
"""simple docstring"""




class 			lowerCAmelCase_		:


				"""simple docstring"""



				def __init__(self					)      ->	None:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: dict[str, TrieNode]     =     {}  # Mapping from char to TrieNode
											SCREAMING_SNAKE_CASE__		: List[Any]     =     False



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	None:



											"""simple docstring"""




											for word in words:
																		self.insert(SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	None:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     self
											for char in word:
																		if char not in curr.nodes:
																									SCREAMING_SNAKE_CASE__		: Optional[Any]     =     TrieNode()
																		SCREAMING_SNAKE_CASE__		: Tuple     =     curr.nodes[char]
											SCREAMING_SNAKE_CASE__		: Any     =     True



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	bool:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     self
											for char in word:
																		if char not in curr.nodes:
																									return False
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     curr.nodes[char]
											return curr.is_leaf



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	None:



											"""simple docstring"""




											def _delete(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) -> bool:
																		if index == len(SCREAMING_SNAKE_CASE__					):
																									# If word does not exist
																									if not curr.is_leaf:
																																return False
																									SCREAMING_SNAKE_CASE__		: str     =     False
																									return len(curr.nodes					) == 0
																		SCREAMING_SNAKE_CASE__		: Any     =     word[index]
																		SCREAMING_SNAKE_CASE__		: int     =     curr.nodes.get(SCREAMING_SNAKE_CASE__					)
																		# If char not in current trie node
																		if not char_node:
																									return False
																		# Flag to check if node can be deleted
																		SCREAMING_SNAKE_CASE__		: Any     =     _delete(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , index + 1					)
																		if delete_curr:
																									del curr.nodes[char]
																									return len(curr.nodes					) == 0
																		return delete_curr

											_delete(self     , SCREAMING_SNAKE_CASE__     , 0					)






def lowercase_						(     _snake_case     ,_snake_case		):
							if node.is_leaf:
														print(_snake_case     ,end=""" """		)

							for key, value in node.nodes.items():
														print_words(_snake_case     ,word + key		)

def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """banana bananas bandana band apple all beast""".split()
							SCREAMING_SNAKE_CASE__		: List[Any]     =     TrieNode()
							root.insert_many(_snake_case		)
							# print_words(root, "")
							assert all(root.find(_snake_case		) for word in words		)
							assert root.find("""banana"""		)
							assert not root.find("""bandanas"""		)
							assert not root.find("""apps"""		)
							assert root.find("""apple"""		)
							assert root.find("""all"""		)
							root.delete("""all"""		)
							assert not root.find("""all"""		)
							root.delete("""banana"""		)
							assert not root.find("""banana"""		)
							assert root.find("""bananas"""		)
							return True

def lowercase_						(     _snake_case     ,_snake_case		):
							print(str(_snake_case		)     ,"""works!""" if passes else """doesn't work :("""		)

def lowercase_						(     ):
							assert test_trie()

def lowercase_						(     ):
							print_results("""Testing trie functionality"""     ,test_trie()		)


if __name__ == "__main__":
							main()






"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
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 import (
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TFMobileBertForMaskedLM,
					    TFMobileBertForMultipleChoice,
					    TFMobileBertForNextSentencePrediction,
					    TFMobileBertForPreTraining,
					    TFMobileBertForQuestionAnswering,
					    TFMobileBertForSequenceClassification,
					    TFMobileBertForTokenClassification,
					    TFMobileBertModel,
					)



@require_tf
class 			lowerCAmelCase_		(a__				,      a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Any       =     (
				    (
				        TFMobileBertModel,
				        TFMobileBertForMaskedLM,
				        TFMobileBertForNextSentencePrediction,
				        TFMobileBertForPreTraining,
				        TFMobileBertForQuestionAnswering,
				        TFMobileBertForSequenceClassification,
				        TFMobileBertForTokenClassification,
				        TFMobileBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__UpperCamelCase   :		Tuple       =     (
				    {
				        '''feature-extraction''': TFMobileBertModel,
				        '''fill-mask''': TFMobileBertForMaskedLM,
				        '''question-answering''': TFMobileBertForQuestionAnswering,
				        '''text-classification''': TFMobileBertForSequenceClassification,
				        '''token-classification''': TFMobileBertForTokenClassification,
				        '''zero-shot''': TFMobileBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__UpperCamelCase   :		Optional[int]       =     False
				__UpperCamelCase   :		str       =     False



