






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

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_           = logging.getLogger(__name__)
@dataclass(frozen=snake_case__ )
class      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   42
		snake_case    =   42
		snake_case    =   None
		snake_case    =   None
		snake_case    =   None


@dataclass(frozen=snake_case__ )
class      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   42
		snake_case    =   None
		snake_case    =   None
		snake_case    =   None
		snake_case    =   None


if is_torch_available():
					import torch
					from torch.utils.data import Dataset
					class      UpperCamelCase (				snake_case__ ):







							"""simple docstring"""



							snake_case    =   42
							def __init__(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       PreTrainedTokenizer						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       List[str]=False						,_SCREAMING_SNAKE_CASE		:       bool = False						,)    ->   List[Any]:


											'''simple docstring'''
											A	 =      hans_processors[task]()

											A	 =      os.path.join(
											    _SCREAMING_SNAKE_CASE						,'cached_{}_{}_{}_{}'.format(
											        'dev' if evaluate else 'train'						,tokenizer.__class__.__name__						,str(_SCREAMING_SNAKE_CASE		)						,_SCREAMING_SNAKE_CASE						,)						,)
											A	 =      processor.get_labels()
											if tokenizer.__class__ in (
											    RobertaTokenizer,
											    RobertaTokenizerFast,
											    XLMRobertaTokenizer,
											    BartTokenizer,
											    BartTokenizerFast,
											):
															# HACK(label indices are swapped in RoBERTa pretrained model)
															A ,		A	 =      label_list[2], label_list[1]
											A	 =      label_list

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											A	 =      cached_features_file + '.lock'
											with FileLock(_SCREAMING_SNAKE_CASE		):
															if os.path.exists(_SCREAMING_SNAKE_CASE		) and not overwrite_cache:
																			logger.info(f'Loading features from cached file {cached_features_file}'		)
																			A	 =      torch.load(_SCREAMING_SNAKE_CASE		)
															else:
																			logger.info(f'Creating features from dataset file at {data_dir}'		)

																			A	 =      (
																			    processor.get_dev_examples(_SCREAMING_SNAKE_CASE		) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE		)
																			)

																			logger.info('Training examples: %s'						,len(_SCREAMING_SNAKE_CASE		)		)
																			A	 =      hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
																			logger.info('Saving features into cached file %s'						,_SCREAMING_SNAKE_CASE		)
																			torch.save(self.features						,_SCREAMING_SNAKE_CASE		)
							def __len__(  self		:       Union[str, Any]		)    ->   Any:


											'''simple docstring'''
											return len(self.features		)
							def __getitem__(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   InputFeatures:


											'''simple docstring'''
											return self.features[i]







							def       A(  self		:       int		)    ->   Any:


											'''simple docstring'''
											return self.label_list


if is_tf_available():
					import tensorflow as tf
					class      UpperCamelCase :







							"""simple docstring"""



							snake_case    =   42
							def __init__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       PreTrainedTokenizer						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[int] = 1_2_8						,_SCREAMING_SNAKE_CASE		:       Dict=False						,_SCREAMING_SNAKE_CASE		:       bool = False						,)    ->   Optional[Any]:


											'''simple docstring'''
											A	 =      hans_processors[task]()
											A	 =      processor.get_labels()
											if tokenizer.__class__ in (
											    RobertaTokenizer,
											    RobertaTokenizerFast,
											    XLMRobertaTokenizer,
											    BartTokenizer,
											    BartTokenizerFast,
											):
															# HACK(label indices are swapped in RoBERTa pretrained model)
															A ,		A	 =      label_list[2], label_list[1]
											A	 =      label_list

											A	 =      processor.get_dev_examples(_SCREAMING_SNAKE_CASE		) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE		)
											A	 =      hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

											def gen():
															for ex_index, ex in tqdm.tqdm(enumerate(self.features		)						,desc='convert examples to features'		):
																			if ex_index % 1_0_0_0_0 == 0:
																							logger.info('Writing example %d of %d' % (ex_index, len(_SCREAMING_SNAKE_CASE		))		)

																			yield (
																			    {
																			        "example_id": 0,
																			        "input_ids": ex.input_ids,
																			        "attention_mask": ex.attention_mask,
																			        "token_type_ids": ex.token_type_ids,
																			    },
																			    ex.label,
																			)

											A	 =      tf.data.Dataset.from_generator(
											    _SCREAMING_SNAKE_CASE						,(
											        {
											            'example_id': tf.intaa,
											            'input_ids': tf.intaa,
											            'attention_mask': tf.intaa,
											            'token_type_ids': tf.intaa,
											        },
											        tf.intaa,
											    )						,(
											        {
											            'example_id': tf.TensorShape([]		),
											            'input_ids': tf.TensorShape([None, None]		),
											            'attention_mask': tf.TensorShape([None, None]		),
											            'token_type_ids': tf.TensorShape([None, None]		),
											        },
											        tf.TensorShape([]		),
											    )						,)
							def       A(  self		:       int		)    ->   List[Any]:


											'''simple docstring'''
											return self.dataset
							def __len__(  self		:       List[str]		)    ->   Optional[Any]:


											'''simple docstring'''
											return len(self.features		)
							def __getitem__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   InputFeatures:


											'''simple docstring'''
											return self.features[i]







							def       A(  self		:       Optional[int]		)    ->   List[str]:


											'''simple docstring'''
											return self.label_list
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   int:


						'''simple docstring'''
						return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE						,'heuristics_train_set.txt'		)		)						,'train'		)
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   str:


