
'''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)
lowerCAmelCase_      =   1E-10
lowerCAmelCase_      =   "This is incredible! I love it, this is the best chicken I have ever had."
lowerCAmelCase_      =   1_0_0







class 		snake_case_       (						nn.Module						):



   """simple docstring"""





   def __init__( self			,   UpperCamelCase			,   UpperCamelCase="gpt2-medium"			,   UpperCamelCase=False			,   UpperCamelCase="cpu"):
          super().__init__()
          lowerCamelCase__				   =      GPTaTokenizer.from_pretrained(UpperCamelCase)
          lowerCamelCase__				   =      GPTaLMHeadModel.from_pretrained(UpperCamelCase)
          lowerCamelCase__				   =      self.encoder.transformer.config.hidden_size
          lowerCamelCase__				   =      ClassificationHead(class_size=UpperCamelCase			,   embed_size=self.embed_size)
          lowerCamelCase__				   =      cached_mode
          lowerCamelCase__				   =      device







   def     __UpperCAmelCase      ( self):
          return self.classifier_head







   def     __UpperCAmelCase      ( self):
          for param in self.encoder.parameters():
                 lowerCamelCase__				   =      False
          self.classifier_head.train()







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      x.ne(0).unsqueeze(2).repeat(1			,   1			,   self.embed_size).float().to(self.device).detach()
          lowerCamelCase__				   =      self.encoder.transformer(UpperCamelCase)["last_hidden_state"]
          lowerCamelCase__				   =      hidden * mask
          lowerCamelCase__				   =      torch.sum(UpperCamelCase			,   dim=1) / (torch.sum(UpperCamelCase			,   dim=1).detach() + EPSILON)
          return avg_hidden







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          if self.cached_mode:
                 lowerCamelCase__				   =      x.to(self.device)
          else:
                 lowerCamelCase__				   =      self.avg_representation(x.to(self.device))

          lowerCamelCase__				   =      self.classifier_head(UpperCamelCase)
          lowerCamelCase__				   =      nn.functional.log_softmax(UpperCamelCase			,   dim=-1)

          return probs







class 		snake_case_       (						data.Dataset						):



   """simple docstring"""





   def __init__( self			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      X
          lowerCamelCase__				   =      y







   def __len__( self):
          return len(self.X)







   def __getitem__( self			,   UpperCamelCase):
          lowerCamelCase__				   =      {}
          lowerCamelCase__				   =      self.X[index]
          lowerCamelCase__				   =      self.y[index]
          return data







def   lowerCAmelCase(  a__    :  List[Any]      ):







       '''simple docstring'''






       def pad_sequences(a__    :  Any      ):
              lowerCamelCase__				   =      [len(a__      ) for seq in sequences]

              lowerCamelCase__				   =      torch.zeros(len(a__      )      , max(a__      )      ).long()  # padding value = 0

              for i, seq in enumerate(a__      ):
                     lowerCamelCase__				   =      lengths[i]
                     lowerCamelCase__				   =      seq[:end]

              return padded_sequences, lengths

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

       lowerCamelCase__   ,		lowerCamelCase__				   =      pad_sequences(item_info["X"]      )
       lowerCamelCase__				   =      torch.tensor(item_info["y"]      , dtype=torch.long      )

       return x_batch, y_batch







def   lowerCAmelCase(  a__    :  List[Any]      ):







       '''simple docstring'''






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

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

       return x_batch, y_batch







def   lowerCAmelCase(  a__    :  Dict      , a__    :  Dict      , a__    :  int      , a__    :  str=0      , a__    :  str=10      , a__    :  Dict="cpu"      ):







       '''simple docstring'''






       lowerCamelCase__				   =      0
       discriminator.train_custom()
       for batch_idx, (input_t, target_t) in enumerate(a__      ):
              lowerCamelCase__   ,		lowerCamelCase__				   =      input_t.to(a__      ), target_t.to(a__      )

              optimizer.zero_grad()

              lowerCamelCase__				   =      discriminator(a__      )
              lowerCamelCase__				   =      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      )      , 100 * samples_so_far / len(data_loader.dataset      )      , loss.item()      , )      )







def   lowerCAmelCase(  a__    :  Any      , a__    :  str      , a__    :  Tuple="cpu"      ):







       '''simple docstring'''






       discriminator.eval()
       lowerCamelCase__				   =      0
       lowerCamelCase__				   =      0
       with torch.no_grad():
              for input_t, target_t in data_loader:
                     lowerCamelCase__   ,		lowerCamelCase__				   =      input_t.to(a__      ), target_t.to(a__      )
                     lowerCamelCase__				   =      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
                     lowerCamelCase__				   =      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      )      , 1_0_0.0 * correct / len(data_loader.dataset      )      )      )







def   lowerCAmelCase(  a__    :  Dict      , a__    :  Optional[int]      , a__    :  Union[str, Any]      , a__    :  Any=False      , a__    :  Optional[int]="cpu"      ):







