





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase_    =				logging.getLogger(__name__)



@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     str
			lowerCamelCase_     :     List[str]
			lowerCamelCase_     :     Optional[List[str]]




@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     List[int]
			lowerCamelCase_     :     List[int]
			lowerCamelCase_     :     Optional[List[int]]       			=      None
			lowerCamelCase_     :     Optional[List[int]]       			=      None




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     str       			=      '''train'''
			lowerCamelCase_     :     List[str]       			=      '''dev'''
			lowerCamelCase_     :     List[Any]       			=      '''test'''




class 					__lowerCAmelCase       :






			@staticmethod
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__    )     ->   List[InputExample]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def 			lowerCamelCase  (__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=False					,							__magic_name__="[CLS]"					,							__magic_name__=1					,							__magic_name__="[SEP]"					,							__magic_name__=False					,							__magic_name__=False					,							__magic_name__=0					,							__magic_name__=0					,							__magic_name__=-100					,							__magic_name__=0					,							__magic_name__=True					,							)     ->   List[InputFeatures]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  {label: i for i, label in enumerate(__magic_name__    )}

							snake_case_						:    Dict				=  []
							for ex_index, example in enumerate(__magic_name__    ):
											if ex_index % 1_0000 == 0:
															logger.info('''Writing example %d of %d'''					,							__magic_name__					,							len(__magic_name__    )    )

											snake_case_						:    List[str]				=  []
											snake_case_						:    List[str]				=  []
											for word, label in zip(example.words					,							example.labels    ):
															snake_case_						:    Optional[Any]				=  tokenizer.tokenize(__magic_name__    )

															# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
															if len(__magic_name__    ) > 0:
																			tokens.extend(__magic_name__    )
																			# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																			label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__magic_name__    ) - 1)    )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
											snake_case_						:    Union[str, Any]				=  tokenizer.num_special_tokens_to_add()
											if len(__magic_name__    ) > max_seq_length - special_tokens_count:
															snake_case_						:    str				=  tokens[: (max_seq_length - special_tokens_count)]
															snake_case_						:    Any				=  label_ids[: (max_seq_length - special_tokens_count)]

											# The convention in BERT is:
											# (a) For sequence pairs:
											#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
											#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
											# (b) For single sequences:
											#  tokens:   [CLS] the dog is hairy . [SEP]
											#  type_ids:   0   0   0   0  0     0   0
											#
											# Where "type_ids" are used to indicate whether this is the first
											# sequence or the second sequence. The embedding vectors for `type=0` and
											# `type=1` were learned during pre-training and are added to the wordpiece
											# embedding vector (and position vector). This is not *strictly* necessary
											# since the [SEP] token unambiguously separates the sequences, but it makes
											# it easier for the model to learn the concept of sequences.
											#
											# For classification tasks, the first vector (corresponding to [CLS]) is
											# used as the "sentence vector". Note that this only makes sense because
											# the entire model is fine-tuned.
											tokens += [sep_token]
											label_ids += [pad_token_label_id]
											if sep_token_extra:
															# roberta uses an extra separator b/w pairs of sentences
															tokens += [sep_token]
															label_ids += [pad_token_label_id]
											snake_case_						:    Union[str, Any]				=  [sequence_a_segment_id] * len(__magic_name__    )

											if cls_token_at_end:
															tokens += [cls_token]
															label_ids += [pad_token_label_id]
															segment_ids += [cls_token_segment_id]
											else:
															snake_case_						:    Union[str, Any]				=  [cls_token] + tokens
															snake_case_						:    List[Any]				=  [pad_token_label_id] + label_ids
															snake_case_						:    Optional[Any]				=  [cls_token_segment_id] + segment_ids

											snake_case_						:    Optional[Any]				=  tokenizer.convert_tokens_to_ids(__magic_name__    )

											# The mask has 1 for real tokens and 0 for padding tokens. Only real
											# tokens are attended to.
											snake_case_						:    int				=  [1 if mask_padding_with_zero else 0] * len(__magic_name__    )

