



import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    LiltForQuestionAnswering,
	    LiltForSequenceClassification,
	    LiltForTokenClassification,
	    LiltModel,
	)
	from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST


class 					_lowercase      :
				'''simple docstring'''







				def __init__(       self   ,       snake_case__   ,       snake_case__=13   ,       snake_case__=7   ,       snake_case__=True   ,       snake_case__=True   ,       snake_case__=True   ,       snake_case__=True   ,       snake_case__=99   ,       snake_case__=24   ,       snake_case__=2   ,       snake_case__=6   ,       snake_case__=37   ,       snake_case__="gelu"   ,       snake_case__=0.1   ,       snake_case__=0.1   ,       snake_case__=512   ,       snake_case__=16   ,       snake_case__=2   ,       snake_case__=0.02   ,       snake_case__=3   ,       snake_case__=None   ,       snake_case__=1000   ,       ):

									'''simple docstring'''


									UpperCamelCase_						     =      parent
									UpperCamelCase_						     =      batch_size
									UpperCamelCase_						     =      seq_length
									UpperCamelCase_						     =      is_training
									UpperCamelCase_						     =      use_input_mask
									UpperCamelCase_						     =      use_token_type_ids
									UpperCamelCase_						     =      use_labels
									UpperCamelCase_						     =      vocab_size
									UpperCamelCase_						     =      hidden_size
									UpperCamelCase_						     =      num_hidden_layers
									UpperCamelCase_						     =      num_attention_heads
									UpperCamelCase_						     =      intermediate_size
									UpperCamelCase_						     =      hidden_act
									UpperCamelCase_						     =      hidden_dropout_prob
									UpperCamelCase_						     =      attention_probs_dropout_prob
									UpperCamelCase_						     =      max_position_embeddings
									UpperCamelCase_						     =      type_vocab_size
									UpperCamelCase_						     =      type_sequence_label_size
									UpperCamelCase_						     =      initializer_range
									UpperCamelCase_						     =      num_labels
									UpperCamelCase_						     =      scope
									UpperCamelCase_						     =      range_bbox

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size			)

									UpperCamelCase_						     =      ids_tensor([self.batch_size, self.seq_length, 4]   ,       self.range_bbox			)
									# Ensure that bbox is legal
									for i in range(bbox.shape[0]			):
														for j in range(bbox.shape[1]			):
																			if bbox[i, j, 3] < bbox[i, j, 1]:
																								UpperCamelCase_						     =      bbox[i, j, 3]
																								UpperCamelCase_						     =      bbox[i, j, 1]
																								UpperCamelCase_						     =      t
																			if bbox[i, j, 2] < bbox[i, j, 0]:
																								UpperCamelCase_						     =      bbox[i, j, 2]
																								UpperCamelCase_						     =      bbox[i, j, 0]
																								UpperCamelCase_						     =      t

									UpperCamelCase_						     =      None
									if self.use_input_mask:
														UpperCamelCase_						     =      ids_tensor([self.batch_size, self.seq_length]   ,       vocab_size=2			)

									UpperCamelCase_						     =      None
									if self.use_token_type_ids:
														UpperCamelCase_						     =      ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size			)

									UpperCamelCase_						     =      None
									UpperCamelCase_						     =      None
									if self.use_labels:
														UpperCamelCase_						     =      ids_tensor([self.batch_size]   ,       self.type_sequence_label_size			)
														UpperCamelCase_						     =      ids_tensor([self.batch_size, self.seq_length]   ,       self.num_labels			)

									UpperCamelCase_						     =      self.get_config()

									return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									return LiltConfig(
									    vocab_size=self.vocab_size   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       num_attention_heads=self.num_attention_heads   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       type_vocab_size=self.type_vocab_size   ,       initializer_range=self.initializer_range   ,       )

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       ):

									'''simple docstring'''


