code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  lowerCAmelCase					 =	{
      '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							Dict ='''mbart'''
  							UpperCamelCase_     :							Any =['''past_key_values''']
  							UpperCamelCase_     :							Union[str, Any] ={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  							def __init__(self   ,       lowerCAmelCase=5_0_2_6_5   ,       lowerCAmelCase=1_0_2_4   ,       lowerCAmelCase=1_2   ,       lowerCAmelCase=4_0_9_6   ,       lowerCAmelCase=1_6   ,       lowerCAmelCase=1_2   ,       lowerCAmelCase=4_0_9_6   ,       lowerCAmelCase=1_6   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=True   ,       lowerCAmelCase=True   ,       lowerCAmelCase="gelu"   ,       lowerCAmelCase=1_0_2_4   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.02   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=False   ,       lowerCAmelCase=1   ,       lowerCAmelCase=0   ,       lowerCAmelCase=2   ,       lowerCAmelCase=2   ,       **lowerCAmelCase   ,       ):
  									__lowercase=    vocab_size
  									__lowercase=    max_position_embeddings
  									__lowercase=    d_model
  									__lowercase=    encoder_ffn_dim
  									__lowercase=    encoder_layers
  									__lowercase=    encoder_attention_heads
  									__lowercase=    decoder_ffn_dim
  									__lowercase=    decoder_layers
  									__lowercase=    decoder_attention_heads
  									__lowercase=    dropout
  									__lowercase=    attention_dropout
  									__lowercase=    activation_dropout
  									__lowercase=    activation_function
  									__lowercase=    init_std
  									__lowercase=    encoder_layerdrop
  									__lowercase=    decoder_layerdrop
  									__lowercase=    classifier_dropout
  									__lowercase=    use_cache
  									__lowercase=    encoder_layers
  									__lowercase=    scale_embedding  # scale factor will be sqrt(d_model) if True
  									super().__init__(
  									    pad_token_id=lowerCAmelCase   ,       bos_token_id=lowerCAmelCase   ,       eos_token_id=lowerCAmelCase   ,       is_encoder_decoder=lowerCAmelCase   ,       forced_eos_token_id=lowerCAmelCase   ,       **lowerCAmelCase   ,       )
  
  
  
  
  
  class    A					(     A_				):
  
  
  							@property
  							def     _A (self      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											    ]      )
  
  											if self.use_past:
  													__lowercase=    {0: 'batch'}
  													__lowercase=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
  											else:
  													__lowercase=    {0: 'batch', 1: 'decoder_sequence'}
  													__lowercase=    {0: 'batch', 1: 'decoder_sequence'}
  
  											if self.use_past:
  													self.fill_with_past_key_values_(lowerCAmelCase   ,       direction='inputs'      )
  									elif self.task == "causal-lm":
  											# TODO: figure this case out.
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											    ]      )
  											if self.use_past:
  													__lowercase,							__lowercase=    self.num_layers
  													for i in range(lowerCAmelCase      ):
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  									else:
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
  											        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
  											    ]      )
  
  									return common_inputs
  
  
  							@property
  							def     _A (self      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    super().outputs
  									else:
  											__lowercase=    super(lowerCAmelCase   ,       self      ).outputs
  											if self.use_past:
  													__lowercase,							__lowercase=    self.num_layers
  													for i in range(lowerCAmelCase      ):
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  									return common_outputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  									# Generate decoder inputs
  									__lowercase=    seq_length if not self.use_past else 1
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									__lowercase=    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
  									__lowercase=    dict(**lowerCAmelCase   ,       **lowerCAmelCase      )
  
  									if self.use_past:
  											if not is_torch_available():
  													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
  											else:
  													import torch
  											__lowercase,							__lowercase=    common_inputs['input_ids'].shape
  											__lowercase=    common_inputs['decoder_input_ids'].shape[1]
  											__lowercase,							__lowercase=    self.num_attention_heads
  											__lowercase=    (
  											    batch,
  											    num_encoder_attention_heads,
  											    encoder_seq_length,
  											    self._config.hidden_size // num_encoder_attention_heads,
  											)
  											__lowercase=    decoder_seq_length + 3
  											__lowercase=    (
  											    batch,
  											    num_decoder_attention_heads,
  											    decoder_past_length,
  											    self._config.hidden_size // num_decoder_attention_heads,
  											)
  
  											__lowercase=    torch.cat(
  											    [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase   ,       lowerCAmelCase      )]   ,       dim=1      )
  
