modeling_glycebert.py

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	#!/usr/bin/env python
	# -- coding: utf-8 --
	"""
	@file : modeling_glycebert.py
	@author: zijun
	@contact : zijun_sun@shannonai.com
	@date : 2020/9/6 18:50
	@version: 1.0
	@desc : ChineseBert Model
	"""
	import json
	import os
	import shutil
	import time
	import warnings
	from pathlib import Path
	from typing import List

	import numpy as np
	import torch
	from huggingface_hub import hf_hub_download
	from huggingface_hub.file_download import http_user_agent
	from torch import nn
	from torch.nn import CrossEntropyLoss, MSELoss
	from torch.nn import functional as F

	try:
	from transformers.modeling_bert import BertEncoder, BertPooler, BertOnlyMLMHead, BertPreTrainedModel, BertModel
	except:
	from transformers.models.bert.modeling_bert import BertEncoder, BertPooler, BertOnlyMLMHead, BertPreTrainedModel, \
	BertModel

	from transformers.modeling_outputs import BaseModelOutputWithPooling, MaskedLMOutput, SequenceClassifierOutput, \
	QuestionAnsweringModelOutput, TokenClassifierOutput

	cache_path = Path(os.path.abspath(__file__)).parent


	def download_file(filename: str, path: Path):
	if os.path.exists(cache_path / filename):
	return

	if os.path.exists(path / filename):
	shutil.copyfile(path / filename, cache_path / filename)
	return

	hf_hub_download(
	"iioSnail/ChineseBERT-base",
	filename,
	local_dir=cache_path,
	user_agent=http_user_agent(),
	)
	time.sleep(0.2)


	class GlyceBertModel(BertModel):
	r"""
	Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
	last_hidden_state: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
	Sequence of hidden-states at the output of the last layer of the models.
	pooler_output: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
	Last layer hidden-state of the first token of the sequence (classification token)
	further processed by a Linear layer and a Tanh activation function. The Linear
	layer weights are trained from the next sentence prediction (classification)
	objective during Bert pretraining. This output is usually not a good summary
	of the semantic content of the input, you're often better with averaging or pooling
	the sequence of hidden-states for the whole input sequence.
	hidden_states: (`optional`, returned when ``config.output_hidden_states=True``)
	list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
	of shape ``(batch_size, sequence_length, hidden_size)``:
	Hidden-states of the models at the output of each layer plus the initial embedding outputs.
	attentions: (`optional`, returned when ``config.output_attentions=True``)
	list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

	Examples::

	tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
	models = BertModel.from_pretrained('bert-base-uncased')
	input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
	outputs = models(input_ids)
	last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple

	"""

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

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

	self.init_weights()

	def forward(
	self,
	input_ids=None,
	pinyin_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
	if the models is configured as a decoder.
	encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask
	is used in the cross-attention if the models is configured as a decoder.
	Mask values selected in ``[0, 1]``:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

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

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

	# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
	# ourselves in which case we just need to make it broadcastable to all heads.
	extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if self.config.is_decoder and encoder_hidden_states is not None:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
	if encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
	encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_extended_attention_mask = None

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

	embedding_output = self.embeddings(
	input_ids=input_ids, pinyin_ids=pinyin_ids, position_ids=position_ids, token_type_ids=token_type_ids,
	inputs_embeds=inputs_embeds
	)
	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask=extended_attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_extended_attention_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	sequence_output = encoder_outputs[0]
	pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

	if not return_dict:
	return (sequence_output, pooled_output) + encoder_outputs[1:]

	return BaseModelOutputWithPooling(
	last_hidden_state=sequence_output,
	pooler_output=pooled_output,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	)


	class GlyceBertForMaskedLM(BertPreTrainedModel):
	def __init__(self, config):
	super(GlyceBertForMaskedLM, self).__init__(config)

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

	self.init_weights()

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

	def forward(
	self,
	input_ids=None,
	pinyin_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	**kwargs
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for computing the masked language modeling loss.
	Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
	Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
	in ``[0, ..., config.vocab_size]``
	kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
	Used to hide legacy arguments that have been deprecated.
	"""
	if "masked_lm_labels" in kwargs:
	warnings.warn(
	"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
	FutureWarning,
	)
	labels = kwargs.pop("masked_lm_labels")
	assert "lm_labels" not in kwargs, "Use `BertWithLMHead` for autoregressive language modeling task."
	assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.bert(
	input_ids,
	pinyin_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

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

	masked_lm_loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss() # -100 index = padding token
	masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

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

	return MaskedLMOutput(
	loss=masked_lm_loss,
	logits=prediction_scores,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	class GlyceBertForSequenceClassification(BertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels

	self.bert = GlyceBertModel(config)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

	self.init_weights()

	def forward(
	self,
	input_ids=None,
	pinyin_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	Labels for computing the sequence classification/regression loss.
	Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
	If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
	If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.bert(
	input_ids,
	pinyin_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = outputs[1]

	pooled_output = self.dropout(pooled_output)
	logits = self.classifier(pooled_output)

	loss = None
	if labels is not None:
	if self.num_labels == 1:
	# We are doing regression
	loss_fct = MSELoss()
	loss = loss_fct(logits.view(-1), labels.view(-1))
	else:
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

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

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


	class GlyceBertForQuestionAnswering(BertPreTrainedModel):
	"""BERT model for Question Answering (span extraction).
	This module is composed of the BERT model with a linear layer on top of
	the sequence output that computes start_logits and end_logits

	Params:
	`config`: a BertConfig class instance with the configuration to build a new model.

