

'''simple docstring'''



import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    Adafactor,
		    AdamW,
		    get_constant_schedule,
		    get_constant_schedule_with_warmup,
		    get_cosine_schedule_with_warmup,
		    get_cosine_with_hard_restarts_schedule_with_warmup,
		    get_inverse_sqrt_schedule,
		    get_linear_schedule_with_warmup,
		    get_polynomial_decay_schedule_with_warmup,
		)
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		Optional[int]=10	)	->							List[str]:






						"""simple docstring"""





						A__					:       List[Any]     =[]
						for _ in range(__snake_case	):
												lrs.append(scheduler.get_lr()[0]	)
												scheduler.step()
						return lrs
def 						__lowerCamelCase	(       __snake_case					:		int,		__snake_case					:		Tuple=10	)	->							Tuple:






						"""simple docstring"""





						A__					:       List[str]     =[]
						for step in range(__snake_case	):
												lrs.append(scheduler.get_lr()[0]	)
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								A__					:       Union[str, Any]     =os.path.join(__snake_case,		"""schedule.bin"""	)
																								torch.save(scheduler.state_dict(),		__snake_case	)

																								A__					:       Dict     =torch.load(__snake_case	)
																								scheduler.load_state_dict(__snake_case	)
						return lrs



@require_torch
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Optional[int]			)      ->		Tuple:





									'''simple docstring'''
									self.assertEqual(len(lowerCAmelCase_			)		,      len(lowerCAmelCase_			)			)
									for a, b in zip(lowerCAmelCase_		,      lowerCAmelCase_			):
															self.assertAlmostEqual(lowerCAmelCase_		,      lowerCAmelCase_		,      delta=lowerCAmelCase_			)


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





									'''simple docstring'''
									A__					:       Union[str, Any]     =torch.tensor([0.1, -0.2, -0.1]		,      requires_grad=lowerCAmelCase_			)
									A__					:       Optional[int]     =torch.tensor([0.4, 0.2, -0.5]			)
									A__					:       List[Any]     =nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									A__					:       Any     =AdamW(params=[w]		,      lr=2e-1		,      weight_decay=0.0			)
									for _ in range(1_00			):
															A__					:       Union[str, Any]     =criterion(lowerCAmelCase_		,      lowerCAmelCase_			)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()		,      [0.4, 0.2, -0.5]		,      tol=1e-2			)



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





									'''simple docstring'''
									A__					:       Tuple     =torch.tensor([0.1, -0.2, -0.1]		,      requires_grad=lowerCAmelCase_			)
									A__					:       Any     =torch.tensor([0.4, 0.2, -0.5]			)
									A__					:       List[str]     =nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									A__					:       Optional[Any]     =Adafactor(
									    params=[w]		,      lr=1e-2		,      eps=(1e-30, 1e-3)		,      clip_threshold=1.0		,      decay_rate=-0.8		,      betaa=lowerCAmelCase_		,      weight_decay=0.0		,      relative_step=lowerCAmelCase_		,      scale_parameter=lowerCAmelCase_		,      warmup_init=lowerCAmelCase_		,      )
									for _ in range(10_00			):
															A__					:       int     =criterion(lowerCAmelCase_		,      lowerCAmelCase_			)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()		,      [0.4, 0.2, -0.5]		,      tol=1e-2			)



@require_torch
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	nn.Linear(50	,						50						) if is_torch_available() else None
			__snake_case        =	AdamW(m.parameters()	,						lr=10.0						) if is_torch_available() else None
			__snake_case        =	10


			def lowercase__						(				self	:						str		,      lowerCAmelCase_	:						List[Any]		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						int=None			)      ->		Any:





									'''simple docstring'''
									self.assertEqual(len(lowerCAmelCase_			)		,      len(lowerCAmelCase_			)			)
									for a, b in zip(lowerCAmelCase_		,      lowerCAmelCase_			):
															self.assertAlmostEqual(lowerCAmelCase_		,      lowerCAmelCase_		,      delta=lowerCAmelCase_		,      msg=lowerCAmelCase_			)



			def lowercase__						(				self	:						int			)      ->		int:





									'''simple docstring'''
									A__					:       str     ={"""num_warmup_steps""": 2, """num_training_steps""": 10}
									# schedulers doct format
									# function: (sched_args_dict, expected_learning_rates)
									A__					:       List[str]     ={
									    get_constant_schedule: ({}, [10.0] * self.num_steps),
									    get_constant_schedule_with_warmup: (
									        {"""num_warmup_steps""": 4},
									        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
									    ),
									    get_linear_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
									    ),
									    get_cosine_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
									    ),
									    get_cosine_with_hard_restarts_schedule_with_warmup: (
									        {**common_kwargs, """num_cycles""": 2},
									        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
									    ),
									    get_polynomial_decay_schedule_with_warmup: (
									        {**common_kwargs, """power""": 2.0, """lr_end""": 1e-7},
									        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
									    ),
									    get_inverse_sqrt_schedule: (
									        {"""num_warmup_steps""": 2},
									        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
									    ),
									}

									for scheduler_func, data in scheds.items():
															A__   ,						A__					:       Tuple     =data

															A__					:       Tuple     =scheduler_func(self.optimizer		,      **lowerCAmelCase_			)
															self.assertEqual(len([scheduler.get_lr()[0]]			)		,      1			)
															A__					:       Any     =unwrap_schedule(lowerCAmelCase_		,      self.num_steps			)
															self.assertListAlmostEqual(
															    lowerCAmelCase_		,      lowerCAmelCase_		,      tol=1e-2		,      msg=f"failed for {scheduler_func} in normal scheduler"		,      )

															A__					:       Tuple     =scheduler_func(self.optimizer		,      **lowerCAmelCase_			)
															if scheduler_func.__name__ != "get_constant_schedule":
																					LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase_			)  # wrap to test picklability of the schedule
															A__					:       Tuple     =unwrap_and_save_reload_schedule(lowerCAmelCase_		,      self.num_steps			)
															self.assertListEqual(lowerCAmelCase_		,      lowerCAmelCase_		,      msg=f"failed for {scheduler_func} in save and reload"			)



class lowerCamelCase			:



			'''simple docstring'''






			def __init__(				self	:						int		,      lowerCAmelCase_	:						Union[str, Any]			)      ->		Any:





									'''simple docstring'''
									A__					:       Any     =fn


			def __call__(				self	:						Any		,      *lowerCAmelCase_	:						Dict		,      **lowerCAmelCase_	:						int			)      ->		Dict:





									'''simple docstring'''
									return self.fn(*lowerCAmelCase_		,      **lowerCAmelCase_			)



			@classmethod
			def lowercase__						(				self	:						List[Any]		,      lowerCAmelCase_	:						List[Any]			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       str     =list(map(self		,      scheduler.lr_lambdas			)			)




'''simple docstring'''



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case			:     List[str]										=      get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
@require_tokenizers
class lowerCamelCase			(  lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	XLMRobertaTokenizer
			__snake_case        =	XLMRobertaTokenizerFast
			__snake_case        =	True
			__snake_case        =	True


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





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

									# We have a SentencePiece fixture for testing
									A__					:       Any     =XLMRobertaTokenizer(lowerCAmelCase_		,      keep_accents=lowerCAmelCase_			)
									tokenizer.save_pretrained(self.tmpdirname			)


			def lowercase__						(				self	:						Optional[Any]			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Union[str, Any]     ="""<pad>"""
									A__					:       Any     =1

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


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





									'''simple docstring'''
									A__					:       int     =list(self.get_tokenizer().get_vocab().keys()			)

									self.assertEqual(vocab_keys[0]		,      """<s>"""			)
									self.assertEqual(vocab_keys[1]		,      """<pad>"""			)
									self.assertEqual(vocab_keys[-1]		,      """<mask>"""			)
									self.assertEqual(len(lowerCAmelCase_			)		,      10_02			)


			def lowercase__						(				self	:						Optional[int]			)      ->		Optional[Any]:





