





'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A   : Optional[int]            =			logging.get_logger(__name__)







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["input_features", "is_longer"]


					def __init__(					self	:  Optional[Any]       ,     __snake_case	:  List[str]=64       ,     __snake_case	:  List[Any]=4_80_00       ,     __snake_case	:  Any=4_80       ,     __snake_case	:  Optional[int]=10       ,     __snake_case	:  Optional[Any]=10_24       ,     __snake_case	:  List[Any]=0.0       ,     __snake_case	:  Union[str, Any]=False       ,     __snake_case	:  float = 0       ,     __snake_case	:  float = 1_40_00       ,     __snake_case	:  int = None       ,     __snake_case	:  str = "fusion"       ,     __snake_case	:  str = "repeatpad"       ,     **__snake_case	:  List[Any]       ,     ):





											'''simple docstring'''





											super().__init__(
											    feature_size=__snake_case       ,     sampling_rate=__snake_case       ,     padding_value=__snake_case       ,     return_attention_mask=__snake_case       ,     **__snake_case       ,     )
											_snake_case:   Dict  					=		top_db
											_snake_case:   Union[str, Any]  					=		truncation
											_snake_case:   Dict  					=		padding
											_snake_case:   Optional[int]  					=		fft_window_size
											_snake_case:   List[str]  					=		(fft_window_size >> 1) + 1
											_snake_case:   List[str]  					=		hop_length
											_snake_case:   str  					=		max_length_s
											_snake_case:   Union[str, Any]  					=		max_length_s * sampling_rate
											_snake_case:   Dict  					=		sampling_rate
											_snake_case:   Any  					=		frequency_min
											_snake_case:   Union[str, Any]  					=		frequency_max
											_snake_case:   List[Any]  					=		mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins       ,     num_mel_filters=__snake_case       ,     min_frequency=__snake_case       ,     max_frequency=__snake_case       ,     sampling_rate=__snake_case       ,     norm=__snake_case       ,     mel_scale='htk'       ,     )
											_snake_case:   int  					=		mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins       ,     num_mel_filters=__snake_case       ,     min_frequency=__snake_case       ,     max_frequency=__snake_case       ,     sampling_rate=__snake_case       ,     norm='slaney'       ,     mel_scale='slaney'       ,     )


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





											'''simple docstring'''





											_snake_case:   str  					=		copy.deepcopy(self.__dict__       )
											_snake_case:   str  					=		self.__class__.__name__
											if "mel_filters" in output:
																	del output["mel_filters"]
											if "mel_filters_slaney" in output:
																	del output["mel_filters_slaney"]
											return output


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  np.array       ,     __snake_case	:  Optional[np.array] = None       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		spectrogram(
											    __snake_case       ,     window_function(self.fft_window_size       ,     'hann'       )       ,     frame_length=self.fft_window_size       ,     hop_length=self.hop_length       ,     power=2.0       ,     mel_filters=__snake_case       ,     log_mel='dB'       ,     )
											return log_mel_spectrogram.T


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Any       ,     __snake_case	:  str       ,     __snake_case	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		np.array_split(list(range(0       ,     total_frames - chunk_frames + 1       )       )       ,     3       )
											if len(ranges[1]       ) == 0:
																	# if the audio is too short, we just use the first chunk
																	_snake_case:   Optional[Any]  					=		[0]
											if len(ranges[2]       ) == 0:
																	# if the audio is too short, we just use the first chunk
																	_snake_case:   str  					=		[0]
											# randomly choose index for each part
											_snake_case:   Union[str, Any]  					=		np.random.choice(ranges[0]       )
											_snake_case:   Tuple  					=		np.random.choice(ranges[1]       )
											_snake_case:   Any  					=		np.random.choice(ranges[2]       )

											_snake_case:   Optional[int]  					=		mel[idx_front : idx_front + chunk_frames, :]
											_snake_case:   Optional[int]  					=		mel[idx_middle : idx_middle + chunk_frames, :]
											_snake_case:   Dict  					=		mel[idx_back : idx_back + chunk_frames, :]