	Inputs:
	`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
	with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
	`extract_features.py`, `run_classifier.py` and `run_squad.py`)
	`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
	types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
	a `sentence B` token (see BERT paper for more details).
	`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
	selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
	input sequence length in the current batch. It's the mask that we typically use for attention when
	a batch has varying length sentences.
	`start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
	Positions are clamped to the length of the sequence and position outside of the sequence are not taken
	into account for computing the loss.
	`end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
	Positions are clamped to the length of the sequence and position outside of the sequence are not taken
	into account for computing the loss.

	Outputs:
	if `start_positions` and `end_positions` are not `None`:
	Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
	if `start_positions` or `end_positions` is `None`:
	Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
	position tokens of shape [batch_size, sequence_length].

	Example usage:
	```python
	# Already been converted into WordPiece token ids
	input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
	input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
	token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])

	config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
	num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)

	model = BertForQuestionAnswering(config)
	start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
	```
	"""

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

	self.bert = GlyceBertModel(config)
	self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

	self.init_weights()

	def forward(
	self,
	input_ids=None,
	pinyin_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	start_positions=None,
	end_positions=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	Labels for position (index) of the start of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (:obj:`sequence_length`).
	Position outside of the sequence are not taken into account for computing the loss.
	end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	Labels for position (index) of the end of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (:obj:`sequence_length`).
	Position outside of the sequence are not taken into account for computing the loss.
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.bert(
	input_ids,
	pinyin_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	sequence_output = outputs[0]

	logits = self.qa_outputs(sequence_output)
	start_logits, end_logits = logits.split(1, dim=-1)
	start_logits = start_logits.squeeze(-1)
	end_logits = end_logits.squeeze(-1)

	total_loss = None
	if start_positions is not None and end_positions is not None:
	# If we are on multi-GPU, split add a dimension
	if len(start_positions.size()) > 1:
	start_positions = start_positions.squeeze(-1)
	if len(end_positions.size()) > 1:
	end_positions = end_positions.squeeze(-1)
	# sometimes the start/end positions are outside our model inputs, we ignore these terms
	ignored_index = start_logits.size(1)
	start_positions.clamp_(0, ignored_index)
	end_positions.clamp_(0, ignored_index)

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

	if not return_dict:
	output = (start_logits, end_logits) + outputs[2:]
	return ((total_loss,) + output) if total_loss is not None else output

	return QuestionAnsweringModelOutput(
	loss=total_loss,
	start_logits=start_logits,
	end_logits=end_logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	class GlyceBertForTokenClassification(BertPreTrainedModel):
	def __init__(self, config, mlp=False):
	super().__init__(config)
	self.num_labels = config.num_labels

	self.bert = GlyceBertModel(config)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	if mlp:
	self.classifier = BertMLP(config)
	else:
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

	self.init_weights()

	def forward(self,
	input_ids=None,
	pinyin_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for computing the token classification loss.
	Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.bert(
	input_ids,
	pinyin_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	sequence_output = outputs[0]

	sequence_output = self.dropout(sequence_output)
	logits = self.classifier(sequence_output)

	loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss()
	# Only keep the active parts of the loss
	if attention_mask is not None:
	active_loss = attention_mask.view(-1) == 1
	active_logits = logits.view(-1, self.num_labels)
	active_labels = torch.where(
	active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
	)
	loss = loss_fct(active_logits, active_labels)
	else:
	loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

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

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


	class FusionBertEmbeddings(nn.Module):
	"""
	Construct the embeddings from word, position, glyph, pinyin and token_type embeddings.
	"""

	def __init__(self, config):
	super(FusionBertEmbeddings, self).__init__()
	self.path = Path(config._name_or_path)
	config_path = cache_path / 'config'
	if not os.path.exists(config_path):
	os.makedirs(config_path)

	font_files = []
	download_file("config/STFANGSO.TTF24.npy", self.path)
	download_file("config/STXINGKA.TTF24.npy", self.path)
	download_file("config/方正古隶繁体.ttf24.npy", self.path)
	for file in os.listdir(config_path):
	if file.endswith(".npy"):
	font_files.append(str(config_path / file))
	self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
	self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
	self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
	self.pinyin_embeddings = PinyinEmbedding(embedding_size=128, pinyin_out_dim=config.hidden_size, config=config)
	self.glyph_embeddings = GlyphEmbedding(font_npy_files=font_files)