									'''simple docstring'''
									self.assertEqual(self.get_tokenizer().vocab_size		,      10_02			)


			def lowercase__						(				self	:						Tuple			)      ->		Any:





									'''simple docstring'''
									A__					:       List[Any]     =XLMRobertaTokenizer(lowerCAmelCase_		,      keep_accents=lowerCAmelCase_			)

									A__					:       Tuple     =tokenizer.tokenize("""This is a test"""			)
									self.assertListEqual(lowerCAmelCase_		,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCAmelCase_			)		,      [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]		,      )

									A__					:       Optional[int]     =tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
									self.assertListEqual(
									    lowerCAmelCase_		,      [
									        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""",
									        """.""",
									    ]		,      )
									A__					:       Optional[int]     =tokenizer.convert_tokens_to_ids(lowerCAmelCase_			)
									self.assertListEqual(
									    lowerCAmelCase_		,      [
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
									    ]		,      )

									A__					:       Union[str, Any]     =tokenizer.convert_ids_to_tokens(lowerCAmelCase_			)
									self.assertListEqual(
									    lowerCAmelCase_		,      [
									        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>""",
									        """.""",
									    ]		,      )


			def lowercase__						(				self	:						List[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									if not self.test_slow_tokenizer:
															# as we don't have a slow version, we can't compare the outputs between slow and fast versions
															return

									A__					:       Dict     =(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
																					A__					:       List[str]     =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_		,      **lowerCAmelCase_			)
																					A__					:       Union[str, Any]     =self.tokenizer_class.from_pretrained(lowerCAmelCase_		,      **lowerCAmelCase_			)

																					A__					:       Optional[Any]     =tempfile.mkdtemp()

																					A__					:       Union[str, Any]     =tokenizer_r.save_pretrained(lowerCAmelCase_			)
																					A__					:       Union[str, Any]     =tokenizer_p.save_pretrained(lowerCAmelCase_			)

																					# Checks it save with the same files + the tokenizer.json file for the fast one
																					self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)
																					A__					:       List[str]     =tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
																					self.assertSequenceEqual(lowerCAmelCase_		,      lowerCAmelCase_			)

																					# Checks everything loads correctly in the same way
																					A__					:       Any     =tokenizer_r.from_pretrained(lowerCAmelCase_			)
																					A__					:       Union[str, Any]     =tokenizer_p.from_pretrained(lowerCAmelCase_			)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(lowerCAmelCase_		,      lowerCAmelCase_			)			)
																											# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																											# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																					shutil.rmtree(lowerCAmelCase_			)

																					# Save tokenizer rust, legacy_format=True
																					A__					:       List[str]     =tempfile.mkdtemp()

																					A__					:       List[str]     =tokenizer_r.save_pretrained(lowerCAmelCase_		,      legacy_format=lowerCAmelCase_			)
																					A__					:       List[Any]     =tokenizer_p.save_pretrained(lowerCAmelCase_			)

																					# Checks it save with the same files
																					self.assertSequenceEqual(lowerCAmelCase_		,      lowerCAmelCase_			)

																					# Checks everything loads correctly in the same way
																					A__					:       str     =tokenizer_r.from_pretrained(lowerCAmelCase_			)
																					A__					:       List[Any]     =tokenizer_p.from_pretrained(lowerCAmelCase_			)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(lowerCAmelCase_		,      lowerCAmelCase_			)			)

																					shutil.rmtree(lowerCAmelCase_			)

																					# Save tokenizer rust, legacy_format=False
																					A__					:       List[str]     =tempfile.mkdtemp()

																					A__					:       Dict     =tokenizer_r.save_pretrained(lowerCAmelCase_		,      legacy_format=lowerCAmelCase_			)
																					A__					:       List[Any]     =tokenizer_p.save_pretrained(lowerCAmelCase_			)

																					# Checks it saved the tokenizer.json file
																					self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)

																					# Checks everything loads correctly in the same way
																					A__					:       Optional[int]     =tokenizer_r.from_pretrained(lowerCAmelCase_			)
																					A__					:       str     =tokenizer_p.from_pretrained(lowerCAmelCase_			)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(lowerCAmelCase_		,      lowerCAmelCase_			)			)

																					shutil.rmtree(lowerCAmelCase_			)