						'''simple docstring'''
						return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE						,'heuristics_evaluation_set.txt'		)		)						,'dev'		)
		def       A(  self		:       Optional[int]		)    ->   Dict:


						'''simple docstring'''
						return ["contradiction", "entailment", "neutral"]







		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       str		)    ->   List[str]:


						'''simple docstring'''
						A	 =      []
						for i, line in enumerate(_SCREAMING_SNAKE_CASE		):
										if i == 0:
														continue
										A	 =      '%s-%s' % (set_type, line[0])
										A	 =      line[5]
										A	 =      line[6]
										A	 =      line[7][2:] if line[7].startswith('ex'		) else line[7]
										A	 =      line[0]
										examples.append(InputExample(guid=_SCREAMING_SNAKE_CASE						,text_a=_SCREAMING_SNAKE_CASE						,text_b=_SCREAMING_SNAKE_CASE						,label=_SCREAMING_SNAKE_CASE						,pairID=_SCREAMING_SNAKE_CASE		)		)
						return examples







def      snake_case		(							UpperCAmelCase	:       List[InputExample],						UpperCAmelCase	:       List[str],						UpperCAmelCase	:       int,						UpperCAmelCase	:       PreTrainedTokenizer,						):
				A	 =      {label: i for i, label in enumerate(UpperCAmelCase   )}

				A	 =      []
				for ex_index, example in tqdm.tqdm(enumerate(UpperCAmelCase   ),						desc='convert examples to features'   ):
								if ex_index % 1_00_00 == 0:
												logger.info('Writing example %d' % (ex_index)   )

								A	 =      tokenizer(
								    example.text_a,						example.text_b,						add_special_tokens=UpperCAmelCase,						max_length=UpperCAmelCase,						padding='max_length',						truncation=UpperCAmelCase,						return_overflowing_tokens=UpperCAmelCase,						)

								A	 =      label_map[example.label] if example.label in label_map else 0

								A	 =      int(example.pairID   )

								features.append(InputFeatures(**UpperCAmelCase,						label=UpperCAmelCase,						pairID=UpperCAmelCase   )   )

				for i, example in enumerate(examples[:5]   ):
								logger.info('*** Example ***'   )
								logger.info(f'guid: {example}'   )
								logger.info(f'features: {features[i]}'   )

				return features


lowerCAmelCase_           = {
    'hans': 3,
}



lowerCAmelCase_           = {
    'hans': HansProcessor,
}













import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   JukeboxTokenizer
		snake_case    =   {
		    "artist": "Zac Brown Band",
		    "genres": "Country",
		    "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
		}
		@require_torch
		def       A(  self		:       Optional[int]		)    ->   Any:


						'''simple docstring'''
						import torch

						A	 =      JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'		)
						A	 =      tokenizer(**self.metas		)['input_ids']
						# fmt: off
						A	 =      [
						    torch.tensor([[
						        0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
						        7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
						        4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
						        4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
						        7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
						        3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
						        2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
						        4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
						        4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
						        1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
						        7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
						        7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
						        6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
						        3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
						        2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
						        3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
						        2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
						        4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
						        7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
						        4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
						        7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
						        3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
						        4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
						        2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
						        4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
						        3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
						        4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
						        7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
						        3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
						        3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
						        4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
						        3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
						        7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
						        7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
						        4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
						        2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
						        4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
						        7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
						        4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
						        4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
						        4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
						        7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
						        4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
						        2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
						        7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
						        7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
						        4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
						        7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
						        2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
						        7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
						        3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
						        4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
						        4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
						        7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
						        4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
						        3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
						        4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
						        2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
						        2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
						        7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
						        7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
						        7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
						        7_6, 7_6]]		),
						    torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]]		),
						    torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]]		),
						]
						# fmt: on
						self.assertTrue(torch.allclose(tokens[0]						,EXPECTED_OUTPUT[0]		)		)
						self.assertTrue(torch.allclose(tokens[1]						,EXPECTED_OUTPUT[1]		)		)
						self.assertTrue(torch.allclose(tokens[2]						,EXPECTED_OUTPUT[2]		)		)







		@require_torch
		def       A(  self		:       Tuple		)    ->   List[Any]:


						'''simple docstring'''
						import torch

						A	 =      JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'		)
						A	 =      tokenizer(**self.metas		)['input_ids']
						# fmt: off
						A	 =      [
						    torch.tensor([[
						        0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
						        3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
						        3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
						        4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
						        7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
						        7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
						        2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
						        3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
						        3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
						        7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
						        7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
						        7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
						        4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
						        7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
						        7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
						        7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
						        6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
						        4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
						        4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
						        3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
						        3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
						        4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
						        7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
						        4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
						        4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
						        3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
						        3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
						        4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
						        3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
						        3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
						        4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
						        2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
						        3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
						        3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
						        3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
						        3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
						        4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
						        3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
						        1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
						        1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
						        4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
						        4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
						        4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
						        4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
						        2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
						        3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
						        7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
						        3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
						        4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
						        7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
						        4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
						        7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
						        7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
						        2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
						        7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
						        7_7, 7_7, 7_7, 7_7, 7_7, 7_7]]		),
						    torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]]		),
						    torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]]		),
						]
						# fmt: on
						self.assertTrue(torch.allclose(tokens[0]						,EXPECTED_OUTPUT[0]		)		)
						self.assertTrue(torch.allclose(tokens[1]						,EXPECTED_OUTPUT[1]		)		)
						self.assertTrue(torch.allclose(tokens[2]						,EXPECTED_OUTPUT[2]		)		)





