






"""simple docstring"""


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)
lowercase_    					=					1e-10
lowercase_    					=					'This is incredible! I love it, this is the best chicken I have ever had.'
lowercase_    					=					100


class  snake_case		(							nn.Module						):


   '''simple docstring'''

   def __init__(     self			:					Dict,	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Tuple="gpt2-medium",	_lowerCamelCase			:					Optional[Any]=False,	_lowerCamelCase			:					int="cpu"  ):

     '''simple docstring'''
     super().__init__()
     __A						      =			GPTaTokenizer.from_pretrained(_UpperCAmelCase  )
     __A						      =			GPTaLMHeadModel.from_pretrained(_UpperCAmelCase  )
     __A						      =			self.encoder.transformer.config.hidden_size
     __A						      =			ClassificationHead(class_size=_UpperCAmelCase,	embed_size=self.embed_size  )
     __A						      =			cached_mode
     __A						      =			device


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     return self.classifier_head


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     for param in self.encoder.parameters():
       __A						      =			False
     self.classifier_head.train()


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					List[str]  ):

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






   def     _SCREAMING_SNAKE_CASE    (     self			:					int,	_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     if self.cached_mode:
       __A						      =			x.to(self.device  )
     else:
       __A						      =			self.avg_representation(x.to(self.device  )  )

     __A						      =			self.classifier_head(_UpperCAmelCase  )
     __A						      =			nn.functional.log_softmax(_UpperCAmelCase,	dim=-1  )

     return probs





class  snake_case		(							data.Dataset						):


   '''simple docstring'''

   def __init__(     self			:					int,	_lowerCamelCase			:					int,	_lowerCamelCase			:					Optional[int]  ):

     '''simple docstring'''
     __A						      =			X
     __A						      =			y


   def __len__(     self			:					Any  ):

     '''simple docstring'''
     return len(self.X  )






   def __getitem__(     self			:					Any,	_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			{}
     __A						      =			self.X[index]
     __A						      =			self.y[index]
     return data





def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  def pad_sequences(__UpperCamelCase			):
    __A						      =			[len(snake_case__			) for seq in sequences]

    __A						      =			torch.zeros(len(snake_case__			)		,	max(snake_case__			)			).long()  # padding value = 0

    for i, seq in enumerate(snake_case__			):
      __A						      =			lengths[i]
      __A						      =			seq[:end]

    return padded_sequences, lengths

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

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

  return x_batch, y_batch



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







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

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

  return x_batch, y_batch



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase=0		,	__UpperCamelCase=1_0		,	__UpperCamelCase="cpu"			):




  """simple docstring"""







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

    optimizer.zero_grad()

    __A						      =			discriminator(snake_case__			)
    __A						      =			nn.functional.nll_loss(snake_case__		,	snake_case__			)
    loss.backward(retain_graph=snake_case__			)
    optimizer.step()

    samples_so_far += len(snake_case__			)

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



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase="cpu"			):




  """simple docstring"""







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

  test_loss /= len(data_loader.dataset			)

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



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase=False		,	__UpperCamelCase="cpu"			):




  """simple docstring"""







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

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



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase=False		,	__UpperCamelCase="cpu"			):




  """simple docstring"""







  __A						      =			torch.utils.data.DataLoader(dataset=snake_case__		,	batch_size=snake_case__		,	collate_fn=snake_case__			)

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

  __A						      =			torch.utils.data.DataLoader(
      dataset=Dataset(snake_case__		,	snake_case__			)		,	batch_size=snake_case__		,	shuffle=snake_case__		,	collate_fn=snake_case__			)

  return data_loader



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase=None		,	__UpperCamelCase="gpt2-medium"		,	__UpperCamelCase=1_0		,	__UpperCamelCase=6_4		,	__UpperCamelCase=1_0		,	__UpperCamelCase=False		,	__UpperCamelCase=False		,	__UpperCamelCase=False		,	):




  """simple docstring"""







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

  print('''Preprocessing {} dataset...'''.format(snake_case__			)			)
  __A						      =			time.time()

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

    __A						      =			Discriminator(
        class_size=len(snake_case__			)		,	pretrained_model=snake_case__		,	cached_mode=snake_case__		,	device=snake_case__			).to(snake_case__			)

