


"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.ctrl.modeling_tf_ctrl import (
					    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFCTRLForSequenceClassification,
					    TFCTRLLMHeadModel,
					    TFCTRLModel,
					)






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							)			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:int				    =  parent
					_UpperCamelCase				:Dict				    =  13
					_UpperCamelCase				:List[str]				    =  7
					_UpperCamelCase				:int				    =  True
					_UpperCamelCase				:List[Any]				    =  True
					_UpperCamelCase				:Any				    =  True
					_UpperCamelCase				:List[str]				    =  True
					_UpperCamelCase				:Optional[int]				    =  True
					_UpperCamelCase				:int				    =  99
					_UpperCamelCase				:Optional[int]				    =  32
					_UpperCamelCase				:List[Any]				    =  2
					_UpperCamelCase				:List[str]				    =  4
					_UpperCamelCase				:Dict				    =  37
					_UpperCamelCase				:Tuple				    =  '''gelu'''
					_UpperCamelCase				:Optional[int]				    =  0.1
					_UpperCamelCase				:List[str]				    =  0.1
					_UpperCamelCase				:List[str]				    =  5_12
					_UpperCamelCase				:Optional[Any]				    =  16
					_UpperCamelCase				:Tuple				    =  2
					_UpperCamelCase				:Optional[int]				    =  0.0_2
					_UpperCamelCase				:List[str]				    =  3
					_UpperCamelCase				:List[str]				    =  4
					_UpperCamelCase				:int				    =  None
					_UpperCamelCase				:Tuple				    =  self.vocab_size - 1
		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size      )

					_UpperCamelCase				:str				    =  None
					if self.use_input_mask:
								_UpperCamelCase				:Optional[int]				    =  random_attention_mask([self.batch_size, self.seq_length]      )

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

					_UpperCamelCase				:int				    =  None
					if self.use_mc_token_ids:
								_UpperCamelCase				:Optional[Any]				    =  ids_tensor([self.batch_size, self.num_choices]				,							self.seq_length      )

					_UpperCamelCase				:Union[str, Any]				    =  None
					_UpperCamelCase				:Tuple				    =  None
					_UpperCamelCase				:Tuple				    =  None
					if self.use_labels:
								_UpperCamelCase				:Union[str, Any]				    =  ids_tensor([self.batch_size]				,							self.type_sequence_label_size      )
								_UpperCamelCase				:Union[str, Any]				    =  ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels      )
								_UpperCamelCase				:Tuple				    =  ids_tensor([self.batch_size]				,							self.num_choices      )

					_UpperCamelCase				:Any				    =  CTRLConfig(
					    vocab_size=self.vocab_size				,							n_embd=self.hidden_size				,							n_layer=self.num_hidden_layers				,							n_head=self.num_attention_heads				,							n_positions=self.max_position_embeddings				,							pad_token_id=self.pad_token_id				,							)

					_UpperCamelCase				:List[Any]				    =  ids_tensor([self.num_hidden_layers, self.num_attention_heads]				,							2      )

					return (
					    config,
					    input_ids,
					    input_mask,
					    head_mask,
					    token_type_ids,
					    mc_token_ids,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					)
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  TFCTRLModel(config=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
					_UpperCamelCase				:List[str]				    =  model(SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Union[str, Any]				    =  [input_ids, None, input_mask]  # None is the input for 'past'
					_UpperCamelCase				:str				    =  model(SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:str				    =  model(SCREAMING_SNAKE_CASE__      )

					self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  TFCTRLLMHeadModel(config=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
					_UpperCamelCase				:List[str]				    =  model(SCREAMING_SNAKE_CASE__      )
					self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__      )			->  Any:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  self.num_labels
					_UpperCamelCase				:str				    =  ids_tensor([self.batch_size]				,							self.type_sequence_label_size      )
					_UpperCamelCase				:int				    =  {
					    '''input_ids''': input_ids,
					    '''token_type_ids''': token_type_ids,
					    '''labels''': sequence_labels,
					}
					_UpperCamelCase				:str				    =  TFCTRLForSequenceClassification(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  model(SCREAMING_SNAKE_CASE__      )
					self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)      )





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

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  self.prepare_config_and_inputs()

					(
					    (
					    _UpperCamelCase
					)    ,			(
					    _UpperCamelCase
					)    ,			(
					    _UpperCamelCase
					)    ,			(
					    _UpperCamelCase
					)    ,			(
					    _UpperCamelCase
					)    ,			(
					    _UpperCamelCase
					)    ,			(
					    _UpperCamelCase
					)    ,			(
					    _UpperCamelCase
					)    ,			(
					    _UpperCamelCase
					)    ,			
					)				:Union[str, Any]				    =  config_and_inputs

					_UpperCamelCase				:List[str]				    =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
					return config, inputs_dict






@require_tf
class     A(     lowerCamelCase__							,       lowerCamelCase__							,       unittest.TestCase		):

		"""simple docstring"""





		A         =       (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
		A         =       (TFCTRLLMHeadModel,) if is_tf_available() else ()
		A         =       (
		    {
		        "feature-extraction": TFCTRLModel,
		        "text-classification": TFCTRLForSequenceClassification,
		        "text-generation": TFCTRLLMHeadModel,
		        "zero-shot": TFCTRLForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		A         =       False
		A         =       False
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  str:

					"""simple docstring"""



					if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
								# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
								# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
								# config could not be created.
								return True

					return False
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  TFCTRLModelTester(self      )
					_UpperCamelCase				:Dict				    =  ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE__				,							n_embd=37      )
		def      _UpperCamelCase(   self      )			->  Optional[Any]:

					"""simple docstring"""



					self.config_tester.run_common_tests()
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:int				    =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_ctrl_model(*SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_ctrl_lm_head(*SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_ctrl_for_sequence_classification(*SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Tuple:

					"""simple docstring"""



					_UpperCamelCase    ,			_UpperCamelCase				:str				    =  self.model_tester.prepare_config_and_inputs_for_common()
					_UpperCamelCase				:Tuple				    =  [TFCTRLLMHeadModel]
					_UpperCamelCase				:Optional[int]				    =  [TFCTRLForSequenceClassification]

					for model_class in self.all_model_classes:
								_UpperCamelCase				:int				    =  model_class(SCREAMING_SNAKE_CASE__      )
								model.build()  # may be needed for the get_bias() call below
								assert isinstance(model.get_input_embeddings()				,							tf.keras.layers.Layer      )

								if model_class in list_lm_models:
											_UpperCamelCase				:Any				    =  model.get_output_embeddings()
											assert isinstance(SCREAMING_SNAKE_CASE__				,							tf.keras.layers.Layer      )
											_UpperCamelCase				:Tuple				    =  model.get_bias()
											assert isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
											for k, v in name.items():
														assert isinstance(SCREAMING_SNAKE_CASE__				,							tf.Variable      )
								elif model_class in list_other_models_with_output_ebd:
											_UpperCamelCase				:Any				    =  model.get_output_embeddings()
											assert isinstance(SCREAMING_SNAKE_CASE__				,							tf.keras.layers.Layer      )
											_UpperCamelCase				:Optional[int]				    =  model.get_bias()
											assert name is None
								else:
											_UpperCamelCase				:Any				    =  model.get_output_embeddings()
											assert x is None
											_UpperCamelCase				:Dict				    =  model.get_bias()
											assert name is None





		@slow
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								_UpperCamelCase				:Optional[int]				    =  TFCTRLModel.from_pretrained(SCREAMING_SNAKE_CASE__      )
								self.assertIsNotNone(SCREAMING_SNAKE_CASE__      )






@require_tf
class     A(     unittest.TestCase		):

		"""simple docstring"""





		@slow
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:int				    =  TFCTRLLMHeadModel.from_pretrained('''ctrl'''      )
					_UpperCamelCase				:Dict				    =  tf.convert_to_tensor([[1_18_59, 0, 16_11, 8]]				,							dtype=tf.intaa      )  # Legal the president is
					_UpperCamelCase				:Union[str, Any]				    =  [
					    1_18_59,
					    0,
					    16_11,
					    8,
					    5,
					    1_50,
					    2_64_49,
					    2,
					    19,
					    3_48,
					    4_69,
					    3,
					    25_95,
					    48,
					    2_07_40,
					    24_65_33,
					    24_65_33,
					    19,
					    30,
					    5,
					]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

					_UpperCamelCase				:str				    =  model.generate(SCREAMING_SNAKE_CASE__				,							do_sample=SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(output_ids[0].numpy().tolist()				,							SCREAMING_SNAKE_CASE__      )





"""simple docstring"""






import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__							:Optional[int]       =  get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class     A(     lowerCamelCase__							,       unittest.TestCase		):

		"""simple docstring"""





		A         =       ErnieMTokenizer
		A         =       False
		A         =       True
		A         =       False
		A         =       False
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					# We have a SentencePiece fixture for testing
					_UpperCamelCase				:int				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )
					tokenizer.save_pretrained(self.tmpdirname      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  '''this is a test'''
					_UpperCamelCase				:Optional[Any]				    =  '''this is a test'''
					return input_text, output_text
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  '''<pad>'''
					_UpperCamelCase				:Optional[Any]				    =  0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



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

					self.assertEqual(vocab_keys[0]				,							'''<pad>'''      )
					self.assertEqual(vocab_keys[1]				,							'''<unk>'''      )
					self.assertEqual(vocab_keys[-1]				,							'''▁eloquent'''      )
					self.assertEqual(len(SCREAMING_SNAKE_CASE__      )				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					self.assertEqual(self.get_tokenizer().vocab_size				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					if not self.test_rust_tokenizer:
								return

					_UpperCamelCase				:Tuple				    =  self.get_tokenizer()
					_UpperCamelCase				:str				    =  self.get_rust_tokenizer()

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

					_UpperCamelCase				:Any				    =  tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[Any]				    =  rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

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

					_UpperCamelCase				:Any				    =  self.get_rust_tokenizer()
					_UpperCamelCase				:List[Any]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							do_lower_case=SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:List[str]				    =  tokenizer.tokenize('''This is a test'''      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							['''▁this''', '''▁is''', '''▁a''', '''▁test''']      )

					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )				,							[48, 25, 21, 12_89]      )

					_UpperCamelCase				:str				    =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
					# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']      )
					_UpperCamelCase				:Optional[int]				    =  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]      )

					_UpperCamelCase				:Dict				    =  tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']				,							)
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:str				    =  tokenizer.encode('''sequence builders'''      )
					_UpperCamelCase				:Optional[Any]				    =  tokenizer.encode('''multi-sequence build'''      )

					_UpperCamelCase				:int				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
					assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
					    tokenizer.sep_token_id
					] + text_a + [tokenizer.sep_token_id]





		@slow
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  {'''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, 9, 3_04, 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, 5, 5, 5, 16, 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, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 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, 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, 1, 1, 1, 1]]}
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=SCREAMING_SNAKE_CASE__				,							model_name='''susnato/ernie-m-base_pytorch'''				,							sequences=[
					        '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
					        '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
					        '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
					        '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
					        '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
					        '''conditioning on both left and right context in all layers.''',
					        '''The quick brown fox jumps over the lazy dog.''',
					    ]				,							)

