




'''simple docstring'''







# 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)
A			   =1E-10
A			   ="This is incredible! I love it, this is the best chicken I have ever had."
A			   =1_00


class       _a  (     nn.Module			):
       def __init__(     self						:     List[Any]       ,    lowercase						:     Dict       ,    lowercase						:     List[str]="gpt2-medium"       ,    lowercase						:     List[str]=False       ,    lowercase						:     List[str]="cpu"    ):



           '''simple docstring'''

           super().__init__()
           UpperCAmelCase      	=		GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )
           UpperCAmelCase      	=		GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
           UpperCAmelCase      	=		self.encoder.transformer.config.hidden_size
           UpperCAmelCase      	=		ClassificationHead(class_size=SCREAMING_SNAKE_CASE_       ,    embed_size=self.embed_size    )
           UpperCAmelCase      	=		cached_mode
           UpperCAmelCase      	=		device
       def      A				(     self						:     int    ):



           '''simple docstring'''

           return self.classifier_head
       def      A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           for param in self.encoder.parameters():
               UpperCAmelCase      	=		False
           self.classifier_head.train()
       def      A				(     self						:     int       ,    lowercase						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		x.ne(0    ).unsqueeze(2    ).repeat(1       ,    1       ,    self.embed_size    ).float().to(self.device    ).detach()
           UpperCAmelCase      	=		self.encoder.transformer(SCREAMING_SNAKE_CASE_    )['''last_hidden_state''']
           UpperCAmelCase      	=		hidden * mask
           UpperCAmelCase      	=		torch.sum(SCREAMING_SNAKE_CASE_       ,    dim=1    ) / (torch.sum(SCREAMING_SNAKE_CASE_       ,    dim=1    ).detach() + EPSILON)
           return avg_hidden







       def      A				(     self						:     Optional[int]       ,    lowercase						:     str    ):



           '''simple docstring'''

           if self.cached_mode:
               UpperCAmelCase      	=		x.to(self.device    )
           else:
               UpperCAmelCase      	=		self.avg_representation(x.to(self.device    )    )

           UpperCAmelCase      	=		self.classifier_head(SCREAMING_SNAKE_CASE_    )
           UpperCAmelCase      	=		nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,    dim=-1    )

           return probs






class       _a  (     data.Dataset			):
       def __init__(     self						:     List[Any]       ,    lowercase						:     List[str]       ,    lowercase						:     Tuple    ):



           '''simple docstring'''

           UpperCAmelCase      	=		X
           UpperCAmelCase      	=		y
       def __len__(     self						:     Any    ):



           '''simple docstring'''

           return len(self.X    )







       def __getitem__(     self						:     List[Any]       ,    lowercase						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		{}
           UpperCAmelCase      	=		self.X[index]
           UpperCAmelCase      	=		self.y[index]
           return data






def 						snake_case_							(_a     :							str					):

    def pad_sequences(_a     :							Tuple					):
        UpperCAmelCase      	=		[len(_a					) for seq in sequences]

        UpperCAmelCase      	=		torch.zeros(len(_a					)   ,       max(_a					)					).long()  # padding value = 0

        for i, seq in enumerate(_a					):
            UpperCAmelCase      	=		lengths[i]
            UpperCAmelCase      	=		seq[:end]

        return padded_sequences, lengths

    UpperCAmelCase      	=		{}
    for key in data[0].keys():
        UpperCAmelCase      	=		[d[key] for d in data]

    UpperCAmelCase	,					UpperCAmelCase      	=		pad_sequences(item_info['''X''']					)
    UpperCAmelCase      	=		torch.tensor(item_info['''y''']   ,       dtype=torch.long					)

    return x_batch, y_batch





def 						snake_case_							(_a     :							List[Any]					):
    UpperCAmelCase      	=		{}
    for key in data[0].keys():
        UpperCAmelCase      	=		[d[key] for d in data]

    UpperCAmelCase      	=		torch.cat(item_info['''X''']   ,       0					)
    UpperCAmelCase      	=		torch.tensor(item_info['''y''']   ,       dtype=torch.long					)

    return x_batch, y_batch





def 						snake_case_							(_a     :							List[Any]   ,       _a     :							Optional[Any]   ,       _a     :							Optional[int]   ,       _a     :							List[str]=0   ,       _a     :							Tuple=1_0   ,       _a     :							Any="cpu"					):
    UpperCAmelCase      	=		0
    discriminator.train_custom()
    for batch_idx, (input_t, target_t) in enumerate(_a					):
        UpperCAmelCase	,					UpperCAmelCase      	=		input_t.to(_a					), target_t.to(_a					)

        optimizer.zero_grad()

