




# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
__lowerCamelCase :    int      						=      1E-10
__lowerCamelCase :    Dict      						=      '''This is incredible! I love it, this is the best chicken I have ever had.'''
__lowerCamelCase :    str      						=      1_00


class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Dict							,    lowerCamelCase_      : str							,    lowerCamelCase_      : str="gpt2-medium"							,    lowerCamelCase_      : Tuple=False							,    lowerCamelCase_      : List[str]="cpu"	)    -> Tuple:
								super().__init__()
								__magic_name__				:						Any     				=				GPTaTokenizer.from_pretrained(lowerCamelCase_	)
								__magic_name__				:						Optional[Any]     				=				GPTaLMHeadModel.from_pretrained(lowerCamelCase_	)
								__magic_name__				:						Optional[int]     				=				self.encoder.transformer.config.hidden_size
								__magic_name__				:						Tuple     				=				ClassificationHead(class_size=lowerCamelCase_							,    embed_size=self.embed_size	)
								__magic_name__				:						List[str]     				=				cached_mode
								__magic_name__				:						Optional[int]     				=				device

			def 				UpperCAmelCase__							(			self      : Tuple	)    -> List[str]:
								return self.classifier_head

			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> int:
								for param in self.encoder.parameters():
													__magic_name__				:						str     				=				False
								self.classifier_head.train()

			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : Optional[Any]	)    -> Optional[Any]:
								__magic_name__				:						List[Any]     				=				x.ne(0	).unsqueeze(2	).repeat(1							,    1							,    self.embed_size	).float().to(self.device	).detach()
								__magic_name__				:						Tuple     				=				self.encoder.transformer(lowerCamelCase_	)['''last_hidden_state''']
								__magic_name__				:						Optional[int]     				=				hidden * mask
								__magic_name__				:						List[str]     				=				torch.sum(lowerCamelCase_							,    dim=1	) / (torch.sum(lowerCamelCase_							,    dim=1	).detach() + EPSILON)
								return avg_hidden


			def 				UpperCAmelCase__							(			self      : Dict							,    lowerCamelCase_      : List[Any]	)    -> Union[str, Any]:
								if self.cached_mode:
													__magic_name__				:						Optional[int]     				=				x.to(self.device	)
								else:
													__magic_name__				:						str     				=				self.avg_representation(x.to(self.device	)	)

								__magic_name__				:						str     				=				self.classifier_head(lowerCamelCase_	)
								__magic_name__				:						Tuple     				=				nn.functional.log_softmax(lowerCamelCase_							,    dim=-1	)

								return probs






class  lowerCamelCase     (		data.Dataset    ):






			'''simple docstring'''





			def __init__(			self      : Optional[int]							,    lowerCamelCase_      : Optional[int]							,    lowerCamelCase_      : Dict	)    -> List[Any]:
								__magic_name__				:						Dict     				=				X
								__magic_name__				:						List[str]     				=				y

			def __len__(			self      : Optional[int]	)    -> Dict:
								return len(self.X	)


			def __getitem__(			self      : List[Any]							,    lowerCamelCase_      : Tuple	)    -> List[Any]:
								__magic_name__				:						int     				=				{}
								__magic_name__				:						List[Any]     				=				self.X[index]
								__magic_name__				:						List[str]     				=				self.y[index]
								return data







def 		lowercase__ (		__A:						str					):





					'''simple docstring'''






					def pad_sequences(__A:						List[Any]					):
										__magic_name__				:						List[str]     				=				[len(__A					) for seq in sequences]

										__magic_name__				:						Tuple     				=				torch.zeros(len(__A					)				,max(__A					)					).long()  # padding value = 0

										for i, seq in enumerate(__A					):
															__magic_name__				:						int     				=				lengths[i]
															__magic_name__				:						Tuple     				=				seq[:end]

										return padded_sequences, lengths

					__magic_name__				:						List[Any]     				=				{}
					for key in data[0].keys():
										__magic_name__				:						List[str]     				=				[d[key] for d in data]

					__magic_name__				,     __magic_name__				:						Optional[Any]     				=				pad_sequences(item_info['''X''']					)
					__magic_name__				:						Tuple     				=				torch.tensor(item_info['''y''']				,dtype=torch.long					)

					return x_batch, y_batch







def 		lowercase__ (		__A:						Any					):





					'''simple docstring'''






