"""simple docstring"""




import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
					import torch

					from transformers import (
					    MraForMaskedLM,
					    MraForMultipleChoice,
					    MraForQuestionAnswering,
					    MraForSequenceClassification,
					    MraForTokenClassification,
					    MraModel,
					)
					from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class 						__A							:






					def __init__(		self    ,					a__    ,					a__=2    ,					a__=8    ,					a__=True    ,					a__=True    ,					a__=True    ,					a__=True    ,					a__=99    ,					a__=16    ,					a__=5    ,					a__=2    ,					a__=36    ,					a__="gelu"    ,					a__=0.0    ,					a__=0.0    ,					a__=512    ,					a__=16    ,					a__=2    ,					a__=0.0_2    ,					a__=3    ,					a__=4    ,					a__=None    ,					):
							_lowerCAmelCase						:	List[str]        =					parent
							_lowerCAmelCase						:	int        =					batch_size
							_lowerCAmelCase						:	Optional[int]        =					seq_length
							_lowerCAmelCase						:	Any        =					is_training
							_lowerCAmelCase						:	List[Any]        =					use_input_mask
							_lowerCAmelCase						:	Union[str, Any]        =					use_token_type_ids
							_lowerCAmelCase						:	Optional[int]        =					use_labels
							_lowerCAmelCase						:	Dict        =					vocab_size
							_lowerCAmelCase						:	Optional[int]        =					hidden_size
							_lowerCAmelCase						:	List[Any]        =					num_hidden_layers
							_lowerCAmelCase						:	Tuple        =					num_attention_heads
							_lowerCAmelCase						:	Dict        =					intermediate_size
							_lowerCAmelCase						:	int        =					hidden_act
							_lowerCAmelCase						:	List[str]        =					hidden_dropout_prob
							_lowerCAmelCase						:	Any        =					attention_probs_dropout_prob
							_lowerCAmelCase						:	List[str]        =					max_position_embeddings
							_lowerCAmelCase						:	List[Any]        =					type_vocab_size
							_lowerCAmelCase						:	int        =					type_sequence_label_size
							_lowerCAmelCase						:	int        =					initializer_range
							_lowerCAmelCase						:	str        =					num_labels
							_lowerCAmelCase						:	List[Any]        =					num_choices
							_lowerCAmelCase						:	Optional[int]        =					scope






					def    __A  (		self  ):
							_lowerCAmelCase						:	List[str]        =					ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size  )

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

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

							_lowerCAmelCase						:	List[Any]        =					None
							_lowerCAmelCase						:	Optional[Any]        =					None
							_lowerCAmelCase						:	Optional[Any]        =					None
							if self.use_labels:
									_lowerCAmelCase						:	Union[str, Any]        =					ids_tensor([self.batch_size]    ,					self.type_sequence_label_size  )
									_lowerCAmelCase						:	int        =					ids_tensor([self.batch_size, self.seq_length]    ,					self.num_labels  )
									_lowerCAmelCase						:	str        =					ids_tensor([self.batch_size]    ,					self.num_choices  )

							_lowerCAmelCase						:	Dict        =					self.get_config()

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






					def    __A  (		self  ):
							return MraConfig(
							    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    ,					is_decoder=a__    ,					initializer_range=self.initializer_range    ,					)






					def    __A  (		self  ):
							_lowerCAmelCase						:	Optional[int]        =					self.get_config()
							_lowerCAmelCase						:	str        =					300
							return config






					def    __A  (		self  ):
							(
							    _lowerCAmelCase     
							)						:	int        =					self.prepare_config_and_inputs()

							_lowerCAmelCase						:	Dict        =					True
							_lowerCAmelCase						:	int        =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
							_lowerCAmelCase						:	Any        =					ids_tensor([self.batch_size, self.seq_length]    ,					vocab_size=2  )

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)






					def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
							_lowerCAmelCase						:	int        =					MraModel(config=a__  )
							model.to(a__  )
							model.eval()
							_lowerCAmelCase						:	Tuple        =					model(a__    ,					attention_mask=a__    ,					token_type_ids=a__  )
							_lowerCAmelCase						:	str        =					model(a__    ,					token_type_ids=a__  )
							_lowerCAmelCase						:	Tuple        =					model(a__  )
							self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)  )






					def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					):
							_lowerCAmelCase						:	List[Any]        =					True
							_lowerCAmelCase						:	List[str]        =					MraModel(a__  )
							model.to(a__  )
							model.eval()
							_lowerCAmelCase						:	int        =					model(
							    a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					encoder_hidden_states=a__    ,					encoder_attention_mask=a__    ,					)
							_lowerCAmelCase						:	Union[str, Any]        =					model(
							    a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					encoder_hidden_states=a__    ,					)
							_lowerCAmelCase						:	List[Any]        =					model(a__    ,					attention_mask=a__    ,					token_type_ids=a__  )
							self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)  )






					def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
							_lowerCAmelCase						:	Optional[Any]        =					MraForMaskedLM(config=a__  )
							model.to(a__  )
							model.eval()
							_lowerCAmelCase						:	Tuple        =					model(a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					labels=a__  )
							self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.vocab_size)  )






					def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
							_lowerCAmelCase						:	int        =					MraForQuestionAnswering(config=a__  )
							model.to(a__  )
							model.eval()
							_lowerCAmelCase						:	Optional[int]        =					model(
							    a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					start_positions=a__    ,					end_positions=a__    ,					)
							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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
							_lowerCAmelCase						:	List[Any]        =					self.num_labels
							_lowerCAmelCase						:	Optional[int]        =					MraForSequenceClassification(a__  )
							model.to(a__  )
							model.eval()
							_lowerCAmelCase						:	Optional[Any]        =					model(a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					labels=a__  )
							self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)  )