											# Zero-pad up to the sequence length.
											snake_case_						:    Optional[int]				=  max_seq_length - len(__magic_name__    )
											if pad_on_left:
															snake_case_						:    Optional[Any]				=  ([pad_token] * padding_length) + input_ids
															snake_case_						:    Optional[int]				=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
															snake_case_						:    Optional[Any]				=  ([pad_token_segment_id] * padding_length) + segment_ids
															snake_case_						:    Dict				=  ([pad_token_label_id] * padding_length) + label_ids
											else:
															input_ids += [pad_token] * padding_length
															input_mask += [0 if mask_padding_with_zero else 1] * padding_length
															segment_ids += [pad_token_segment_id] * padding_length
															label_ids += [pad_token_label_id] * padding_length

											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length

											if ex_index < 5:
															logger.info('''*** Example ***'''    )
															logger.info('''guid: %s'''					,							example.guid    )
															logger.info('''tokens: %s'''					,							''' '''.join([str(__magic_name__    ) for x in tokens]    )    )
															logger.info('''input_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in input_ids]    )    )
															logger.info('''input_mask: %s'''					,							''' '''.join([str(__magic_name__    ) for x in input_mask]    )    )
															logger.info('''segment_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in segment_ids]    )    )
															logger.info('''label_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in label_ids]    )    )

											if "token_type_ids" not in tokenizer.model_input_names:
															snake_case_						:    int				=  None

											features.append(
											    InputFeatures(
											        input_ids=__magic_name__					,							attention_mask=__magic_name__					,							token_type_ids=__magic_name__					,							label_ids=__magic_name__    )    )
							return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset



	class 					__lowerCAmelCase       (			_a     ):
				lowerCamelCase_     :     List[InputFeatures]
				lowerCamelCase_     :     int       			=      nn.CrossEntropyLoss().ignore_index






				def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__=False					,							__magic_name__ = Split.train					,							)     ->   Union[str, Any]:


								'''simple docstring'''



								snake_case_						:    List[str]				=  os.path.join(
								    __magic_name__					,							'''cached_{}_{}_{}'''.format(mode.value					,							tokenizer.__class__.__name__					,							str(__magic_name__    )    )					,							)

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								snake_case_						:    Dict				=  cached_features_file + '''.lock'''
								with FileLock(__magic_name__    ):
												if os.path.exists(__magic_name__    ) and not overwrite_cache:
																logger.info(F'''Loading features from cached file {cached_features_file}'''    )
																snake_case_						:    Dict				=  torch.load(__magic_name__    )
												else:
																logger.info(F'''Creating features from dataset file at {data_dir}'''    )
																snake_case_						:    Any				=  token_classification_task.read_examples_from_file(__magic_name__					,							__magic_name__    )
																# TODO clean up all this to leverage built-in features of tokenizers
																snake_case_						:    int				=  token_classification_task.convert_examples_to_features(
																    __magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							cls_token_at_end=bool(model_type in ['''xlnet''']    )					,							cls_token=tokenizer.cls_token					,							cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,							sep_token=tokenizer.sep_token					,							sep_token_extra=__magic_name__					,							pad_on_left=bool(tokenizer.padding_side == '''left'''    )					,							pad_token=tokenizer.pad_token_id					,							pad_token_segment_id=tokenizer.pad_token_type_id					,							pad_token_label_id=self.pad_token_label_id					,							)
																logger.info(F'''Saving features into cached file {cached_features_file}'''    )
																torch.save(self.features					,							__magic_name__    )






				def __len__(self    )     ->   Optional[Any]:


								'''simple docstring'''



								return len(self.features    )






				def __getitem__(self					,							__magic_name__    )     ->   InputFeatures:


								'''simple docstring'''



								return self.features[i]


if is_tf_available():
	import tensorflow as tf



	class 					__lowerCAmelCase       :
				lowerCamelCase_     :     List[InputFeatures]
				lowerCamelCase_     :     int       			=      -100






				def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__=False					,							__magic_name__ = Split.train					,							)     ->   Optional[int]:


								'''simple docstring'''



								snake_case_						:    Optional[int]				=  token_classification_task.read_examples_from_file(__magic_name__					,							__magic_name__    )
								# TODO clean up all this to leverage built-in features of tokenizers
								snake_case_						:    int				=  token_classification_task.convert_examples_to_features(
								    __magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							cls_token_at_end=bool(model_type in ['''xlnet''']    )					,							cls_token=tokenizer.cls_token					,							cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,							sep_token=tokenizer.sep_token					,							sep_token_extra=__magic_name__					,							pad_on_left=bool(tokenizer.padding_side == '''left'''    )					,							pad_token=tokenizer.pad_token_id					,							pad_token_segment_id=tokenizer.pad_token_type_id					,							pad_token_label_id=self.pad_token_label_id					,							)

								def gen():
												for ex in self.features:
																if ex.token_type_ids is None:
																				yield (
																				    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																				    ex.label_ids,
																				)
																else:
																				yield (
																				    {
																				        "input_ids": ex.input_ids,
																				        "attention_mask": ex.attention_mask,
																				        "token_type_ids": ex.token_type_ids,
																				    },
																				    ex.label_ids,
																				)

								if "token_type_ids" not in tokenizer.model_input_names:
												snake_case_						:    Optional[Any]				=  tf.data.Dataset.from_generator(
												    __magic_name__					,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)					,							(
												        {'''input_ids''': tf.TensorShape([None]    ), '''attention_mask''': tf.TensorShape([None]    )},
												        tf.TensorShape([None]    ),
												    )					,							)
								else:
												snake_case_						:    int				=  tf.data.Dataset.from_generator(
												    __magic_name__					,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)					,							(
												        {
												            '''input_ids''': tf.TensorShape([None]    ),
												            '''attention_mask''': tf.TensorShape([None]    ),
												            '''token_type_ids''': tf.TensorShape([None]    ),
												        },
												        tf.TensorShape([None]    ),
												    )					,							)






				def 			lowerCamelCase  (self    )     ->   List[Any]:


								'''simple docstring'''



								snake_case_						:    Optional[Any]				=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

								return self.dataset






				def __len__(self    )     ->   str:


								'''simple docstring'''



								return len(self.features    )






				def __getitem__(self					,							__magic_name__    )     ->   InputFeatures:


								'''simple docstring'''



								return self.features[i]







import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_    =				get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')



@require_sentencepiece
@require_tokenizers
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[int]       			=      SpeechTaTokenizer
			lowerCamelCase_     :     int       			=      False
			lowerCamelCase_     :     Dict       			=      True






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:    Tuple				=  SpeechTaTokenizer(__magic_name__    )

							snake_case_						:    Any				=  AddedToken('''<mask>'''					,							lstrip=__magic_name__					,							rstrip=__magic_name__    )
							snake_case_						:    int				=  mask_token
							tokenizer.add_special_tokens({'''mask_token''': mask_token}    )
							tokenizer.add_tokens(['''<ctc_blank>''']    )

							tokenizer.save_pretrained(self.tmpdirname    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Dict				=  '''this is a test'''
							snake_case_						:    int				=  '''this is a test'''
							return input_text, output_text






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__=False					,							__magic_name__=20					,							__magic_name__=5    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_			,							snake_case_						:    int				=  self.get_input_output_texts(__magic_name__    )
							snake_case_						:    Optional[Any]				=  tokenizer.encode(__magic_name__					,							add_special_tokens=__magic_name__    )
							snake_case_						:    Any				=  tokenizer.decode(__magic_name__					,							clean_up_tokenization_spaces=__magic_name__    )
							return text, ids






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    List[str]				=  '''<pad>'''
							snake_case_						:    Any				=  1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__    )					,							__magic_name__    )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__    )					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Any				=  list(self.get_tokenizer().get_vocab().keys()    )

							self.assertEqual(vocab_keys[0]					,							'''<s>'''    )
							self.assertEqual(vocab_keys[1]					,							'''<pad>'''    )
							self.assertEqual(vocab_keys[-4]					,							'''œ'''    )
							self.assertEqual(vocab_keys[-2]					,							'''<mask>'''    )
							self.assertEqual(vocab_keys[-1]					,							'''<ctc_blank>'''    )
							self.assertEqual(len(__magic_name__    )					,							81    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size					,							79    )