					__magic_name__				:						Optional[int]     				=				{}
					for key in data[0].keys():
										__magic_name__				:						Tuple     				=				[d[key] for d in data]

					__magic_name__				:						str     				=				torch.cat(item_info['''X''']				,0					)
					__magic_name__				:						Optional[Any]     				=				torch.tensor(item_info['''y''']				,dtype=torch.long					)

					return x_batch, y_batch







def 		lowercase__ (		__A:						Dict				,__A:						int				,__A:						List[Any]				,__A:						List[Any]=0				,__A:						Tuple=1_0				,__A:						Union[str, Any]="cpu"					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				0
					discriminator.train_custom()
					for batch_idx, (input_t, target_t) in enumerate(__A					):
										__magic_name__				,     __magic_name__				:						Optional[Any]     				=				input_t.to(__A					), target_t.to(__A					)

										optimizer.zero_grad()

										__magic_name__				:						Dict     				=				discriminator(__A					)
										__magic_name__				:						int     				=				nn.functional.nll_loss(__A				,__A					)
										loss.backward(retain_graph=__A					)
										optimizer.step()

										samples_so_far += len(__A					)

										if batch_idx % log_interval == 0:
															print(
															    '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
															        epoch + 1				,__A				,len(data_loader.dataset					)				,1_0_0 * samples_so_far / len(data_loader.dataset					)				,loss.item()				,)					)







def 		lowercase__ (		__A:						str				,__A:						Optional[int]				,__A:						Any="cpu"					):





					'''simple docstring'''






					discriminator.eval()
					__magic_name__				:						Any     				=				0
					__magic_name__				:						Any     				=				0
					with torch.no_grad():
										for input_t, target_t in data_loader:
															__magic_name__				,     __magic_name__				:						Tuple     				=				input_t.to(__A					), target_t.to(__A					)
															__magic_name__				:						List[str]     				=				discriminator(__A					)
															# sum up batch loss
															test_loss += nn.functional.nll_loss(__A				,__A				,reduction='''sum'''					).item()
															# get the index of the max log-probability
															__magic_name__				:						Union[str, Any]     				=				output_t.argmax(dim=1				,keepdim=__A					)
															correct += pred_t.eq(target_t.view_as(__A					)					).sum().item()

					test_loss /= len(data_loader.dataset					)

					print(
					    '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
					        __A				,__A				,len(data_loader.dataset					)				,100.0 * correct / len(data_loader.dataset					)					)					)







def 		lowercase__ (		__A:						Optional[int]				,__A:						str				,__A:						List[str]				,__A:						Union[str, Any]=False				,__A:						Tuple="cpu"					):





					'''simple docstring'''






					__magic_name__				:						str     				=				model.tokenizer.encode(__A					)
					__magic_name__				:						int     				=				torch.tensor([input_t]				,dtype=torch.long				,device=__A					)
					if cached:
										__magic_name__				:						Optional[Any]     				=				model.avg_representation(__A					)

					__magic_name__				:						Optional[int]     				=				model(__A					).data.cpu().numpy().flatten().tolist()
					print('''Input sentence:'''				,__A					)
					print(
					    '''Predictions:'''				,''', '''.join('''{}: {:.4f}'''.format(__A				,math.exp(__A					)					) for c, log_prob in zip(__A				,__A					)					)				,)







def 		lowercase__ (		__A:						Dict				,__A:						int				,__A:						Any				,__A:						List[str]=False				,__A:						List[Any]="cpu"					):





					'''simple docstring'''






					__magic_name__				:						str     				=				torch.utils.data.DataLoader(dataset=__A				,batch_size=__A				,collate_fn=__A					)

					__magic_name__				:						List[Any]     				=				[]
					__magic_name__				:						Tuple     				=				[]
					for batch_idx, (x, y) in enumerate(tqdm(__A				,ascii=__A					)					):
										with torch.no_grad():
															__magic_name__				:						List[Any]     				=				x.to(__A					)
															__magic_name__				:						Optional[int]     				=				discriminator.avg_representation(__A					).cpu().detach()
															__magic_name__				:						Union[str, Any]     				=				torch.unbind(avg_rep.unsqueeze(1					)					)
															xs += avg_rep_list
															ys += y.cpu().numpy().tolist()