  											__lowercase=    []
  											# If the number of encoder and decoder layers are present in the model configuration, both are considered
  											__lowercase,							__lowercase=    self.num_layers
  											__lowercase=    min(lowerCAmelCase   ,       lowerCAmelCase      )
  											__lowercase=    max(lowerCAmelCase   ,       lowerCAmelCase      ) - min_num_layers
  											__lowercase=    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
  
  											for _ in range(lowerCAmelCase      ):
  													common_inputs["past_key_values"].append(
  													    (
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													    )      )
  											# TODO: test this.
  											__lowercase=    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
  											for _ in range(lowerCAmelCase   ,       lowerCAmelCase      ):
  													common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase      ), torch.zeros(lowerCAmelCase      ))      )
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  									if self.use_past:
  											if not is_torch_available():
  													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
  											else:
  													import torch
  											__lowercase,							__lowercase=    common_inputs['input_ids'].shape
  											# Not using the same length for past_key_values
  											__lowercase=    seqlen + 2
  											__lowercase,							__lowercase=    self.num_layers
  											__lowercase,							__lowercase=    self.num_attention_heads
  											__lowercase=    (
  											    batch,
  											    num_encoder_attention_heads,
  											    past_key_values_length,
  											    self._config.hidden_size // num_encoder_attention_heads,
  											)
  
  											__lowercase=    common_inputs['attention_mask'].dtype
  											__lowercase=    torch.cat(
  											    [common_inputs['attention_mask'], torch.ones(lowerCAmelCase   ,       lowerCAmelCase   ,       dtype=lowerCAmelCase      )]   ,       dim=1      )
  											__lowercase=    [
  											    (torch.zeros(lowerCAmelCase      ), torch.zeros(lowerCAmelCase      )) for _ in range(lowerCAmelCase      )
  											]
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									# Copied from OnnxConfig.generate_dummy_inputs
  									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
  									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
  									__lowercase=    compute_effective_axis_dimension(
  									    lowerCAmelCase   ,       fixed_dimension=OnnxConfig.default_fixed_batch   ,       num_token_to_add=0      )
  
  									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
  									__lowercase=    tokenizer.num_special_tokens_to_add(lowerCAmelCase      )
  									__lowercase=    compute_effective_axis_dimension(
  									    lowerCAmelCase   ,       fixed_dimension=OnnxConfig.default_fixed_sequence   ,       num_token_to_add=lowerCAmelCase      )
  
  									# Generate dummy inputs according to compute batch and sequence
  									__lowercase=    [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
  									__lowercase=    dict(tokenizer(lowerCAmelCase   ,       return_tensors=lowerCAmelCase      )      )
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  
  									elif self.task == "causal-lm":
  											__lowercase=    self._generate_dummy_inputs_for_causal_lm(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  									else:
  											__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  
  									return common_inputs
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    super()._flatten_past_key_values_(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									else:
  											__lowercase=    super(lowerCAmelCase   ,       self      )._flatten_past_key_values_(
  											    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  
  
  
  
  
style_context: 
  
  
  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    A					(     A_      ,       unittest.TestCase				):
  							UpperCamelCase_     :							Tuple =ElectraTokenizer
  							UpperCamelCase_     :							Tuple =ElectraTokenizerFast
  							UpperCamelCase_     :							Any =True
  							UpperCamelCase_     :							List[str] =True
  							UpperCamelCase_     :							Any =filter_non_english
  
  
  							def     _A (self      ):
  									super().setUp()
  
  									__lowercase=    [
  									    '[UNK]',
  									    '[CLS]',
  									    '[SEP]',
  									    '[PAD]',
  									    '[MASK]',
  									    'want',
  									    '##want',
  									    '##ed',
  									    'wa',
  									    'un',
  									    'runn',
  									    '##ing',
  									    ',',
  									    'low',
  									    'lowest',
  									]
  									__lowercase=    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   ,       lowerCAmelCase      ):
  									__lowercase=    'UNwant\u00E9d,running'
  									__lowercase=    'unwanted, running'
  									return input_text, output_text
  
  
  							def     _A (self      ):
  									__lowercase=    self.tokenizer_class(self.vocab_file      )
  
  									__lowercase=    tokenizer.tokenize('UNwant\u00E9d,running'      )
  									self.assertListEqual(lowerCAmelCase   ,       ['un', '##want', '##ed', ',', 'runn', '##ing']      )
  									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase      )   ,       [9, 6, 7, 1_2, 1_0, 1_1]      )
  
  
  							def     _A (self      ):
  									if not self.test_rust_tokenizer:
  											return
  
  									__lowercase=    self.get_tokenizer()
  									__lowercase=    self.get_rust_tokenizer()
  
  									__lowercase=    'UNwant\u00E9d,running'
  
  									__lowercase=    tokenizer.tokenize(lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.tokenize(lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									__lowercase=    tokenizer.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									__lowercase=    self.get_rust_tokenizer()
  									__lowercase=    tokenizer.encode(lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.encode(lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									# With lower casing
  									__lowercase=    self.get_tokenizer(do_lower_case=lowerCAmelCase      )
  									__lowercase=    self.get_rust_tokenizer(do_lower_case=lowerCAmelCase      )
  