       '''simple docstring'''






       lowerCamelCase__				   =      model.tokenizer.encode(a__      )
       lowerCamelCase__				   =      torch.tensor([input_t]      , dtype=torch.long      , device=a__      )
       if cached:
              lowerCamelCase__				   =      model.avg_representation(a__      )

       lowerCamelCase__				   =      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   lowerCAmelCase(  a__    :  Tuple      , a__    :  Optional[Any]      , a__    :  Dict      , a__    :  Dict=False      , a__    :  str="cpu"      ):







       '''simple docstring'''






       lowerCamelCase__				   =      torch.utils.data.DataLoader(dataset=a__      , batch_size=a__      , collate_fn=a__      )

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

       lowerCamelCase__				   =      torch.utils.data.DataLoader(
           dataset=Dataset(a__      , a__      )      , batch_size=a__      , shuffle=a__      , collate_fn=a__      )

       return data_loader







def   lowerCAmelCase(  a__    :  str      , a__    :  int=None      , a__    :  str="gpt2-medium"      , a__    :  Union[str, Any]=10      , a__    :  List[Any]=64      , a__    :  Optional[int]=10      , a__    :  Tuple=False      , a__    :  Dict=False      , a__    :  List[str]=False      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

       print("Preprocessing {} dataset...".format(a__      )      )
       lowerCamelCase__				   =      time.time()

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

              lowerCamelCase__				   =      Discriminator(
                  class_size=len(a__      )      , pretrained_model=a__      , cached_mode=a__      , device=a__      ).to(a__      )

              lowerCamelCase__				   =      torchtext_data.Field()
              lowerCamelCase__				   =      torchtext_data.Field(sequential=a__      )
              lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      datasets.SST.splits(
                  a__      , a__      , fine_grained=a__      , train_subtrees=a__      , )

              lowerCamelCase__				   =      []
              lowerCamelCase__				   =      []
              for i in trange(len(a__      )      , ascii=a__      ):
                     lowerCamelCase__				   =      TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["text"]      )
                     lowerCamelCase__				   =      discriminator.tokenizer.encode(a__      )
                     lowerCamelCase__				   =      torch.tensor([5_0256] + seq      , device=a__      , dtype=torch.long      )
                     x.append(a__      )
                     y.append(classaidx[vars(train_data[i]      )["label"]]      )
              lowerCamelCase__				   =      Dataset(a__      , a__      )

              lowerCamelCase__				   =      []
              lowerCamelCase__				   =      []
              for i in trange(len(a__      )      , ascii=a__      ):
                     lowerCamelCase__				   =      TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["text"]      )
                     lowerCamelCase__				   =      discriminator.tokenizer.encode(a__      )
                     lowerCamelCase__				   =      torch.tensor([5_0256] + seq      , device=a__      , dtype=torch.long      )
                     test_x.append(a__      )
                     test_y.append(classaidx[vars(test_data[i]      )["label"]]      )
              lowerCamelCase__				   =      Dataset(a__      , a__      )

              lowerCamelCase__				   =      {
                  "class_size": len(a__      ),
                  "embed_size": discriminator.embed_size,
                  "pretrained_model": pretrained_model,
                  "class_vocab": classaidx,
                  "default_class": 2,
              }

       elif dataset == "clickbait":
              lowerCamelCase__				   =      ["non_clickbait", "clickbait"]
              lowerCamelCase__				   =      {c: i for i, c in enumerate(a__      )}

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

                                   if len(a__      ) < max_length_seq:
                                          lowerCamelCase__				   =      torch.tensor([5_0256] + 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

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

              lowerCamelCase__				   =      {
                  "class_size": len(a__      ),
                  "embed_size": discriminator.embed_size,
                  "pretrained_model": pretrained_model,
                  "class_vocab": classaidx,
                  "default_class": 1,
              }

       elif dataset == "toxic":
              lowerCamelCase__				   =      ["non_toxic", "toxic"]
              lowerCamelCase__				   =      {c: i for i, c in enumerate(a__      )}

              lowerCamelCase__				   =      Discriminator(
                  class_size=len(a__      )      , pretrained_model=a__      , cached_mode=a__      , device=a__      ).to(a__      )

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

                                   if len(a__      ) < max_length_seq:
                                          lowerCamelCase__				   =      torch.tensor([5_0256] + 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

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

              lowerCamelCase__				   =      {
                  "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."      )

              lowerCamelCase__				   =      set()
              with open(a__      ) as f:
                     lowerCamelCase__				   =      csv.reader(a__      , delimiter="\t"      )
                     for row in tqdm(a__      , ascii=a__      ):
                            if row:
                                   classes.add(row[0]      )

              lowerCamelCase__				   =      sorted(a__      )
              lowerCamelCase__				   =      {c: i for i, c in enumerate(a__      )}

              lowerCamelCase__				   =      Discriminator(
                  class_size=len(a__      )      , pretrained_model=a__      , cached_mode=a__      , device=a__      ).to(a__      )

              lowerCamelCase__				   =      []
              lowerCamelCase__				   =      []
              with open(a__      ) as f:
                     lowerCamelCase__				   =      csv.reader(a__      , delimiter="\t"      )
                     for i, row in enumerate(tqdm(a__      , ascii=a__      )      ):
                            if row:
                                   lowerCamelCase__				   =      row[0]
                                   lowerCamelCase__				   =      row[1]