					__magic_name__				:						List[str]     				=				torch.utils.data.DataLoader(
					    dataset=Dataset(__A				,__A					)				,batch_size=__A				,shuffle=__A				,collate_fn=__A					)

					return data_loader







def 		lowercase__ (		__A:						List[str]				,__A:						str=None				,__A:						List[str]="gpt2-medium"				,__A:						Any=1_0				,__A:						Optional[int]=6_4				,__A:						Optional[Any]=1_0				,__A:						Tuple=False				,__A:						List[str]=False				,__A:						int=False				,):





					'''simple docstring'''






					__magic_name__				:						Tuple     				=				'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

					print('''Preprocessing {} dataset...'''.format(__A					)					)
					__magic_name__				:						List[str]     				=				time.time()

					if dataset == "SST":
										__magic_name__				:						Dict     				=				['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
										__magic_name__				:						List[Any]     				=				{c: i for i, c in enumerate(__A					)}

										__magic_name__				:						Union[str, Any]     				=				Discriminator(
										    class_size=len(__A					)				,pretrained_model=__A				,cached_mode=__A				,device=__A					).to(__A					)

										__magic_name__				:						Any     				=				torchtext_data.Field()
										__magic_name__				:						Optional[int]     				=				torchtext_data.Field(sequential=__A					)
										__magic_name__				,     __magic_name__				,     __magic_name__				:						List[str]     				=				datasets.SST.splits(
										    __A				,__A				,fine_grained=__A				,train_subtrees=__A				,)

										__magic_name__				:						List[str]     				=				[]
										__magic_name__				:						str     				=				[]
										for i in trange(len(__A					)				,ascii=__A					):
															__magic_name__				:						List[str]     				=				TreebankWordDetokenizer().detokenize(vars(train_data[i]					)['''text''']					)
															__magic_name__				:						List[str]     				=				discriminator.tokenizer.encode(__A					)
															__magic_name__				:						str     				=				torch.tensor([5_0_2_5_6] + seq				,device=__A				,dtype=torch.long					)
															x.append(__A					)
															y.append(classaidx[vars(train_data[i]					)['''label''']]					)
										__magic_name__				:						List[str]     				=				Dataset(__A				,__A					)

										__magic_name__				:						Tuple     				=				[]
										__magic_name__				:						int     				=				[]
										for i in trange(len(__A					)				,ascii=__A					):
															__magic_name__				:						Union[str, Any]     				=				TreebankWordDetokenizer().detokenize(vars(test_data[i]					)['''text''']					)
															__magic_name__				:						int     				=				discriminator.tokenizer.encode(__A					)
															__magic_name__				:						List[str]     				=				torch.tensor([5_0_2_5_6] + seq				,device=__A				,dtype=torch.long					)
															test_x.append(__A					)
															test_y.append(classaidx[vars(test_data[i]					)['''label''']]					)
										__magic_name__				:						Optional[int]     				=				Dataset(__A				,__A					)

										__magic_name__				:						Union[str, Any]     				=				{
										    '''class_size''': len(__A					),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 2,
										}

					elif dataset == "clickbait":
										__magic_name__				:						List[Any]     				=				['''non_clickbait''', '''clickbait''']
										__magic_name__				:						Any     				=				{c: i for i, c in enumerate(__A					)}

										__magic_name__				:						Dict     				=				Discriminator(
										    class_size=len(__A					)				,pretrained_model=__A				,cached_mode=__A				,device=__A					).to(__A					)

										with open('''datasets/clickbait/clickbait_train_prefix.txt'''					) as f:
															__magic_name__				:						str     				=				[]
															for i, line in enumerate(__A					):
																				try:
																									data.append(eval(__A					)					)
																				except Exception:
																									print('''Error evaluating line {}: {}'''.format(__A				,__A					)					)
																									continue
										__magic_name__				:						Dict     				=				[]
										__magic_name__				:						Optional[Any]     				=				[]
										with open('''datasets/clickbait/clickbait_train_prefix.txt'''					) as f:
															for i, line in enumerate(tqdm(__A				,ascii=__A					)					):
																				try:
																									__magic_name__				:						Optional[int]     				=				eval(__A					)
																									__magic_name__				:						int     				=				discriminator.tokenizer.encode(d['''text''']					)

																									if len(__A					) < max_length_seq:
																														__magic_name__				:						Optional[int]     				=				torch.tensor([5_0_2_5_6] + seq				,device=__A				,dtype=torch.long					)
																									else:
																														print('''Line {} is longer than maximum length {}'''.format(__A				,__A					)					)
																														continue
																									x.append(__A					)
																									y.append(d['''label''']					)
																				except Exception:
																									print('''Error evaluating / tokenizing line {}, skipping it'''.format(__A					)					)
																									pass