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    int				=  self.get_tokenizers(do_lower_case=__magic_name__    )
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
															snake_case_						:    int				=  tokenizer.vocab_size
															snake_case_						:    Optional[Any]				=  len(__magic_name__    )

															self.assertNotEqual(__magic_name__					,							0    )

															# We usually have added tokens from the start in tests because our vocab fixtures are
															# smaller than the original vocabs - let's not assert this
															# self.assertEqual(vocab_size, all_size)

															snake_case_						:    List[Any]				=  ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
															snake_case_						:    List[Any]				=  tokenizer.add_tokens(__magic_name__    )
															snake_case_						:    Dict				=  tokenizer.vocab_size
															snake_case_						:    Optional[Any]				=  len(__magic_name__    )

															self.assertNotEqual(__magic_name__					,							0    )
															self.assertEqual(__magic_name__					,							__magic_name__    )
															self.assertEqual(__magic_name__					,							len(__magic_name__    )    )
															self.assertEqual(__magic_name__					,							all_size + len(__magic_name__    )    )

															snake_case_						:    Union[str, Any]				=  tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''					,							add_special_tokens=__magic_name__    )

															self.assertGreaterEqual(len(__magic_name__    )					,							4    )
															self.assertGreater(tokens[0]					,							tokenizer.vocab_size - 1    )
															self.assertGreater(tokens[-3]					,							tokenizer.vocab_size - 1    )

															snake_case_						:    Union[str, Any]				=  {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
															snake_case_						:    List[str]				=  tokenizer.add_special_tokens(__magic_name__    )
															snake_case_						:    Dict				=  tokenizer.vocab_size
															snake_case_						:    Dict				=  len(__magic_name__    )

															self.assertNotEqual(__magic_name__					,							0    )
															self.assertEqual(__magic_name__					,							__magic_name__    )
															self.assertEqual(__magic_name__					,							len(__magic_name__    )    )
															self.assertEqual(__magic_name__					,							all_size_a + len(__magic_name__    )    )

															snake_case_						:    Tuple				=  tokenizer.encode(
															    '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''					,							add_special_tokens=__magic_name__    )

															self.assertGreaterEqual(len(__magic_name__    )					,							6    )
															self.assertGreater(tokens[0]					,							tokenizer.vocab_size - 1    )
															self.assertGreater(tokens[0]					,							tokens[1]    )
															self.assertGreater(tokens[-3]					,							tokenizer.vocab_size - 1    )
															self.assertGreater(tokens[-3]					,							tokens[-4]    )
															self.assertEqual(tokens[0]					,							tokenizer.eos_token_id    )
															self.assertEqual(tokens[-3]					,							tokenizer.pad_token_id    )






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.get_tokenizer()

							snake_case_						:    Optional[Any]				=  tokenizer.tokenize('''This is a test'''    )
							# fmt: off
							self.assertListEqual(__magic_name__					,							[SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t''']    )
							# fmt: on

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(__magic_name__    )					,							[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]					,							)

							snake_case_						:    List[Any]				=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
							self.assertListEqual(
							    __magic_name__					,							[SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.''']    )

							snake_case_						:    List[str]				=  tokenizer.convert_tokens_to_ids(__magic_name__    )
							# fmt: off
							self.assertListEqual(__magic_name__					,							[4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]    )
							# fmt: on

							snake_case_						:    int				=  tokenizer.convert_ids_to_tokens(__magic_name__    )
							self.assertListEqual(
							    __magic_name__					,							[SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.''']    )






			@slow
			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Tuple				=  [
							    '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
							    '''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
							    '''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
							    '''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
							    '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
							    '''conditioning on both left and right context in all layers.''',
							    '''The quick brown fox jumps over the lazy dog.''',
							]

							# fmt: off
							snake_case_						:    List[Any]				=  {
							    '''input_ids''': [
							        [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
							        [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
							        [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
							    ],
							    '''attention_mask''': [
							        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
							        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							    ]
							}
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=__magic_name__					,							model_name='''microsoft/speecht5_asr'''					,							revision='''c5ef64c71905caeccde0e4462ef3f9077224c524'''					,							sequences=__magic_name__					,							)