											_snake_case:   Optional[int]  					=		torch.tensor(mel[None, None, :]       )
											_snake_case:   List[Any]  					=		torch.nn.functional.interpolate(
											    __snake_case       ,     size=[chunk_frames, 64]       ,     mode='bilinear'       ,     align_corners=__snake_case       )
											_snake_case:   Any  					=		mel_shrink[0][0].numpy()
											_snake_case:   Optional[int]  					=		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,     axis=0       )
											return mel_fusion


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  np.array       ,     __snake_case	:  List[str]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  int       ):





											'''simple docstring'''





											if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																							_snake_case:   Dict  					=		True
																							# random crop to max_length (for compatibility) -> this should be handled by self.pad
																							_snake_case:   Optional[Any]  					=		len(__snake_case       ) - max_length
																							_snake_case:   List[str]  					=		np.random.randint(0       ,     overflow + 1       )
																							_snake_case:   Optional[Any]  					=		waveform[idx : idx + max_length]
																							_snake_case:   List[Any]  					=		self._np_extract_fbank_features(__snake_case       ,     self.mel_filters_slaney       )[None, :]
																	elif truncation == "fusion":
																							_snake_case:   Any  					=		self._np_extract_fbank_features(__snake_case       ,     self.mel_filters       )
																							_snake_case:   Dict  					=		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																							_snake_case:   List[Any]  					=		mel.shape[0]
																							if chunk_frames == total_frames:
																													# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																													# In this case, we just use the whole audio.
																													_snake_case:   Optional[Any]  					=		np.stack([mel, mel, mel, mel]       ,     axis=0       )
																													_snake_case:   Optional[Any]  					=		False
																							else:
																													_snake_case:   Tuple  					=		self._random_mel_fusion(__snake_case       ,     __snake_case       ,     __snake_case       )
																													_snake_case:   str  					=		True
																	else:
																							raise NotImplementedError(f'''data_truncating {truncation} not implemented'''       )

											else:
																	_snake_case:   Union[str, Any]  					=		False
																	# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																	if waveform.shape[0] < max_length:
																							if padding == "repeat":
																													_snake_case:   int  					=		int(max_length / len(__snake_case       )       )
																													_snake_case:   Union[str, Any]  					=		np.stack(np.tile(__snake_case       ,     n_repeat + 1       )       )[:max_length]
																							if padding == "repeatpad":
																													_snake_case:   List[str]  					=		int(max_length / len(__snake_case       )       )
																													_snake_case:   Any  					=		np.stack(np.tile(__snake_case       ,     __snake_case       )       )
																							_snake_case:   int  					=		np.pad(__snake_case       ,     (0, max_length - waveform.shape[0])       ,     mode='constant'       ,     constant_values=0       )

																	if truncation == "fusion":
																							_snake_case:   int  					=		self._np_extract_fbank_features(__snake_case       ,     self.mel_filters       )
																							_snake_case:   Optional[Any]  					=		np.stack([input_mel, input_mel, input_mel, input_mel]       ,     axis=0       )
																	else:
																							_snake_case:   Dict  					=		self._np_extract_fbank_features(__snake_case       ,     self.mel_filters_slaney       )[None, :]

											return input_mel, longer




					def __call__(					self	:  List[Any]       ,     __snake_case	:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,     __snake_case	:  str = None       ,     __snake_case	:  Optional[str] = None       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  Optional[Union[str, TensorType]] = None       ,     **__snake_case	:  Optional[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		truncation if truncation is not None else self.truncation
											_snake_case:   Tuple  					=		padding if padding else self.padding

											if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																							raise ValueError(
																							    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																							    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																							    f''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''       )
											else:
																	logger.warning(
																	    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
																	    'Failing to do so can result in silent errors that might be hard to debug.'       )

											_snake_case:   Any  					=		isinstance(__snake_case       ,     np.ndarray       ) and len(raw_speech.shape       ) > 1
											if is_batched_numpy and len(raw_speech.shape       ) > 2:
																	raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''       )
											_snake_case:   Union[str, Any]  					=		is_batched_numpy or (
											    isinstance(__snake_case       ,     (list, tuple)       ) and (isinstance(raw_speech[0]       ,     (np.ndarray, tuple, list)       ))
											)

											if is_batched:
																	_snake_case:   Optional[int]  					=		[np.asarray(__snake_case       ,     dtype=np.floataa       ) for speech in raw_speech]
											elif not is_batched and not isinstance(__snake_case       ,     np.ndarray       ):
																	_snake_case:   Union[str, Any]  					=		np.asarray(__snake_case       ,     dtype=np.floataa       )
											elif isinstance(__snake_case       ,     np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
																	_snake_case:   Dict  					=		raw_speech.astype(np.floataa       )