    __A						      =			torchtext_data.Field()
    __A						      =			torchtext_data.Field(sequential=snake_case__			)
    __A						      =			datasets.SST.splits(
        snake_case__		,	snake_case__		,	fine_grained=snake_case__		,	train_subtrees=snake_case__		,	)

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

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

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

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

    __A						      =			Discriminator(
        class_size=len(snake_case__			)		,	pretrained_model=snake_case__		,	cached_mode=snake_case__		,	device=snake_case__			).to(snake_case__			)

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

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

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

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

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

    __A						      =			Discriminator(
        class_size=len(snake_case__			)		,	pretrained_model=snake_case__		,	cached_mode=snake_case__		,	device=snake_case__			).to(snake_case__			)

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

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

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

    __A						      =			{
        "class_size": len(snake_case__			),
        "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.'''			)

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

    __A						      =			sorted(snake_case__			)
    __A						      =			{c: i for i, c in enumerate(snake_case__			)}

    __A						      =			Discriminator(
        class_size=len(snake_case__			)		,	pretrained_model=snake_case__		,	cached_mode=snake_case__		,	device=snake_case__			).to(snake_case__			)

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

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

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

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

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

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

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

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

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

    __A						      =			time.time()

    __A						      =			get_cached_data_loader(snake_case__		,	snake_case__		,	snake_case__		,	shuffle=snake_case__		,	device=snake_case__			)

    __A						      =			get_cached_data_loader(snake_case__		,	snake_case__		,	snake_case__		,	device=snake_case__			)

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

  else:
    __A						      =			torch.utils.data.DataLoader(
        dataset=snake_case__		,	batch_size=snake_case__		,	shuffle=snake_case__		,	collate_fn=snake_case__			)
    __A						      =			torch.utils.data.DataLoader(dataset=snake_case__		,	batch_size=snake_case__		,	collate_fn=snake_case__			)

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

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

  for epoch in range(snake_case__			):
    __A						      =			time.time()
    print('''\nEpoch'''		,	epoch + 1			)

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

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

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

    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(snake_case__		,	epoch + 1			)		,	)


if __name__ == "__main__":
       lowercase_    					=					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')
       lowercase_    					=					parser.parse_args()

       train_discriminator(**(vars(args)))













"""simple docstring"""


import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class  snake_case		(							_lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     str									=			ElectraTokenizer
   A_						:     List[Any]									=			ElectraTokenizerFast
   A_						:     Union[str, Any]									=			True
   A_						:     List[str]									=			True
   A_						:     Optional[Any]									=			filter_non_english


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     super().setUp()

     __A						      =			[
         '''[UNK]''',
         '''[CLS]''',
         '''[SEP]''',
         '''[PAD]''',
         '''[MASK]''',
         '''want''',
         '''##want''',
         '''##ed''',
         '''wa''',
         '''un''',
         '''runn''',
         '''##ing''',
         ''',''',
         '''low''',
         '''lowest''',
     ]
     __A						      =			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     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					int  ):

     '''simple docstring'''
     __A						      =			'''UNwant\u00E9d,running'''
     __A						      =			'''unwanted, running'''
     return input_text, output_text


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.tokenizer_class(self.vocab_file  )

     __A						      =			tokenizer.tokenize('''UNwant\u00E9d,running'''  )
     self.assertListEqual(_lowerCamelCase,	['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase  ),	[9, 6, 7, 12, 10, 11]  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     if not self.test_rust_tokenizer:
       return

     __A						      =			self.get_tokenizer()
     __A						      =			self.get_rust_tokenizer()

     __A						      =			'''UNwant\u00E9d,running'''

     __A						      =			tokenizer.tokenize(_lowerCamelCase  )
     __A						      =			rust_tokenizer.tokenize(_lowerCamelCase  )
     self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )

     __A						      =			tokenizer.encode(_lowerCamelCase,	add_special_tokens=_lowerCamelCase  )
     __A						      =			rust_tokenizer.encode(_lowerCamelCase,	add_special_tokens=_lowerCamelCase  )
     self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )

     __A						      =			self.get_rust_tokenizer()
     __A						      =			tokenizer.encode(_lowerCamelCase  )
     __A						      =			rust_tokenizer.encode(_lowerCamelCase  )
     self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )

     # With lower casing
     __A						      =			self.get_tokenizer(do_lower_case=_lowerCamelCase  )
     __A						      =			self.get_rust_tokenizer(do_lower_case=_lowerCamelCase  )