			@cached_property
			def lowercase__						(				self	:						Optional[Any]			)      ->		List[str]:





									'''simple docstring'''
									return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""			)


			def lowercase__						(				self	:						Tuple			)      ->		Tuple:





									'''simple docstring'''
									with tempfile.NamedTemporaryFile() as f:
															shutil.copyfile(lowerCAmelCase_		,      f.name			)
															A__					:       Dict     =XLMRobertaTokenizer(f.name		,      keep_accents=lowerCAmelCase_			)
															A__					:       Optional[Any]     =pickle.dumps(lowerCAmelCase_			)
									pickle.loads(lowerCAmelCase_			)


			def lowercase__						(				self	:						Optional[Any]			)      ->		List[Any]:





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

									A__					:       Any     =self.get_tokenizer()
									A__					:       Any     =self.get_rust_tokenizer()

									A__					:       Optional[Any]     ="""I was born in 92000, and this is falsé."""

									A__					:       List[str]     =tokenizer.tokenize(lowerCAmelCase_			)
									A__					:       int     =rust_tokenizer.tokenize(lowerCAmelCase_			)
									self.assertListEqual(lowerCAmelCase_		,      lowerCAmelCase_			)

									A__					:       str     =tokenizer.encode(lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_			)
									A__					:       Dict     =rust_tokenizer.encode(lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_			)
									self.assertListEqual(lowerCAmelCase_		,      lowerCAmelCase_			)

									A__					:       Union[str, Any]     =self.get_rust_tokenizer()
									A__					:       Union[str, Any]     =tokenizer.encode(lowerCAmelCase_			)
									A__					:       Optional[Any]     =rust_tokenizer.encode(lowerCAmelCase_			)
									self.assertListEqual(lowerCAmelCase_		,      lowerCAmelCase_			)


			@slow
			def lowercase__						(				self	:						Dict			)      ->		int:





									'''simple docstring'''
									A__					:       Optional[Any]     ="""Hello World!"""
									A__					:       Optional[Any]     =[0, 3_53_78, 66_61, 38, 2]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

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


			@slow
			def lowercase__						(				self	:						List[Any]			)      ->		int:





									'''simple docstring'''
									A__					:       List[Any]     =(
									    """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"""
									)
									A__					:       Optional[Any]     =[
									    0,
									    32_93,
									    83,
									    10,
									    45_52,
									    49_89,
									    79_86,
									    6_78,
									    10,
									    59_15,
									    1_11,
									    17_94_59,
									    12_48_50,
									    4,
									    60_44,
									    2_37,
									    12,
									    6,
									    5,
									    6,
									    4,
									    67_80,
									    7_05,
									    15,
									    13_88,
									    44,
									    3_78,
									    1_01_14,
									    7_11,
									    1_52,
									    20,
									    6,
									    5,
									    2_23_76,
									    6_42,
									    12_21,
									    1_51_90,
									    3_41_53,
									    4_50,
									    56_08,
									    9_59,
									    11_19,
									    5_77_02,
									    1_36,
									    1_86,
									    47,
									    10_98,
									    2_93_67,
									    47,
									    # 4426, # What fairseq tokenizes from "<unk>": "_<"
									    # 3678, # What fairseq tokenizes from "<unk>": "unk"
									    # 2740, # What fairseq tokenizes from "<unk>": ">"
									    3,  # What we tokenize from "<unk>": "<unk>"
									    6,  # Residue from the tokenization: an extra sentencepiece underline
									    4,
									    60_44,
									    2_37,
									    62_84,
									    5_09_01,
									    5_28,
									    31,
									    90,
									    34,
									    9_27,
									    2,
									]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

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



			@slow
			def lowercase__						(				self	:						int			)      ->		Optional[Any]:





									'''simple docstring'''
									# fmt: off
									A__					:       List[Any]     ={"""input_ids""": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 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], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=lowerCAmelCase_		,      model_name="""xlm-roberta-base"""		,      revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""		,      )