        UpperCAmelCase      	=		discriminator(_a					)
        UpperCAmelCase      	=		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 						snake_case_							(_a     :							Any   ,       _a     :							Optional[int]   ,       _a     :							Optional[int]="cpu"					):
    discriminator.eval()
    UpperCAmelCase      	=		0
    UpperCAmelCase      	=		0
    with torch.no_grad():
        for input_t, target_t in data_loader:
            UpperCAmelCase	,					UpperCAmelCase      	=		input_t.to(_a					), target_t.to(_a					)
            UpperCAmelCase      	=		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
            UpperCAmelCase      	=		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 						snake_case_							(_a     :							List[Any]   ,       _a     :							List[Any]   ,       _a     :							Dict   ,       _a     :							Any=False   ,       _a     :							List[Any]="cpu"					):
    UpperCAmelCase      	=		model.tokenizer.encode(_a					)
    UpperCAmelCase      	=		torch.tensor([input_t]   ,       dtype=torch.long   ,       device=_a					)
    if cached:
        UpperCAmelCase      	=		model.avg_representation(_a					)

    UpperCAmelCase      	=		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 						snake_case_							(_a     :							int   ,       _a     :							str   ,       _a     :							List[str]   ,       _a     :							Union[str, Any]=False   ,       _a     :							Any="cpu"					):
    UpperCAmelCase      	=		torch.utils.data.DataLoader(dataset=_a   ,       batch_size=_a   ,       collate_fn=_a					)

    UpperCAmelCase      	=		[]
    UpperCAmelCase      	=		[]
    for batch_idx, (x, y) in enumerate(tqdm(_a   ,       ascii=_a					)					):
        with torch.no_grad():
            UpperCAmelCase      	=		x.to(_a					)
            UpperCAmelCase      	=		discriminator.avg_representation(_a					).cpu().detach()
            UpperCAmelCase      	=		torch.unbind(avg_rep.unsqueeze(1					)					)
            xs += avg_rep_list
            ys += y.cpu().numpy().tolist()

    UpperCAmelCase      	=		torch.utils.data.DataLoader(
        dataset=Dataset(_a   ,       _a					)   ,       batch_size=_a   ,       shuffle=_a   ,       collate_fn=_a					)

    return data_loader





def 						snake_case_							(_a     :							List[Any]   ,       _a     :							Optional[int]=None   ,       _a     :							Optional[int]="gpt2-medium"   ,       _a     :							Any=1_0   ,       _a     :							Tuple=6_4   ,       _a     :							Optional[Any]=1_0   ,       _a     :							Tuple=False   ,       _a     :							Tuple=False   ,       _a     :							Any=False   ,       ):
    UpperCAmelCase      	=		'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

    print('''Preprocessing {} dataset...'''.format(_a					)					)
    UpperCAmelCase      	=		time.time()

    if dataset == "SST":
        UpperCAmelCase      	=		['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
        UpperCAmelCase      	=		{c: i for i, c in enumerate(_a					)}

        UpperCAmelCase      	=		Discriminator(
            class_size=len(_a					)   ,       pretrained_model=_a   ,       cached_mode=_a   ,       device=_a					).to(_a					)

        UpperCAmelCase      	=		torchtext_data.Field()
        UpperCAmelCase      	=		torchtext_data.Field(sequential=_a					)
        UpperCAmelCase	,					UpperCAmelCase	,					UpperCAmelCase      	=		datasets.SST.splits(
            _a   ,       _a   ,       fine_grained=_a   ,       train_subtrees=_a   ,       )

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

        UpperCAmelCase      	=		[]
        UpperCAmelCase      	=		[]
        for i in trange(len(_a					)   ,       ascii=_a					):
            UpperCAmelCase      	=		TreebankWordDetokenizer().detokenize(vars(test_data[i]					)['''text''']					)
            UpperCAmelCase      	=		discriminator.tokenizer.encode(_a					)
            UpperCAmelCase      	=		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''']]					)
        UpperCAmelCase      	=		Dataset(_a   ,       _a					)

        UpperCAmelCase      	=		{
            '''class_size''': len(_a					),
            '''embed_size''': discriminator.embed_size,
            '''pretrained_model''': pretrained_model,
            '''class_vocab''': classaidx,
            '''default_class''': 2,
        }

    elif dataset == "clickbait":
        UpperCAmelCase      	=		['''non_clickbait''', '''clickbait''']
        UpperCAmelCase      	=		{c: i for i, c in enumerate(_a					)}

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

                    if len(_a					) < max_length_seq:
                        UpperCAmelCase      	=		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

        UpperCAmelCase      	=		Dataset(_a   ,       _a					)
        UpperCAmelCase      	=		int(0.9 * len(_a					)					)
        UpperCAmelCase      	=		len(_a					) - train_size
        UpperCAmelCase	,					UpperCAmelCase      	=		torch.utils.data.random_split(_a   ,       [train_size, test_size]					)

        UpperCAmelCase      	=		{
            '''class_size''': len(_a					),
            '''embed_size''': discriminator.embed_size,
            '''pretrained_model''': pretrained_model,
            '''class_vocab''': classaidx,
            '''default_class''': 1,
        }

    elif dataset == "toxic":
        UpperCAmelCase      	=		['''non_toxic''', '''toxic''']
        UpperCAmelCase      	=		{c: i for i, c in enumerate(_a					)}