     __A						      =			'''UNwant\u00E9d,running'''

     __A						      =			tokenizer.tokenize(_lowerCamelCase  )
     __A						      =			rust_tokenizer.tokenize(_lowerCamelCase  )
     self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )

     __A						      =			tokenizer.encode(_lowerCamelCase,	add_special_tokens=_lowerCamelCase  )
     __A						      =			rust_tokenizer.encode(_lowerCamelCase,	add_special_tokens=_lowerCamelCase  )
     self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )

     __A						      =			self.get_rust_tokenizer()
     __A						      =			tokenizer.encode(_lowerCamelCase  )
     __A						      =			rust_tokenizer.encode(_lowerCamelCase  )
     self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer()

     self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''  ),	['''ah''', '''\u535A''', '''\u63A8''', '''zz''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer(do_lower_case=_lowerCamelCase  )

     self.assertListEqual(
         tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  ),	['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
     self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  ),	['''hello''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer(do_lower_case=_lowerCamelCase,	strip_accents=_lowerCamelCase  )

     self.assertListEqual(
         tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ),	['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
     self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  ),	['''h\u00E9llo''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer(do_lower_case=_lowerCamelCase,	strip_accents=_lowerCamelCase  )

     self.assertListEqual(
         tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ),	['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
     self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  ),	['''hello''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer(do_lower_case=_lowerCamelCase  )

     self.assertListEqual(
         tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ),	['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
     self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  ),	['''hello''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer(do_lower_case=_lowerCamelCase  )

     self.assertListEqual(
         tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  ),	['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer(do_lower_case=_lowerCamelCase,	strip_accents=_lowerCamelCase  )

     self.assertListEqual(
         tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ),	['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer(do_lower_case=_lowerCamelCase,	strip_accents=_lowerCamelCase  )

     self.assertListEqual(
         tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ),	['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			BasicTokenizer(do_lower_case=_lowerCamelCase,	never_split=['''[UNK]''']  )

     self.assertListEqual(
         tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''  ),	['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     __A						      =			['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

     __A						      =			{}
     for i, token in enumerate(_lowerCamelCase  ):
       __A						      =			i
     __A						      =			WordpieceTokenizer(vocab=_lowerCamelCase,	unk_token='''[UNK]'''  )

     self.assertListEqual(tokenizer.tokenize(''''''  ),	[]  )

     self.assertListEqual(tokenizer.tokenize('''unwanted running'''  ),	['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']  )

     self.assertListEqual(tokenizer.tokenize('''unwantedX running'''  ),	['''[UNK]''', '''runn''', '''##ing''']  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     self.assertTrue(_is_whitespace(''' '''  )  )
     self.assertTrue(_is_whitespace('''\t'''  )  )
     self.assertTrue(_is_whitespace('''\r'''  )  )
     self.assertTrue(_is_whitespace('''\n'''  )  )
     self.assertTrue(_is_whitespace('''\u00A0'''  )  )

     self.assertFalse(_is_whitespace('''A'''  )  )
     self.assertFalse(_is_whitespace('''-'''  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     self.assertTrue(_is_control('''\u0005'''  )  )

     self.assertFalse(_is_control('''A'''  )  )
     self.assertFalse(_is_control(''' '''  )  )
     self.assertFalse(_is_control('''\t'''  )  )
     self.assertFalse(_is_control('''\r'''  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     self.assertTrue(_is_punctuation('''-'''  )  )
     self.assertTrue(_is_punctuation('''$'''  )  )
     self.assertTrue(_is_punctuation('''`'''  )  )
     self.assertTrue(_is_punctuation('''.'''  )  )

     self.assertFalse(_is_punctuation('''A'''  )  )
     self.assertFalse(_is_punctuation(''' '''  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     __A						      =			self.get_tokenizer()
     __A						      =			self.get_rust_tokenizer()

     # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
     self.assertListEqual([tokenizer.tokenize(_lowerCamelCase  ) for t in ['''Test''', '''\xad''', '''test''']],	[['''[UNK]'''], [], ['''[UNK]''']]  )

     self.assertListEqual(
         [rust_tokenizer.tokenize(_lowerCamelCase  ) for t in ['''Test''', '''\xad''', '''test''']],	[['''[UNK]'''], [], ['''[UNK]''']]  )


   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     __A						      =			self.tokenizer_class.from_pretrained('''google/electra-base-discriminator'''  )

