
"""simple docstring"""







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

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization 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_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
							from fairscale.optim import OSS


lowerCamelCase__   					=		logging.get_logger(__name__)

lowerCamelCase__   					=		{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			super().__init__(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			if config is None:
				assert isinstance(self.model       ,		_SCREAMING_SNAKE_CASE ), (
				    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
				    f" {self.model.__class__}"
				)
				__lowerCAmelCase			:						Any									=      self.model.config
			else:
				__lowerCAmelCase			:						int									=      config

			__lowerCAmelCase			:						Any									=      data_args
			__lowerCAmelCase			:						int									=      self.config.tgt_vocab_size if isinstance(self.config       ,		_SCREAMING_SNAKE_CASE ) else self.config.vocab_size

			if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
				assert self.config.pad_token_id is not None, (
				    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
				    " calculation or doing label smoothing."
				)

			if self.config.pad_token_id is None and self.config.eos_token_id is not None:
				logger.warning(
				    f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
				    ' padding..' )

			if self.args.label_smoothing == 0:
				__lowerCAmelCase			:						int									=      torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
			else:
				# dynamically import label_smoothed_nll_loss
				from utils import label_smoothed_nll_loss

				__lowerCAmelCase			:						int									=      label_smoothed_nll_loss

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if self.optimizer is None:
				__lowerCAmelCase			:						Optional[int]									=      ['bias', 'LayerNorm.weight']
				__lowerCAmelCase			:						Dict									=      [
				    {
				        'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
				        'weight_decay': self.args.weight_decay,
				    },
				    {
				        'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
				        'weight_decay': 0.0,
				    },
				]
				__lowerCAmelCase			:						List[Any]									=      Adafactor if self.args.adafactor else AdamW
				if self.args.adafactor:
					__lowerCAmelCase			:						int									=      Adafactor
					__lowerCAmelCase			:						List[Any]									=      {'scale_parameter': False, 'relative_step': False}
				else:
					__lowerCAmelCase			:						Any									=      AdamW
					__lowerCAmelCase			:						int									=      {
					    'betas': (self.args.adam_betaa, self.args.adam_betaa),
					    'eps': self.args.adam_epsilon,
					}
				__lowerCAmelCase			:						List[Any]									=      self.args.learning_rate
				if self.sharded_ddp:
					__lowerCAmelCase			:						Optional[Any]									=      OSS(
					    params=_SCREAMING_SNAKE_CASE       ,		optim=_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE       ,		)
				else:
					__lowerCAmelCase			:						int									=      optimizer_cls(_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			if self.lr_scheduler is None:
				__lowerCAmelCase			:						Any									=      self._get_lr_scheduler(_SCREAMING_SNAKE_CASE )
			else:  # ignoring --lr_scheduler
				logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.' )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						int									=      arg_to_scheduler[self.args.lr_scheduler]
			if self.args.lr_scheduler == "constant":
				__lowerCAmelCase			:						Union[str, Any]									=      schedule_func(self.optimizer )
			elif self.args.lr_scheduler == "constant_w_warmup":
				__lowerCAmelCase			:						Optional[Any]									=      schedule_func(self.optimizer       ,		num_warmup_steps=self.args.warmup_steps )
			else:
				__lowerCAmelCase			:						List[str]									=      schedule_func(
				    self.optimizer       ,		num_warmup_steps=self.args.warmup_steps       ,		num_training_steps=_SCREAMING_SNAKE_CASE )
			return scheduler

		def 							__lowerCamelCase							(      self ):
			if isinstance(self.train_dataset       ,		torch.utils.data.IterableDataset ):
				return None
			elif is_torch_tpu_available():
				return get_tpu_sampler(self.train_dataset )
			else:
				if self.args.sortish_sampler:
					self.train_dataset.make_sortish_sampler(
					    self.args.per_device_train_batch_size       ,		distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)       ,		)

				return (
				    RandomSampler(self.train_dataset )
				    if self.args.local_rank == -1
				    else DistributedSampler(self.train_dataset )
				)