										__magic_name__				:						Tuple     				=				Dataset(__A				,__A					)
										__magic_name__				:						Any     				=				int(0.9 * len(__A					)					)
										__magic_name__				:						List[Any]     				=				len(__A					) - train_size
										__magic_name__				,     __magic_name__				:						Dict     				=				torch.utils.data.random_split(__A				,[train_size, test_size]					)

										__magic_name__				:						Tuple     				=				{
										    '''class_size''': len(__A					),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 1,
										}

					elif dataset == "toxic":
										__magic_name__				:						List[str]     				=				['''non_toxic''', '''toxic''']
										__magic_name__				:						Dict     				=				{c: i for i, c in enumerate(__A					)}

										__magic_name__				:						List[str]     				=				Discriminator(
										    class_size=len(__A					)				,pretrained_model=__A				,cached_mode=__A				,device=__A					).to(__A					)

										__magic_name__				:						Tuple     				=				[]
										__magic_name__				:						List[str]     				=				[]
										with open('''datasets/toxic/toxic_train.txt'''					) as f:
															for i, line in enumerate(tqdm(__A				,ascii=__A					)					):
																				try:
																									__magic_name__				:						List[str]     				=				eval(__A					)
																									__magic_name__				:						List[str]     				=				discriminator.tokenizer.encode(d['''text''']					)

																									if len(__A					) < max_length_seq:
																														__magic_name__				:						Optional[int]     				=				torch.tensor([5_0_2_5_6] + seq				,device=__A				,dtype=torch.long					)
																									else:
																														print('''Line {} is longer than maximum length {}'''.format(__A				,__A					)					)
																														continue
																									x.append(__A					)
																									y.append(int(np.sum(d['''label''']					) > 0					)					)
																				except Exception:
																									print('''Error evaluating / tokenizing line {}, skipping it'''.format(__A					)					)
																									pass

										__magic_name__				:						Optional[Any]     				=				Dataset(__A				,__A					)
										__magic_name__				:						Tuple     				=				int(0.9 * len(__A					)					)
										__magic_name__				:						int     				=				len(__A					) - train_size
										__magic_name__				,     __magic_name__				:						str     				=				torch.utils.data.random_split(__A				,[train_size, test_size]					)

										__magic_name__				:						Optional[Any]     				=				{
										    '''class_size''': len(__A					),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 0,
										}

					else:  # if dataset == "generic":
										# This assumes the input dataset is a TSV with the following structure:
										# class \t text

										if dataset_fp is None:
															raise ValueError('''When generic dataset is selected, dataset_fp needs to be specified aswell.'''					)

										__magic_name__				:						str     				=				set()
										with open(__A					) as f:
															__magic_name__				:						List[Any]     				=				csv.reader(__A				,delimiter='''\t'''					)
															for row in tqdm(__A				,ascii=__A					):
																				if row:
																									classes.add(row[0]					)

										__magic_name__				:						Optional[int]     				=				sorted(__A					)
										__magic_name__				:						List[Any]     				=				{c: i for i, c in enumerate(__A					)}

										__magic_name__				:						Dict     				=				Discriminator(
										    class_size=len(__A					)				,pretrained_model=__A				,cached_mode=__A				,device=__A					).to(__A					)

										__magic_name__				:						List[str]     				=				[]
										__magic_name__				:						List[Any]     				=				[]
										with open(__A					) as f:
															__magic_name__				:						Any     				=				csv.reader(__A				,delimiter='''\t'''					)
															for i, row in enumerate(tqdm(__A				,ascii=__A					)					):
																				if row:
																									__magic_name__				:						Any     				=				row[0]
																									__magic_name__				:						Any     				=				row[1]

																									try:
																														__magic_name__				:						int     				=				discriminator.tokenizer.encode(__A					)
																														if len(__A					) < max_length_seq:
																																			__magic_name__				:						Dict     				=				torch.tensor([5_0_2_5_6] + seq				,device=__A				,dtype=torch.long					)

																														else:
																																			print('''Line {} is longer than maximum length {}'''.format(__A				,__A					)					)
																																			continue

																														x.append(__A					)
																														y.append(classaidx[label]					)