  									__lowercase=    'UNwant\u00E9d,running'
  
  									__lowercase=    tokenizer.tokenize(lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.tokenize(lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									__lowercase=    tokenizer.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									__lowercase=    self.get_rust_tokenizer()
  									__lowercase=    tokenizer.encode(lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.encode(lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer()
  
  									self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'      )   ,       ['ah', '\u535A', '\u63A8', 'zz']      )
  
  
  							def     _A (self      ):
  									__lowercase=    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     _A (self      ):
  									__lowercase=    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     _A (self      ):
  									__lowercase=    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     _A (self      ):
  									__lowercase=    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     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )   ,       ['HeLLo', '!', 'how', 'Are', 'yoU', '?']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase   ,       strip_accents=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )   ,       ['HäLLo', '!', 'how', 'Are', 'yoU', '?']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase   ,       strip_accents=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )   ,       ['HaLLo', '!', 'how', 'Are', 'yoU', '?']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase   ,       never_split=['[UNK]']      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'      )   ,       ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']      )
  
  
  							def     _A (self      ):
  									__lowercase=    ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
  
  									__lowercase=    {}
  									for i, token in enumerate(lowerCAmelCase      ):
  											__lowercase=    i
  									__lowercase=    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     _A (self      ):
  									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     _A (self      ):
  									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     _A (self      ):
  									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     _A (self      ):
  									__lowercase=    self.get_tokenizer()
  									__lowercase=    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     _A (self      ):
  									__lowercase=    self.tokenizer_class.from_pretrained('google/electra-base-discriminator'      )
  
  									__lowercase=    tokenizer.encode('sequence builders'   ,       add_special_tokens=lowerCAmelCase      )
  									__lowercase=    tokenizer.encode('multi-sequence build'   ,       add_special_tokens=lowerCAmelCase      )
  
  									__lowercase=    tokenizer.build_inputs_with_special_tokens(lowerCAmelCase      )
  									__lowercase=    tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									assert encoded_sentence == [1_0_1] + text + [1_0_2]
  									assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]
  
  
  							def     _A (self      ):
  									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
  													__lowercase=    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  
  													__lowercase=    f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
  													__lowercase=    tokenizer_r.encode_plus(
  													    lowerCAmelCase   ,       return_attention_mask=lowerCAmelCase   ,       return_token_type_ids=lowerCAmelCase   ,       return_offsets_mapping=lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase   ,       )
  
  													__lowercase=    tokenizer_r.do_lower_case if hasattr(lowerCAmelCase   ,       'do_lower_case'      ) else False
  													__lowercase=    (
  													    [
  													        ((0, 0), tokenizer_r.cls_token),
  													        ((0, 1), 'A'),
  													        ((1, 2), ','),
  													        ((3, 5), 'na'),
  													        ((5, 6), '##ï'),
  													        ((6, 8), '##ve'),
  													        ((9, 1_5), tokenizer_r.mask_token),
  													        ((1_6, 2_1), 'Allen'),
  													        ((2_1, 2_3), '##NL'),
  													        ((2_3, 2_4), '##P'),
  													        ((2_5, 3_3), 'sentence'),
  													        ((3_3, 3_4), '.'),
  													        ((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, 1_5), tokenizer_r.mask_token),
  													        ((1_6, 2_1), 'allen'),
  													        ((2_1, 2_3), '##nl'),
  													        ((2_3, 2_4), '##p'),
  													        ((2_5, 3_3), 'sentence'),
  													        ((3_3, 3_4), '.'),
  													        ((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     _A (self      ):
  									__lowercase=    ['的', '人', '有']
  									__lowercase=    ''.join(lowerCAmelCase      )
  									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
  													__lowercase=    True
  													__lowercase=    self.tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  													__lowercase=    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  
  													__lowercase=    tokenizer_p.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  													__lowercase=    tokenizer_r.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  
  													__lowercase=    tokenizer_r.convert_ids_to_tokens(lowerCAmelCase      )
  													__lowercase=    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      )
  
  													__lowercase=    False
  													__lowercase=    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  													__lowercase=    self.tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  
  													__lowercase=    tokenizer_r.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  													__lowercase=    tokenizer_p.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  
  													__lowercase=    tokenizer_r.convert_ids_to_tokens(lowerCAmelCase      )
  													__lowercase=    tokenizer_p.convert_ids_to_tokens(lowerCAmelCase      )
  
  													# it is expected that only the first Chinese character is not preceded by "##".
  													__lowercase=    [
  													    f'##{token}' if idx != 0 else token for idx, token in enumerate(lowerCAmelCase      )
  													]
  													self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  													self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  
  
  
  
  