	# self.LayerNorm is not snake-cased to stick with TensorFlow models variable name and be able to load
	# any TensorFlow checkpoint file
	self.glyph_map = nn.Linear(1728, config.hidden_size)
	self.map_fc = nn.Linear(config.hidden_size * 3, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	# position_ids (1, len position emb) is contiguous in memory and exported when serialized
	self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))

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

	seq_length = input_shape[1]

	if position_ids is None:
	position_ids = self.position_ids[:, :seq_length]

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

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

	# get char embedding, pinyin embedding and glyph embedding
	word_embeddings = inputs_embeds # [bs,l,hidden_size]
	pinyin_embeddings = self.pinyin_embeddings(pinyin_ids) # [bs,l,hidden_size]
	glyph_embeddings = self.glyph_map(self.glyph_embeddings(input_ids)) # [bs,l,hidden_size]
	# fusion layer
	concat_embeddings = torch.cat((word_embeddings, pinyin_embeddings, glyph_embeddings), 2)
	inputs_embeds = self.map_fc(concat_embeddings)

	position_embeddings = self.position_embeddings(position_ids)
	token_type_embeddings = self.token_type_embeddings(token_type_ids)

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


	class PinyinEmbedding(nn.Module):

	def __init__(self, embedding_size: int, pinyin_out_dim: int, config):
	"""
	Pinyin Embedding Module
	Args:
	embedding_size: the size of each embedding vector
	pinyin_out_dim: kernel number of conv
	"""
	super(PinyinEmbedding, self).__init__()
	download_file('config/pinyin_map.json', Path(config._name_or_path))
	with open(cache_path / 'config' / 'pinyin_map.json') as fin:
	pinyin_dict = json.load(fin)
	self.pinyin_out_dim = pinyin_out_dim
	self.embedding = nn.Embedding(len(pinyin_dict['idx2char']), embedding_size)
	self.conv = nn.Conv1d(in_channels=embedding_size, out_channels=self.pinyin_out_dim, kernel_size=2,
	stride=1, padding=0)

	def forward(self, pinyin_ids):
	"""
	Args:
	pinyin_ids: (bssentence_lengthpinyin_locs)

	Returns:
	pinyin_embed: (bs,sentence_length,pinyin_out_dim)
	"""
	# input pinyin ids for 1-D conv
	embed = self.embedding(pinyin_ids) # [bs,sentence_length,pinyin_locs,embed_size]
	bs, sentence_length, pinyin_locs, embed_size = embed.shape
	view_embed = embed.view(-1, pinyin_locs, embed_size) # [(bs*sentence_length),pinyin_locs,embed_size]
	input_embed = view_embed.permute(0, 2, 1) # [(bs*sentence_length), embed_size, pinyin_locs]
	# conv + max_pooling
	pinyin_conv = self.conv(input_embed) # [(bs*sentence_length),pinyin_out_dim,H]
	pinyin_embed = F.max_pool1d(pinyin_conv, pinyin_conv.shape[-1]) # [(bs*sentence_length),pinyin_out_dim,1]
	return pinyin_embed.view(bs, sentence_length, self.pinyin_out_dim) # [bs,sentence_length,pinyin_out_dim]


	class BertMLP(nn.Module):
	def __init__(self, config, ):
	super().__init__()
	self.dense_layer = nn.Linear(config.hidden_size, config.hidden_size)
	self.dense_to_labels_layer = nn.Linear(config.hidden_size, config.num_labels)
	self.activation = nn.Tanh()

	def forward(self, sequence_hidden_states):
	sequence_output = self.dense_layer(sequence_hidden_states)
	sequence_output = self.activation(sequence_output)
	sequence_output = self.dense_to_labels_layer(sequence_output)
	return sequence_output


	class GlyphEmbedding(nn.Module):
	"""Glyph2Image Embedding"""

	def __init__(self, font_npy_files: List[str]):
	super(GlyphEmbedding, self).__init__()
	font_arrays = [
	np.load(np_file).astype(np.float32) for np_file in font_npy_files
	]
	self.vocab_size = font_arrays[0].shape[0]
	self.font_num = len(font_arrays)
	self.font_size = font_arrays[0].shape[-1]
	# N, C, H, W
	font_array = np.stack(font_arrays, axis=1)
	self.embedding = nn.Embedding(
	num_embeddings=self.vocab_size,
	embedding_dim=self.font_size ** 2 * self.font_num,
	_weight=torch.from_numpy(font_array.reshape([self.vocab_size, -1]))
	)

	def forward(self, input_ids):
	"""
	get glyph images for batch inputs
	Args:
	input_ids: [batch, sentence_length]
	Returns:
	images: [batch, sentence_length, self.font_numself.font_sizeself.font_size]
	"""
	# return self.embedding(input_ids).view([-1, self.font_num, self.font_size, self.font_size])
	return self.embedding(input_ids)