     __A						      =			tokenizer.encode('''sequence builders''',	add_special_tokens=_lowerCamelCase  )
     __A						      =			tokenizer.encode('''multi-sequence build''',	add_special_tokens=_lowerCamelCase  )

     __A						      =			tokenizer.build_inputs_with_special_tokens(_lowerCamelCase  )
     __A						      =			tokenizer.build_inputs_with_special_tokens(_lowerCamelCase,	_lowerCamelCase  )

     assert encoded_sentence == [1_01] + text + [1_02]
     assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'  ):
         __A						      =			self.rust_tokenizer_class.from_pretrained(_lowerCamelCase,	**_lowerCamelCase  )

         __A						      =			f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
         __A						      =			tokenizer_r.encode_plus(
             _lowerCamelCase,	return_attention_mask=_lowerCamelCase,	return_token_type_ids=_lowerCamelCase,	return_offsets_mapping=_lowerCamelCase,	add_special_tokens=_lowerCamelCase,	)

         __A						      =			tokenizer_r.do_lower_case if hasattr(_lowerCamelCase,	'''do_lower_case'''  ) else False
         __A						      =			(
             [
                 ((0, 0), tokenizer_r.cls_token),
                 ((0, 1), '''A'''),
                 ((1, 2), ''','''),
                 ((3, 5), '''na'''),
                 ((5, 6), '''##ï'''),
                 ((6, 8), '''##ve'''),
                 ((9, 15), tokenizer_r.mask_token),
                 ((16, 21), '''Allen'''),
                 ((21, 23), '''##NL'''),
                 ((23, 24), '''##P'''),
                 ((25, 33), '''sentence'''),
                 ((33, 34), '''.'''),
                 ((0, 0), tokenizer_r.sep_token),
             ]
             if not do_lower_case
             else [
                 ((0, 0), tokenizer_r.cls_token),
                 ((0, 1), '''a'''),
                 ((1, 2), ''','''),
                 ((3, 8), '''naive'''),
                 ((9, 15), tokenizer_r.mask_token),
                 ((16, 21), '''allen'''),
                 ((21, 23), '''##nl'''),
                 ((23, 24), '''##p'''),
                 ((25, 33), '''sentence'''),
                 ((33, 34), '''.'''),
                 ((0, 0), tokenizer_r.sep_token),
             ]
         )

         self.assertEqual(
             [e[1] for e in expected_results],	tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']  )  )
         self.assertEqual([e[0] for e in expected_results],	tokens['''offset_mapping''']  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     __A						      =			['''的''', '''人''', '''有''']
     __A						      =			''''''.join(_lowerCamelCase  )
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'  ):
         __A						      =			True
         __A						      =			self.tokenizer_class.from_pretrained(_lowerCamelCase,	**_lowerCamelCase  )
         __A						      =			self.rust_tokenizer_class.from_pretrained(_lowerCamelCase,	**_lowerCamelCase  )

         __A						      =			tokenizer_p.encode(_lowerCamelCase,	add_special_tokens=_lowerCamelCase  )
         __A						      =			tokenizer_r.encode(_lowerCamelCase,	add_special_tokens=_lowerCamelCase  )

         __A						      =			tokenizer_r.convert_ids_to_tokens(_lowerCamelCase  )
         __A						      =			tokenizer_p.convert_ids_to_tokens(_lowerCamelCase  )

         # it is expected that each Chinese character is not preceded by "##"
         self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )
         self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )

         __A						      =			False
         __A						      =			self.rust_tokenizer_class.from_pretrained(_lowerCamelCase,	**_lowerCamelCase  )
         __A						      =			self.tokenizer_class.from_pretrained(_lowerCamelCase,	**_lowerCamelCase  )

         __A						      =			tokenizer_r.encode(_lowerCamelCase,	add_special_tokens=_lowerCamelCase  )
         __A						      =			tokenizer_p.encode(_lowerCamelCase,	add_special_tokens=_lowerCamelCase  )

         __A						      =			tokenizer_r.convert_ids_to_tokens(_lowerCamelCase  )
         __A						      =			tokenizer_p.convert_ids_to_tokens(_lowerCamelCase  )

         # it is expected that only the first Chinese character is not preceded by "##".
         __A						      =			[
             f'##{token}' if idx != 0 else token for idx, token in enumerate(_lowerCamelCase  )
         ]
         self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )
         self.assertListEqual(_lowerCamelCase,	_lowerCamelCase  )