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			if self.args.label_smoothing == 0:
				if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
					# force training to ignore pad token
					__lowerCAmelCase			:						Optional[int]									=      model(**_SCREAMING_SNAKE_CASE       ,		use_cache=_SCREAMING_SNAKE_CASE )[0]
					__lowerCAmelCase			:						Optional[Any]									=      self.loss_fn(logits.view(-1       ,		logits.shape[-1] )       ,		labels.view(-1 ) )
				else:
					# compute usual loss via models
					__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      model(**_SCREAMING_SNAKE_CASE       ,		labels=_SCREAMING_SNAKE_CASE       ,		use_cache=_SCREAMING_SNAKE_CASE )[:2]
			else:
				# compute label smoothed loss
				__lowerCAmelCase			:						int									=      model(**_SCREAMING_SNAKE_CASE       ,		use_cache=_SCREAMING_SNAKE_CASE )[0]
				__lowerCAmelCase			:						Optional[int]									=      torch.nn.functional.log_softmax(_SCREAMING_SNAKE_CASE       ,		dim=-1 )
				__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      self.loss_fn(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		self.args.label_smoothing       ,		ignore_index=self.config.pad_token_id )
			return loss, logits

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Tuple									=      inputs.pop('labels' )
			__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      self._compute_loss(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			return loss

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			__lowerCAmelCase			:						Tuple									=      self._prepare_inputs(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[Any]									=      {
			    'max_length': self.data_args.val_max_target_length
			    if self.data_args is not None
			    else self.config.max_length,
			    'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
			}

			if self.args.predict_with_generate and not self.args.prediction_loss_only:
				__lowerCAmelCase			:						Tuple									=      self.model.generate(
				    inputs['input_ids']       ,		attention_mask=inputs['attention_mask']       ,		**_SCREAMING_SNAKE_CASE       ,		)
				# in case the batch is shorter than max length, the output should be padded
				if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
					__lowerCAmelCase			:						Optional[Any]									=      self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE       ,		gen_kwargs['max_length'] )

			__lowerCAmelCase			:						Any									=      inputs.pop('labels' )
			with torch.no_grad():
				# compute loss on predict data
				__lowerCAmelCase			,   __lowerCAmelCase			:						List[Any]									=      self._compute_loss(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Tuple									=      loss.mean().detach()
			if self.args.prediction_loss_only:
				return (loss, None, None)

			__lowerCAmelCase			:						str									=      generated_tokens if self.args.predict_with_generate else logits

			if labels.shape[-1] < gen_kwargs["max_length"]:
				__lowerCAmelCase			:						Tuple									=      self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE       ,		gen_kwargs['max_length'] )

			return (loss, logits, labels)







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			# If PAD token is not defined at least EOS token has to be defined
			__lowerCAmelCase			:						Any									=      self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

			if pad_token_id is None:
				raise ValueError(
				    'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
				    f" padded to `max_length`={max_length}" )

			__lowerCAmelCase			:						Tuple									=      pad_token_id * torch.ones(
			    (tensor.shape[0], max_length)       ,		dtype=tensor.dtype       ,		device=tensor.device )
			__lowerCAmelCase			:						Dict									=      tensor
			return padded_tensor




"""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


lowercase__    :  Dict							    =	get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			str							=		XLMRobertaTokenizer
 _lowerCAmelCase   :			int							=		XLMRobertaTokenizerFast
 _lowerCAmelCase   :			str							=		True
 _lowerCAmelCase   :			Dict							=		True

 def 						_snake_case					(     self		:       List[Any]				):
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_ :     List[str]				=	XLMRobertaTokenizer(lowercase_  ,     keep_accents=lowercase_				)
     tokenizer.save_pretrained(self.tmpdirname				)

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     List[Any]				=	'''<pad>'''
     snake_case_ :     Optional[int]				=	1

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

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     Dict				=	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(lowercase_				)  ,     1002				)

 def 						_snake_case					(     self		:       Union[str, Any]				):
     self.assertEqual(self.get_tokenizer().vocab_size  ,     1002				)