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=False					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     super()._prepare_for_class(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , return_labels=SCREAMING_SNAKE_CASE__					)

											if return_labels:
																		if model_class in get_values(SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: List[str]     =     tf.zeros(self.model_tester.batch_size     , dtype=tf.intaa					)

											return inputs_dict



				class 			lowerCAmelCase_		(a__						):


								"""simple docstring"""



								def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=13     , SCREAMING_SNAKE_CASE__=7     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=99     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=4     , SCREAMING_SNAKE_CASE__=37     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=16     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=4     , SCREAMING_SNAKE_CASE__=None     , )      ->	Optional[Any]:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: Tuple     =     parent
															SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     batch_size
															SCREAMING_SNAKE_CASE__		: List[Any]     =     seq_length
															SCREAMING_SNAKE_CASE__		: Any     =     is_training
															SCREAMING_SNAKE_CASE__		: List[str]     =     use_input_mask
															SCREAMING_SNAKE_CASE__		: List[Any]     =     use_token_type_ids
															SCREAMING_SNAKE_CASE__		: List[Any]     =     use_labels
															SCREAMING_SNAKE_CASE__		: Optional[int]     =     vocab_size
															SCREAMING_SNAKE_CASE__		: int     =     hidden_size
															SCREAMING_SNAKE_CASE__		: List[Any]     =     num_hidden_layers
															SCREAMING_SNAKE_CASE__		: List[str]     =     num_attention_heads
															SCREAMING_SNAKE_CASE__		: List[str]     =     intermediate_size
															SCREAMING_SNAKE_CASE__		: List[Any]     =     hidden_act
															SCREAMING_SNAKE_CASE__		: Optional[int]     =     hidden_dropout_prob
															SCREAMING_SNAKE_CASE__		: Dict     =     attention_probs_dropout_prob
															SCREAMING_SNAKE_CASE__		: Optional[Any]     =     max_position_embeddings
															SCREAMING_SNAKE_CASE__		: Dict     =     type_vocab_size
															SCREAMING_SNAKE_CASE__		: Optional[int]     =     type_sequence_label_size
															SCREAMING_SNAKE_CASE__		: str     =     initializer_range
															SCREAMING_SNAKE_CASE__		: str     =     num_labels
															SCREAMING_SNAKE_CASE__		: Dict     =     num_choices
															SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     scope
															SCREAMING_SNAKE_CASE__		: Dict     =     embedding_size



								def 			__magic_name__					(self					)      ->	List[str]:



															"""simple docstring"""




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

															SCREAMING_SNAKE_CASE__		: Optional[Any]     =     None
															if self.use_input_mask:
																						SCREAMING_SNAKE_CASE__		: Optional[Any]     =     random_attention_mask([self.batch_size, self.seq_length]					)

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

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

															SCREAMING_SNAKE_CASE__		: Optional[Any]     =     MobileBertConfig(
															    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     , embedding_size=self.embedding_size     , )

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



								def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: str     =     TFMobileBertModel(config=SCREAMING_SNAKE_CASE__					)
															SCREAMING_SNAKE_CASE__		: int     =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
															SCREAMING_SNAKE_CASE__		: List[Any]     =     model(SCREAMING_SNAKE_CASE__					)

															SCREAMING_SNAKE_CASE__		: Dict     =     [input_ids, input_mask]
															SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model(SCREAMING_SNAKE_CASE__					)

															SCREAMING_SNAKE_CASE__		: List[str]     =     model(SCREAMING_SNAKE_CASE__					)