        UpperCAmelCase      	=		Discriminator(
            class_size=len(_a					)   ,       pretrained_model=_a   ,       cached_mode=_a   ,       device=_a					).to(_a					)

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

                    if len(_a					) < max_length_seq:
                        UpperCAmelCase      	=		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

        UpperCAmelCase      	=		Dataset(_a   ,       _a					)
        UpperCAmelCase      	=		int(0.9 * len(_a					)					)
        UpperCAmelCase      	=		len(_a					) - train_size
        UpperCAmelCase	,					UpperCAmelCase      	=		torch.utils.data.random_split(_a   ,       [train_size, test_size]					)

        UpperCAmelCase      	=		{
            '''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.'''					)

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

        UpperCAmelCase      	=		sorted(_a					)
        UpperCAmelCase      	=		{c: i for i, c in enumerate(_a					)}

        UpperCAmelCase      	=		Discriminator(
            class_size=len(_a					)   ,       pretrained_model=_a   ,       cached_mode=_a   ,       device=_a					).to(_a					)

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

                    try:
                        UpperCAmelCase      	=		discriminator.tokenizer.encode(_a					)
                        if len(_a					) < max_length_seq:
                            UpperCAmelCase      	=		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

        UpperCAmelCase      	=		Dataset(_a   ,       _a					)
        UpperCAmelCase      	=		int(0.9 * len(_a					)					)
        UpperCAmelCase      	=		len(_a					) - train_size
        UpperCAmelCase	,					UpperCAmelCase      	=		torch.utils.data.random_split(_a   ,       [train_size, test_size]					)

        UpperCAmelCase      	=		{
            '''class_size''': len(_a					),
            '''embed_size''': discriminator.embed_size,
            '''pretrained_model''': pretrained_model,
            '''class_vocab''': classaidx,
            '''default_class''': 0,
        }

    UpperCAmelCase      	=		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...'''					)

        UpperCAmelCase      	=		time.time()

        UpperCAmelCase      	=		get_cached_data_loader(_a   ,       _a   ,       _a   ,       shuffle=_a   ,       device=_a					)

        UpperCAmelCase      	=		get_cached_data_loader(_a   ,       _a   ,       _a   ,       device=_a					)

        UpperCAmelCase      	=		time.time()
        print('''Building representation cache took: {:.3f}s'''.format(end - start					)					)

    else:
        UpperCAmelCase      	=		torch.utils.data.DataLoader(
            dataset=_a   ,       batch_size=_a   ,       shuffle=_a   ,       collate_fn=_a					)
        UpperCAmelCase      	=		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					)

    UpperCAmelCase      	=		optim.Adam(discriminator.parameters()   ,       lr=0.0001					)

    for epoch in range(_a					):
        UpperCAmelCase      	=		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					)

        UpperCAmelCase      	=		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__":
   A			   =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')
   A			   =parser.parse_args()

   train_discriminator(**(vars(args)))










'''simple docstring'''







import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


@require_tokenizers
class       _a  (     unittest.TestCase			):
       __a    :					Optional[int]           = EsmTokenizer
       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase      	=		tempfile.mkdtemp()
           # fmt: off
           UpperCAmelCase      	=		["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
           # fmt: on
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           with open(self.vocab_file       ,    '''w'''       ,    encoding='''utf-8'''    ) as vocab_writer:
               vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )
       def    A				(     self						:     Union[str, Any]       ,    **lowercase						:     Tuple    ):



           '''simple docstring'''

           return [self.get_tokenizer(**lowercase    )]
       def    A				(     self						:     Optional[Any]       ,    **lowercase						:     str    ):



           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **lowercase    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		tokenizer.tokenize('''LAGVS'''    )
           self.assertListEqual(lowercase       ,    ['''L''', '''A''', '''G''', '''V''', '''S''']    )
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    [4, 5, 6, 7, 8]    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		'''LAGVS'''
           self.assertListEqual(tokenizer.encode(lowercase    )       ,    [0, 4, 5, 6, 7, 8, 2]    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		['''LAGVS''', '''WCB''']
           UpperCAmelCase      	=		tokenizer(lowercase       ,    padding=lowercase    )['''input_ids''']

           self.assertListEqual(lowercase       ,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]    )
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		['''LAGVS''', '''WCB''']
           UpperCAmelCase      	=		tokenizer(lowercase       ,    padding=lowercase    )['''input_ids''']

           self.assertListEqual(lowercase       ,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]    )







       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_tokenizers(fast=lowercase    )
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"    ):
                   UpperCAmelCase      	=		'''<unk>'''
                   UpperCAmelCase      	=		'''<mask>'''

                   UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
                   UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )

                   self.assertEqual(len(lowercase    )       ,    1    )
                   self.assertEqual(len(lowercase    )       ,    1    )
                   self.assertEqual(token_a[0]       ,    lowercase    )
                   self.assertEqual(token_a[0]       ,    lowercase    )