											# always return batch
											if not is_batched:
																	_snake_case:   List[str]  					=		[np.asarray(__snake_case       )]

											# convert to mel spectrogram, truncate and pad if needed.
											_snake_case:   str  					=		[
											    self._get_input_mel(__snake_case       ,     max_length if max_length else self.nb_max_samples       ,     __snake_case       ,     __snake_case       )
											    for waveform in raw_speech
											]

											_snake_case:   Optional[Any]  					=		[]
											_snake_case:   Any  					=		[]
											for mel, longer in padded_inputs:
																	input_mel.append(__snake_case       )
																	is_longer.append(__snake_case       )

											if truncation == "fusion" and sum(__snake_case       ) == 0:
																	# if no audio is longer than 10s, then randomly select one audio to be longer
																	_snake_case:   Optional[int]  					=		np.random.randint(0       ,     len(__snake_case       )       )
																	_snake_case:   Optional[int]  					=		True

											if isinstance(input_mel[0]       ,     __snake_case       ):
																	_snake_case:   Optional[Any]  					=		[np.asarray(__snake_case       ,     dtype=np.floataa       ) for feature in input_mel]

											# is_longer is a list of bool
											_snake_case:   Any  					=		[[longer] for longer in is_longer]

											_snake_case:   List[str]  					=		{'input_features': input_mel, 'is_longer': is_longer}
											_snake_case:   List[str]  					=		BatchFeature(__snake_case       )

											if return_tensors is not None:
																	_snake_case:   Optional[int]  					=		input_features.convert_to_tensors(__snake_case       )

											return input_features












'''simple docstring'''






import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A   : Tuple             =			'▁'

A   : Optional[Any]             =			get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    BigBirdTokenizer
					_SCREAMING_SNAKE_CASE							   =    BigBirdTokenizerFast
					_SCREAMING_SNAKE_CASE							   =    True
					_SCREAMING_SNAKE_CASE							   =    True


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											super().setUp()

											_snake_case:   Dict  					=		self.tokenizer_class(__snake_case       ,     keep_accents=__snake_case       )
											tokenizer.save_pretrained(self.tmpdirname       )


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





											'''simple docstring'''





											_snake_case:   List[str]  					=		'<s>'
											_snake_case:   Tuple  					=		1

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		list(self.get_tokenizer().get_vocab().keys()       )

											self.assertEqual(vocab_keys[0]       ,     '<unk>'       )
											self.assertEqual(vocab_keys[1]       ,     '<s>'       )
											self.assertEqual(vocab_keys[-1]       ,     '[MASK]'       )
											self.assertEqual(len(__snake_case       )       ,     10_04       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											self.assertEqual(self.get_tokenizer().vocab_size       ,     10_00       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											if not self.test_rust_tokenizer:
																	return

											_snake_case:   Optional[int]  					=		self.get_tokenizer()
											_snake_case:   Union[str, Any]  					=		self.get_rust_tokenizer()

											_snake_case:   List[str]  					=		'I was born in 92000, and this is falsé.'

											_snake_case:   str  					=		tokenizer.tokenize(__snake_case       )
											_snake_case:   Dict  					=		rust_tokenizer.tokenize(__snake_case       )
											self.assertListEqual(__snake_case       ,     __snake_case       )

											_snake_case:   Union[str, Any]  					=		tokenizer.encode(__snake_case       ,     add_special_tokens=__snake_case       )
											_snake_case:   List[Any]  					=		rust_tokenizer.encode(__snake_case       ,     add_special_tokens=__snake_case       )
											self.assertListEqual(__snake_case       ,     __snake_case       )