															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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: Any     =     TFMobileBertForMaskedLM(config=SCREAMING_SNAKE_CASE__					)
															SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
															SCREAMING_SNAKE_CASE__		: List[str]     =     model(SCREAMING_SNAKE_CASE__					)
															self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)					)



								def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	str:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: List[str]     =     TFMobileBertForNextSentencePrediction(config=SCREAMING_SNAKE_CASE__					)
															SCREAMING_SNAKE_CASE__		: Any     =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
															SCREAMING_SNAKE_CASE__		: List[str]     =     model(SCREAMING_SNAKE_CASE__					)
															self.parent.assertEqual(result.logits.shape     , (self.batch_size, 2)					)



								def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     TFMobileBertForPreTraining(config=SCREAMING_SNAKE_CASE__					)
															SCREAMING_SNAKE_CASE__		: List[Any]     =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
															SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model(SCREAMING_SNAKE_CASE__					)
															self.parent.assertEqual(
															    result.prediction_logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)					)
															self.parent.assertEqual(result.seq_relationship_logits.shape     , (self.batch_size, 2)					)



								def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	int:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: List[Any]     =     self.num_labels
															SCREAMING_SNAKE_CASE__		: List[Any]     =     TFMobileBertForSequenceClassification(config=SCREAMING_SNAKE_CASE__					)
															SCREAMING_SNAKE_CASE__		: str     =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
															SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model(SCREAMING_SNAKE_CASE__					)
															self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)					)



								def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.num_choices
															SCREAMING_SNAKE_CASE__		: List[Any]     =     TFMobileBertForMultipleChoice(config=SCREAMING_SNAKE_CASE__					)
															SCREAMING_SNAKE_CASE__		: Any     =     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__     , 1					)     , (1, self.num_choices, 1)					)
															SCREAMING_SNAKE_CASE__		: Tuple     =     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__     , 1					)     , (1, self.num_choices, 1)					)
															SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__     , 1					)     , (1, self.num_choices, 1)					)
															SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {
															    """input_ids""": multiple_choice_inputs_ids,
															    """attention_mask""": multiple_choice_input_mask,
															    """token_type_ids""": multiple_choice_token_type_ids,
															}
															SCREAMING_SNAKE_CASE__		: Optional[int]     =     model(SCREAMING_SNAKE_CASE__					)
															self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_choices)					)



								def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: str     =     self.num_labels
															SCREAMING_SNAKE_CASE__		: Tuple     =     TFMobileBertForTokenClassification(config=SCREAMING_SNAKE_CASE__					)
															SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
															SCREAMING_SNAKE_CASE__		: List[str]     =     model(SCREAMING_SNAKE_CASE__					)
															self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)					)



								def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__		: Any     =     TFMobileBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE__					)
															SCREAMING_SNAKE_CASE__		: Dict     =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
															SCREAMING_SNAKE_CASE__		: str     =     model(SCREAMING_SNAKE_CASE__					)
															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 			__magic_name__					(self					)      ->	Dict:



															"""simple docstring"""




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






				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     TFMobileBertModelTest.TFMobileBertModelTester(self					)
											SCREAMING_SNAKE_CASE__		: str     =     ConfigTester(self     , config_class=SCREAMING_SNAKE_CASE__     , hidden_size=37					)



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



											"""simple docstring"""




											self.config_tester.run_common_tests()



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_model(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_masked_lm(*SCREAMING_SNAKE_CASE__					)



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_multiple_choice(*SCREAMING_SNAKE_CASE__					)



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_pretraining(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_question_answering(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_sequence_classification(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_token_classification(*SCREAMING_SNAKE_CASE__					)



				@slow
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											for model_name in ["google/mobilebert-uncased"]:
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     TFMobileBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)






@require_tf
class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				@slow
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased"""					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     tf.constant([[0, 1, 2, 3, 4, 5]]					)
											SCREAMING_SNAKE_CASE__		: Dict     =     model(SCREAMING_SNAKE_CASE__					)[0]

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [1, 6, 3_05_22]
											self.assertEqual(output.shape     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: List[Any]     =     tf.constant(
											    [
											        [
											            [-4.5919547, -9.248295, -9.645256],
											            [-6.7306175, -6.440284, -6.6052837],
											            [-7.2743506, -6.7847915, -6.024673],
											        ]
											    ]					)
											tf.debugging.assert_near(output[:, :3, :3]     , SCREAMING_SNAKE_CASE__     , atol=1E-4					)