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Optional[Any]				=	XLMRobertaTokenizer(lowercase_  ,     keep_accents=lowercase_				)

     snake_case_ :     Dict				=	tokenizer.tokenize('''This is a test'''				)
     self.assertListEqual(lowercase_  ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowercase_				)  ,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,     )

     snake_case_ :     Dict				=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
     self.assertListEqual(
         lowercase_  ,     [
             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(lowercase_				)
     self.assertListEqual(
         lowercase_  ,     [
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
         ]  ,     )

     snake_case_ :     List[str]				=	tokenizer.convert_ids_to_tokens(lowercase_				)
     self.assertListEqual(
         lowercase_  ,     [
             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 						_snake_case					(     self		:       List[str]				):
     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

     snake_case_ :     int				=	(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})"				):
             snake_case_ :     Union[str, Any]				=	self.rust_tokenizer_class.from_pretrained(lowercase_  ,     **lowercase_				)
             snake_case_ :     int				=	self.tokenizer_class.from_pretrained(lowercase_  ,     **lowercase_				)

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

             snake_case_ :     Tuple				=	tokenizer_r.save_pretrained(lowercase_				)
             snake_case_ :     List[str]				=	tokenizer_p.save_pretrained(lowercase_				)

             # 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				)				)
             snake_case_ :     str				=	tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
             self.assertSequenceEqual(lowercase_  ,     lowercase_				)

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

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

             shutil.rmtree(lowercase_				)

             # Save tokenizer rust, legacy_format=True
             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     List[str]				=	tokenizer_r.save_pretrained(lowercase_  ,     legacy_format=lowercase_				)
             snake_case_ :     List[str]				=	tokenizer_p.save_pretrained(lowercase_				)

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

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

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

             shutil.rmtree(lowercase_				)

             # Save tokenizer rust, legacy_format=False
             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     List[Any]				=	tokenizer_r.save_pretrained(lowercase_  ,     legacy_format=lowercase_				)
             snake_case_ :     Tuple				=	tokenizer_p.save_pretrained(lowercase_				)

             # 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
             snake_case_ :     Optional[Any]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     Dict				=	tokenizer_p.from_pretrained(lowercase_				)

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

             shutil.rmtree(lowercase_				)

 @cached_property
 def 						_snake_case					(     self		:       List[str]				):
     return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(lowercase_  ,     f.name				)
         snake_case_ :     Any				=	XLMRobertaTokenizer(f.name  ,     keep_accents=lowercase_				)
         snake_case_ :     List[Any]				=	pickle.dumps(lowercase_				)
     pickle.loads(lowercase_				)

 def 						_snake_case					(     self		:       Tuple				):
     if not self.test_rust_tokenizer:
         return

     snake_case_ :     List[str]				=	self.get_tokenizer()
     snake_case_ :     Optional[int]				=	self.get_rust_tokenizer()

     snake_case_ :     Dict				=	'''I was born in 92000, and this is falsé.'''

     snake_case_ :     Optional[int]				=	tokenizer.tokenize(lowercase_				)
     snake_case_ :     Tuple				=	rust_tokenizer.tokenize(lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

     snake_case_ :     List[str]				=	tokenizer.encode(lowercase_  ,     add_special_tokens=lowercase_				)
     snake_case_ :     str				=	rust_tokenizer.encode(lowercase_  ,     add_special_tokens=lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

     snake_case_ :     int				=	self.get_rust_tokenizer()
     snake_case_ :     Any				=	tokenizer.encode(lowercase_				)
     snake_case_ :     int				=	rust_tokenizer.encode(lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

 @slow
 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     int				=	'''Hello World!'''
     snake_case_ :     int				=	[0, 35378, 6661, 38, 2]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

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

 @slow
 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     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'''
     )
     snake_case_ :     Optional[int]				=	[
         0,
         3293,
         83,
         10,
         4552,
         4989,
         7986,
         678,
         10,
         5915,
         111,
         179459,
         124850,
         4,
         6044,
         237,
         12,
         6,
         5,
         6,
         4,
         6780,
         705,
         15,
         1388,
         44,
         378,
         10114,
         711,
         152,
         20,
         6,
         5,
         22376,
         642,
         1221,
         15190,
         34153,
         450,
         5608,
         959,
         1119,
         57702,
         136,
         186,
         47,
         1098,
         29367,
         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,
         6044,
         237,
         6284,
         50901,
         528,
         31,
         90,
         34,
         927,
         2,
     ]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

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






 @slow
 def 						_snake_case					(     self		:       Dict				):
     # fmt: off
     snake_case_ :     int				=	{'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=lowercase_  ,     model_name='''xlm-roberta-base'''  ,     revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''  ,     )