									UpperCamelCase_						     =      LiltModel(config=__UpperCAmelCase			)
									model.to(__UpperCAmelCase			)
									model.eval()
									UpperCamelCase_						     =      model(__UpperCAmelCase   ,       bbox=__UpperCAmelCase   ,       attention_mask=__UpperCAmelCase   ,       token_type_ids=__UpperCAmelCase			)
									UpperCamelCase_						     =      model(__UpperCAmelCase   ,       bbox=__UpperCAmelCase   ,       token_type_ids=__UpperCAmelCase			)
									UpperCamelCase_						     =      model(__UpperCAmelCase   ,       bbox=__UpperCAmelCase			)

									self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)			)
									self.parent.assertEqual(result.pooler_output.shape   ,       (self.batch_size, self.hidden_size)			)

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       ):

									'''simple docstring'''


									UpperCamelCase_						     =      self.num_labels
									UpperCamelCase_						     =      LiltForTokenClassification(config=__UpperCAmelCase			)
									model.to(__UpperCAmelCase			)
									model.eval()
									UpperCamelCase_						     =      model(
									    __UpperCAmelCase   ,       bbox=__UpperCAmelCase   ,       attention_mask=__UpperCAmelCase   ,       token_type_ids=__UpperCAmelCase   ,       labels=__UpperCAmelCase			)
									self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.num_labels)			)

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       ):

									'''simple docstring'''


									UpperCamelCase_						     =      LiltForQuestionAnswering(config=__UpperCAmelCase			)
									model.to(__UpperCAmelCase			)
									model.eval()
									UpperCamelCase_						     =      model(
									    __UpperCAmelCase   ,       bbox=__UpperCAmelCase   ,       attention_mask=__UpperCAmelCase   ,       token_type_ids=__UpperCAmelCase   ,       start_positions=__UpperCAmelCase   ,       end_positions=__UpperCAmelCase   ,       )
									self.parent.assertEqual(result.start_logits.shape   ,       (self.batch_size, self.seq_length)			)
									self.parent.assertEqual(result.end_logits.shape   ,       (self.batch_size, self.seq_length)			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.prepare_config_and_inputs()
									(
									    UpperCamelCase_				
									)						     =      config_and_inputs
									UpperCamelCase_						     =      {
									    'input_ids': input_ids,
									    'bbox': bbox,
									    'token_type_ids': token_type_ids,
									    'attention_mask': input_mask,
									}
									return config, inputs_dict




@require_torch
class 					_lowercase      (a_					,      a_					,      a_					,      unittest.TestCase					):
				'''simple docstring'''







				lowercase__        =  (
				    (
				        LiltModel,
				        LiltForSequenceClassification,
				        LiltForTokenClassification,
				        LiltForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__        =  (
				    {
				        """feature-extraction""": LiltModel,
				        """question-answering""": LiltForQuestionAnswering,
				        """text-classification""": LiltForSequenceClassification,
				        """token-classification""": LiltForTokenClassification,
				        """zero-shot""": LiltForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__        =  False
				lowercase__        =  False

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__			):

									'''simple docstring'''


									return True

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      LiltModelTester(self			)
									UpperCamelCase_						     =      ConfigTester(self   ,       config_class=__UpperCAmelCase   ,       hidden_size=37			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									self.config_tester.run_common_tests()

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCAmelCase			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
														UpperCamelCase_						     =      type
														self.model_tester.create_and_check_model(*__UpperCAmelCase			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase			)

				@slow
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCamelCase_						     =      LiltModel.from_pretrained(__UpperCAmelCase			)
														self.assertIsNotNone(__UpperCAmelCase			)




@require_torch
@slow
class 					_lowercase      (unittest.TestCase					):
				'''simple docstring'''







				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"			).to(__UpperCAmelCase			)

									UpperCamelCase_						     =      torch.tensor([[1, 2]]   ,       device=__UpperCAmelCase			)
									UpperCamelCase_						     =      torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]   ,       device=__UpperCAmelCase			)