											_snake_case:   Optional[int]  					=		self.get_rust_tokenizer()
											_snake_case:   int  					=		tokenizer.encode(__snake_case       )
											_snake_case:   Tuple  					=		rust_tokenizer.encode(__snake_case       )
											self.assertListEqual(__snake_case       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		BigBirdTokenizer(__snake_case       ,     keep_accents=__snake_case       )

											_snake_case:   Optional[int]  					=		tokenizer.tokenize('This is a test'       )
											self.assertListEqual(__snake_case       ,     ['▁This', '▁is', '▁a', '▁t', 'est']       )

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(__snake_case       )       ,     [2_85, 46, 10, 1_70, 3_82]       ,     )

											_snake_case:   Any  					=		tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
											self.assertListEqual(
											    __snake_case       ,     [
											        SPIECE_UNDERLINE + 'I',
											        SPIECE_UNDERLINE + 'was',
											        SPIECE_UNDERLINE + 'b',
											        'or',
											        'n',
											        SPIECE_UNDERLINE + 'in',
											        SPIECE_UNDERLINE + '',
											        '9',
											        '2',
											        '0',
											        '0',
											        '0',
											        ',',
											        SPIECE_UNDERLINE + 'and',
											        SPIECE_UNDERLINE + 'this',
											        SPIECE_UNDERLINE + 'is',
											        SPIECE_UNDERLINE + 'f',
											        'al',
											        's',
											        'é',
											        '.',
											    ]       ,     )
											_snake_case:   List[Any]  					=		tokenizer.convert_tokens_to_ids(__snake_case       )
											self.assertListEqual(
											    __snake_case       ,     [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,     )

											_snake_case:   Optional[Any]  					=		tokenizer.convert_ids_to_tokens(__snake_case       )
											self.assertListEqual(
											    __snake_case       ,     [
											        SPIECE_UNDERLINE + 'I',
											        SPIECE_UNDERLINE + 'was',
											        SPIECE_UNDERLINE + 'b',
											        'or',
											        'n',
											        SPIECE_UNDERLINE + 'in',
											        SPIECE_UNDERLINE + '',
											        '<unk>',
											        '2',
											        '0',
											        '0',
											        '0',
											        ',',
											        SPIECE_UNDERLINE + 'and',
											        SPIECE_UNDERLINE + 'this',
											        SPIECE_UNDERLINE + 'is',
											        SPIECE_UNDERLINE + 'f',
											        'al',
											        's',
											        '<unk>',
											        '.',
											    ]       ,     )


					@cached_property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		'Hello World!'
											_snake_case:   Optional[int]  					=		[65, 1_85_36, 22_60, 1_01, 66]

											self.assertListEqual(__snake_case       ,     self.big_tokenizer.encode(__snake_case       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		(
											    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
											    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
											)
											# fmt: off
											_snake_case:   str  					=		[65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
											# fmt: on
											self.assertListEqual(__snake_case       ,     self.big_tokenizer.encode(__snake_case       )       )


					@require_torch
					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											import torch

											from transformers import BigBirdConfig, BigBirdModel

											# Build sequence
											_snake_case:   Tuple  					=		list(self.big_tokenizer.get_vocab().keys()       )[:10]
											_snake_case:   Union[str, Any]  					=		' '.join(__snake_case       )
											_snake_case:   Optional[Any]  					=		self.big_tokenizer.encode_plus(__snake_case       ,     return_tensors='pt'       ,     return_token_type_ids=__snake_case       )
											_snake_case:   int  					=		self.big_tokenizer.batch_encode_plus(
											    [sequence + ' ' + sequence]       ,     return_tensors='pt'       ,     return_token_type_ids=__snake_case       )

											_snake_case:   int  					=		BigBirdConfig(attention_type='original_full'       )
											_snake_case:   int  					=		BigBirdModel(__snake_case       )

											assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

											with torch.no_grad():
																	model(**__snake_case       )
																	model(**__snake_case       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'       )
											_snake_case:   Optional[Any]  					=		tokenizer.decode(tokenizer('Paris is the [MASK].'       ).input_ids       )

											self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		{'input_ids': [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=__snake_case       ,     model_name='google/bigbird-roberta-base'       ,     revision='215c99f1600e06f83acce68422f2035b2b5c3510'       ,     )