                                   try:
                                          lowerCamelCase__				   =      discriminator.tokenizer.encode(a__      )
                                          if len(a__      ) < max_length_seq:
                                                 lowerCamelCase__				   =      torch.tensor([5_0256] + 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

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

              lowerCamelCase__				   =      {
                  "class_size": len(a__      ),
                  "embed_size": discriminator.embed_size,
                  "pretrained_model": pretrained_model,
                  "class_vocab": classaidx,
                  "default_class": 0,
              }

       lowerCamelCase__				   =      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..."      )

              lowerCamelCase__				   =      time.time()

              lowerCamelCase__				   =      get_cached_data_loader(a__      , a__      , a__      , shuffle=a__      , device=a__      )

              lowerCamelCase__				   =      get_cached_data_loader(a__      , a__      , a__      , device=a__      )

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

       else:
              lowerCamelCase__				   =      torch.utils.data.DataLoader(
                  dataset=a__      , batch_size=a__      , shuffle=a__      , collate_fn=a__      )
              lowerCamelCase__				   =      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__      )

       lowerCamelCase__				   =      optim.Adam(discriminator.parameters()      , lr=0.0_0_0_1      )

       for epoch in range(a__      ):
              lowerCamelCase__				   =      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__      )

              lowerCamelCase__				   =      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_      =   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=1_0, metavar="N", help="Number of training epochs")
    parser.add_argument(
        "--batch_size", type=int, default=6_4, metavar="N", help="input batch size for training (default: 64)"
    )
    parser.add_argument(
        "--log_interval",
        type=int,
        default=1_0,
        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_      =   parser.parse_args()

    train_discriminator(**(vars(args)))



'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		snake_case_       (						A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							Optional[Any]								=HerbertTokenizer
   __lowerCAmelCase	:							Optional[Any]								=HerbertTokenizerFast
   __lowerCAmelCase	:							int								=True







   def     __UpperCAmelCase      ( self):
          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(f"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"""			,   encoding="utf-8") as f_data:
                 lowerCamelCase__				   =      f_data.read().replace("\n\n"			,   "\n").strip()

          lowerCamelCase__				   =      [
              "<s>",
              "</s>",
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "w</w>",
              "r</w>",
              "t</w>",
              "lo",
              "low",
              "er</w>",
              "low</w>",
              "lowest</w>",
              "newer</w>",
              "wider</w>",
              ",</w>",
              "<unk>",
          ]
          lowerCamelCase__				   =      dict(zip(UpperCamelCase			,   range(len(UpperCamelCase))))
          lowerCamelCase__				   =      ["l o 123", "lo w 1456", "e r</w> 1789", ""]

          lowerCamelCase__				   =      os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES["vocab_file"])
          lowerCamelCase__				   =      os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES["merges_file"])
          with open(self.vocab_file			,   "w") as fp:
                 fp.write(json.dumps(UpperCamelCase))
          with open(self.merges_file			,   "w") as fp:
                 fp.write("\n".join(UpperCamelCase))







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      "lower newer"
          lowerCamelCase__				   =      "lower newer"
          return input_text, output_text







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.tokenizer_class(vocab_file=self.vocab_file			,   merges_file=self.merges_file)

          lowerCamelCase__				   =      "lower"
          lowerCamelCase__				   =      ["low", "er</w>"]
          lowerCamelCase__				   =      tokenizer.tokenize(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      tokens + ["<unk>"]
          lowerCamelCase__				   =      [16, 17, 23]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase)			,   UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          if not self.test_rust_tokenizer:
                 return

          lowerCamelCase__				   =      self.get_tokenizer()
          lowerCamelCase__				   =      self.get_rust_tokenizer()

          lowerCamelCase__				   =      "lower,newer"

          lowerCamelCase__				   =      tokenizer.tokenize(UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.tokenize(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      tokenizer.encode(UpperCamelCase			,   add_special_tokens=UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.encode(UpperCamelCase			,   add_special_tokens=UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      self.get_rust_tokenizer()
          lowerCamelCase__				   =      tokenizer.encode(UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.encode(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)







   @slow
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.tokenizer_class.from_pretrained("allegro/herbert-base-cased")

          lowerCamelCase__				   =      tokenizer.encode("konstruowanie sekwencji"			,   add_special_tokens=UpperCamelCase)
          lowerCamelCase__				   =      tokenizer.encode("konstruowanie wielu sekwencji"			,   add_special_tokens=UpperCamelCase)

          lowerCamelCase__				   =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase)
          lowerCamelCase__				   =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase			,   UpperCamelCase)

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]