									# forward pass
									with torch.no_grad():
														UpperCamelCase_						     =      model(input_ids=__UpperCAmelCase   ,       bbox=__UpperCAmelCase			)

									UpperCamelCase_						     =      torch.Size([1, 2, 768]			)
									UpperCamelCase_						     =      torch.tensor(
									    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]   ,       device=__UpperCAmelCase   ,       )

									self.assertTrue(outputs.last_hidden_state.shape   ,       __UpperCAmelCase			)
									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]   ,       __UpperCAmelCase   ,       atol=1e-3			)			)







"""simple docstring"""





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
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


__A											=		get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_sentencepiece
@require_tokenizers
class 					_lowerCAmelCase   (     a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :List[str]		  =					XGLMTokenizer
       __magic_name__     :Any		  =					XGLMTokenizerFast
       __magic_name__     :Dict		  =					True
       __magic_name__     :Union[str, Any]		  =					True






       def      snake_case						(    self      ):



         '''simple docstring'''

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCAmelCase__			:int											=							XGLMTokenizer(__UpperCAmelCase    , keep_accents=__UpperCAmelCase      )
         tokenizer.save_pretrained(self.tmpdirname      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							'<pad>'
         lowerCAmelCase__			:int											=							1

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							list(self.get_tokenizer().get_vocab().keys()      )

         self.assertEqual(vocab_keys[0]    , '<s>'      )
         self.assertEqual(vocab_keys[1]    , '<pad>'      )
         self.assertEqual(len(__UpperCAmelCase      )    , 1_0_0_8      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self.assertEqual(self.get_tokenizer().vocab_size    , 1_0_0_8      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							XGLMTokenizer(__UpperCAmelCase    , keep_accents=__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							tokenizer.tokenize('This is a test'      )
         self.assertListEqual(__UpperCAmelCase    , ['▁This', '▁is', '▁a', '▁t', 'est']      )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__UpperCAmelCase      )    , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]    , )

         lowerCAmelCase__			:int											=							tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 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',
                 '.',
             ]    , )
         lowerCAmelCase__			:Tuple											=							tokenizer.convert_tokens_to_ids(__UpperCAmelCase      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             ]    , )

         lowerCAmelCase__			:Optional[int]											=							tokenizer.convert_ids_to_tokens(__UpperCAmelCase      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 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      snake_case						(    self      ):



         '''simple docstring'''

         return XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         with tempfile.NamedTemporaryFile() as f:
           shutil.copyfile(__UpperCAmelCase    , f.name      )
           lowerCAmelCase__			:Dict											=							XGLMTokenizer(f.name    , keep_accents=__UpperCAmelCase      )
           lowerCAmelCase__			:List[Any]											=							pickle.dumps(__UpperCAmelCase      )
         pickle.loads(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         if not self.test_rust_tokenizer:
           return

         lowerCAmelCase__			:Optional[Any]											=							self.get_tokenizer()
         lowerCAmelCase__			:List[str]											=							self.get_rust_tokenizer()

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

         lowerCAmelCase__			:Dict											=							tokenizer.tokenize(__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							rust_tokenizer.tokenize(__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							tokenizer.encode(__UpperCAmelCase    , add_special_tokens=__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							rust_tokenizer.encode(__UpperCAmelCase    , add_special_tokens=__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:int											=							self.get_rust_tokenizer()
         lowerCAmelCase__			:Dict											=							tokenizer.encode(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							rust_tokenizer.encode(__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							'Hello World!'
         lowerCAmelCase__			:Tuple											=							[2, 3_1_2_2_7, 4_4_4_7, 3_5]

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






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							(
             '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
         lowerCAmelCase__			:List[str]											=							[2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
         # fmt: on

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






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							{
             'input_ids': [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
             'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
         }  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__UpperCAmelCase    , model_name='facebook/xglm-564M'    , padding=__UpperCAmelCase    , )