																									except Exception:
																														print('''Error tokenizing line {}, skipping it'''.format(__A					)					)
																														pass

										__magic_name__				:						str     				=				Dataset(__A				,__A					)
										__magic_name__				:						Dict     				=				int(0.9 * len(__A					)					)
										__magic_name__				:						Optional[Any]     				=				len(__A					) - train_size
										__magic_name__				,     __magic_name__				:						List[str]     				=				torch.utils.data.random_split(__A				,[train_size, test_size]					)

										__magic_name__				:						Tuple     				=				{
										    '''class_size''': len(__A					),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 0,
										}

					__magic_name__				:						int     				=				time.time()
					print('''Preprocessed {} data points'''.format(len(__A					) + len(__A					)					)					)
					print('''Data preprocessing took: {:.3f}s'''.format(end - start					)					)

					if cached:
										print('''Building representation cache...'''					)

										__magic_name__				:						List[str]     				=				time.time()

										__magic_name__				:						Dict     				=				get_cached_data_loader(__A				,__A				,__A				,shuffle=__A				,device=__A					)

										__magic_name__				:						Any     				=				get_cached_data_loader(__A				,__A				,__A				,device=__A					)

										__magic_name__				:						Optional[Any]     				=				time.time()
										print('''Building representation cache took: {:.3f}s'''.format(end - start					)					)

					else:
										__magic_name__				:						Union[str, Any]     				=				torch.utils.data.DataLoader(
										    dataset=__A				,batch_size=__A				,shuffle=__A				,collate_fn=__A					)
										__magic_name__				:						List[str]     				=				torch.utils.data.DataLoader(dataset=__A				,batch_size=__A				,collate_fn=__A					)

					if save_model:
										with open('''{}_classifier_head_meta.json'''.format(__A					)				,'''w'''					) as meta_file:
															json.dump(__A				,__A					)

					__magic_name__				:						List[Any]     				=				optim.Adam(discriminator.parameters()				,lr=0.0001					)

					for epoch in range(__A					):
										__magic_name__				:						Union[str, Any]     				=				time.time()
										print('''\nEpoch'''				,epoch + 1					)

										train_epoch(
										    discriminator=__A				,data_loader=__A				,optimizer=__A				,epoch=__A				,log_interval=__A				,device=__A				,)
										evaluate_performance(data_loader=__A				,discriminator=__A				,device=__A					)

										__magic_name__				:						Dict     				=				time.time()
										print('''Epoch took: {:.3f}s'''.format(end - start					)					)

										print('''\nExample prediction'''					)
										predict(__A				,__A				,__A				,cached=__A				,device=__A					)

										if save_model:
															# torch.save(discriminator.state_dict(),
															#           "{}_discriminator_{}.pt".format(
															#               args.dataset, epoch + 1
															#               ))
															torch.save(
															    discriminator.get_classifier().state_dict()				,'''{}_classifier_head_epoch_{}.pt'''.format(__A				,epoch + 1					)				,)


if __name__ == "__main__":
	__lowerCamelCase :    List[str]      						=      argparse.ArgumentParser(description='''Train a discriminator on top of GPT-2 representations''')
	parser.add_argument(
	    '''--dataset''',
	    type=str,
	    default='''SST''',
	    choices=('''SST''', '''clickbait''', '''toxic''', '''generic'''),
	    help=(
	        '''dataset to train the discriminator on.'''
	        '''In case of generic, the dataset is expected'''
	        '''to be a TSBV file with structure: class \\t text'''
	    ),
	)
	parser.add_argument(
	    '''--dataset_fp''',
	    type=str,
	    default='''''',
	    help='''File path of the dataset to use. Needed only in case of generic datadset''',
	)
	parser.add_argument(
	    '''--pretrained_model''', type=str, default='''gpt2-medium''', help='''Pretrained model to use as encoder'''
	)
	parser.add_argument('''--epochs''', type=int, default=10, metavar='''N''', help='''Number of training epochs''')
	parser.add_argument(
	    '''--batch_size''', type=int, default=64, metavar='''N''', help='''input batch size for training (default: 64)'''
	)
	parser.add_argument(
	    '''--log_interval''',
	    type=int,
	    default=10,
	    metavar='''N''',
	    help='''how many batches to wait before logging training status''',
	)
	parser.add_argument('''--save_model''', action='''store_true''', help='''whether to save the model''')
	parser.add_argument('''--cached''', action='''store_true''', help='''whether to cache the input representations''')
	parser.add_argument('''--no_cuda''', action='''store_true''', help='''use to turn off cuda''')
	__lowerCamelCase :    int      						=      parser.parse_args()

	train_discriminator(**(vars(args)))