					def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
							_lowerCAmelCase						:	Optional[Any]        =					self.num_labels
							_lowerCAmelCase						:	Optional[int]        =					MraForTokenClassification(config=a__  )
							model.to(a__  )
							model.eval()
							_lowerCAmelCase						:	Optional[int]        =					model(a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					labels=a__  )
							self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.num_labels)  )






					def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
							_lowerCAmelCase						:	List[Any]        =					self.num_choices
							_lowerCAmelCase						:	Tuple        =					MraForMultipleChoice(config=a__  )
							model.to(a__  )
							model.eval()
							_lowerCAmelCase						:	List[Any]        =					input_ids.unsqueeze(1  ).expand(-1    ,					self.num_choices    ,					-1  ).contiguous()
							_lowerCAmelCase						:	Tuple        =					token_type_ids.unsqueeze(1  ).expand(-1    ,					self.num_choices    ,					-1  ).contiguous()
							_lowerCAmelCase						:	Optional[Any]        =					input_mask.unsqueeze(1  ).expand(-1    ,					self.num_choices    ,					-1  ).contiguous()
							_lowerCAmelCase						:	Union[str, Any]        =					model(
							    a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					labels=a__    ,					)
							self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_choices)  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Tuple        =					self.prepare_config_and_inputs()
							(
							    _lowerCAmelCase     
							)						:	Union[str, Any]        =					config_and_inputs
							_lowerCAmelCase						:	str        =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict







@require_torch
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
					_UpperCamelCase							:      int		 =		(
					    (
					        MraModel,
					        MraForMaskedLM,
					        MraForMultipleChoice,
					        MraForQuestionAnswering,
					        MraForSequenceClassification,
					        MraForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					_UpperCamelCase							:      Optional[Any]		 =		False
					_UpperCamelCase							:      Any		 =		False
					_UpperCamelCase							:      Any		 =		False
					_UpperCamelCase							:      Optional[Any]		 =		False

					_UpperCamelCase							:      Optional[Any]		 =		()






					def    __A  (		self  ):
							_lowerCAmelCase						:	List[str]        =					MraModelTester(self  )
							_lowerCAmelCase						:	List[Any]        =					ConfigTester(self    ,					config_class=a__    ,					hidden_size=37  )






					def    __A  (		self  ):
							self.config_tester.run_common_tests()






					def    __A  (		self  ):
							_lowerCAmelCase						:	Any        =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*a__  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
									_lowerCAmelCase						:	str        =					type
									self.model_tester.create_and_check_model(*a__  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Dict        =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*a__  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Dict        =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*a__  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Dict        =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*a__  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	str        =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*a__  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*a__  )






					@slow
					def    __A  (		self  ):
							for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_lowerCAmelCase						:	List[str]        =					MraModel.from_pretrained(a__  )
									self.assertIsNotNone(a__  )






					@unittest.skip(reason="""MRA does not output attentions"""  )
					def    __A  (		self  ):
							return







@require_torch
class 						__A							(			unittest.TestCase		):






					@slow
					def    __A  (		self  ):
							_lowerCAmelCase						:	Dict        =					MraModel.from_pretrained("""uw-madison/mra-base-512-4"""  )
							_lowerCAmelCase						:	str        =					torch.arange(256  ).unsqueeze(0  )

							with torch.no_grad():
									_lowerCAmelCase						:	List[Any]        =					model(a__  )[0]

							_lowerCAmelCase						:	Any        =					torch.Size((1, 256, 768)  )
							self.assertEqual(output.shape    ,					a__  )

							_lowerCAmelCase						:	str        =					torch.tensor(
							    [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]  )

							self.assertTrue(torch.allclose(output[:, :3, :3]    ,					a__    ,					atol=1e-4  )  )






					@slow
					def    __A  (		self  ):
							_lowerCAmelCase						:	Any        =					MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""  )
							_lowerCAmelCase						:	int        =					torch.arange(256  ).unsqueeze(0  )

							with torch.no_grad():
									_lowerCAmelCase						:	Optional[Any]        =					model(a__  )[0]

							_lowerCAmelCase						:	str        =					50265

							_lowerCAmelCase						:	Optional[int]        =					torch.Size((1, 256, vocab_size)  )
							self.assertEqual(output.shape    ,					a__  )

							_lowerCAmelCase						:	int        =					torch.tensor(
							    [[[9.2_5_9_5, -3.6_0_3_8, 11.8819], [9.3_8_6_9, -3.2_6_9_3, 11.0956], [11.8524, -3.4_9_3_8, 13.1210]]]  )

							self.assertTrue(torch.allclose(output[:, :3, :3]    ,					a__    ,					atol=1e-4  )  )






					@slow
					def    __A  (		self  ):
							_lowerCAmelCase						:	Dict        =					MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""  )
							_lowerCAmelCase						:	str        =					torch.arange(4096  ).unsqueeze(0  )

							with torch.no_grad():
									_lowerCAmelCase						:	Any        =					model(a__  )[0]

							_lowerCAmelCase						:	List[Any]        =					50265

							_lowerCAmelCase						:	Tuple        =					torch.Size((1, 4096, vocab_size)  )
							self.assertEqual(output.shape    ,					a__  )

							_lowerCAmelCase						:	List[str]        =					torch.tensor(
							    [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]  )

							self.assertTrue(torch.allclose(output[:, :3, :3]    ,					a__    ,					atol=1e-4  )  )





"""simple docstring"""




from ..utils import DummyObject, requires_backends
class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      int		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      str		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      str		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[str]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Any		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      str		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      str		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      str		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      str		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Dict		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Dict		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      int		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Dict		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[str]		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      int		 =		["sentencepiece"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""sentencepiece"""]  )