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


__lowerCamelCase :    List[Any]      						=      logging.get_logger(__name__)

__lowerCamelCase :    Optional[int]      						=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase :    List[Any]      						=      {
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

__lowerCamelCase :    int      						=      {
    '''microsoft/mpnet-base''': 5_12,
}

__lowerCamelCase :    Union[str, Any]      						=      {
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}


class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			UpperCamelCase__										=VOCAB_FILES_NAMES
			UpperCamelCase__										=PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__										=PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase__										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__										=MPNetTokenizer
			UpperCamelCase__										=['''input_ids''', '''attention_mask''']

			def __init__(			self      : Optional[int]							,    lowerCamelCase_      : List[str]=None							,    lowerCamelCase_      : int=None							,    lowerCamelCase_      : Dict=True							,    lowerCamelCase_      : int="<s>"							,    lowerCamelCase_      : Dict="</s>"							,    lowerCamelCase_      : int="</s>"							,    lowerCamelCase_      : List[Any]="<s>"							,    lowerCamelCase_      : Union[str, Any]="[UNK]"							,    lowerCamelCase_      : Any="<pad>"							,    lowerCamelCase_      : Dict="<mask>"							,    lowerCamelCase_      : Optional[Any]=True							,    lowerCamelCase_      : List[Any]=None							,    **lowerCamelCase_      : List[Any]							,    )    -> Dict:
								super().__init__(
								    lowerCamelCase_							,    tokenizer_file=lowerCamelCase_							,    do_lower_case=lowerCamelCase_							,    bos_token=lowerCamelCase_							,    eos_token=lowerCamelCase_							,    sep_token=lowerCamelCase_							,    cls_token=lowerCamelCase_							,    unk_token=lowerCamelCase_							,    pad_token=lowerCamelCase_							,    mask_token=lowerCamelCase_							,    tokenize_chinese_chars=lowerCamelCase_							,    strip_accents=lowerCamelCase_							,    **lowerCamelCase_							,    )

								__magic_name__				:						Any     				=				json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
								if (
								    pre_tok_state.get('''lowercase'''							,    lowerCamelCase_	) != do_lower_case
								    or pre_tok_state.get('''strip_accents'''							,    lowerCamelCase_	) != strip_accents
								):
													__magic_name__				:						int     				=				getattr(lowerCamelCase_							,    pre_tok_state.pop('''type'''	)	)
													__magic_name__				:						str     				=				do_lower_case
													__magic_name__				:						Union[str, Any]     				=				strip_accents
													__magic_name__				:						str     				=				pre_tok_class(**lowerCamelCase_	)

								__magic_name__				:						List[Any]     				=				do_lower_case

			@property
			def 				UpperCAmelCase__							(			self      : str	)    -> str:
								if self._mask_token is None:
													if self.verbose:
																		logger.error('''Using mask_token, but it is not set yet.'''	)
													return None
								return str(self._mask_token	)

			@mask_token.setter
			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : List[str]	)    -> Optional[int]:
								__magic_name__				:						List[str]     				=				AddedToken(lowerCamelCase_							,    lstrip=lowerCamelCase_							,    rstrip=lowerCamelCase_	) if isinstance(lowerCamelCase_							,    lowerCamelCase_	) else value
								__magic_name__				:						Tuple     				=				value

			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : Optional[Any]							,    lowerCamelCase_      : Any=None	)    -> List[Any]:
								__magic_name__				:						Optional[int]     				=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
													return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

			def 				UpperCAmelCase__							(			self      : List[str]							,    lowerCamelCase_      : List[int]							,    lowerCamelCase_      : Optional[List[int]] = None	)    -> List[int]:
								__magic_name__				:						Tuple     				=				[self.sep_token_id]
								__magic_name__				:						Any     				=				[self.cls_token_id]

								if token_ids_a is None:
													return len(cls + token_ids_a + sep	) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]


			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : str							,    lowerCamelCase_      : Optional[str] = None	)    -> Tuple[str]:
								__magic_name__				:						List[Any]     				=				self._tokenizer.model.save(lowerCamelCase_							,    name=lowerCamelCase_	)
								return tuple(lowerCamelCase_	)



