




"""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 __lowercase(      lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Any     		= (
					    (
					        TFMobileBertModel,
					        TFMobileBertForMaskedLM,
					        TFMobileBertForNextSentencePrediction,
					        TFMobileBertForPreTraining,
					        TFMobileBertForQuestionAnswering,
					        TFMobileBertForSequenceClassification,
					        TFMobileBertForTokenClassification,
					        TFMobileBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					__a						:						Any     		= (
					    {
					        'feature-extraction': TFMobileBertModel,
					        'fill-mask': TFMobileBertForMaskedLM,
					        'question-answering': TFMobileBertForQuestionAnswering,
					        'text-classification': TFMobileBertForSequenceClassification,
					        'token-classification': TFMobileBertForTokenClassification,
					        'zero-shot': TFMobileBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					__a						:						Optional[Any]     		= False
					__a						:						Tuple     		= False
					def     snake_case_				(    self						,	__a						,	__a						,	__a=False     ):
								__lowerCamelCase					:	int	       =				super()._prepare_for_class(__a						,	__a						,	return_labels=__a     )

								if return_labels:
											if model_class in get_values(__a     ):
														__lowerCamelCase					:	Dict	       =				tf.zeros(self.model_tester.batch_size						,	dtype=tf.intaa     )

								return inputs_dict

					class __lowercase(      lowercase__				):




										'''simple docstring'''


										def __init__(    self						,	__a						,	__a=13						,	__a=7						,	__a=True						,	__a=True						,	__a=True						,	__a=True						,	__a=99						,	__a=32						,	__a=32						,	__a=2						,	__a=4						,	__a=37						,	__a="gelu"						,	__a=0.1						,	__a=0.1						,	__a=512						,	__a=16						,	__a=2						,	__a=0.02						,	__a=3						,	__a=4						,	__a=None						,	):
													__lowerCamelCase					:	int	       =				parent
													__lowerCamelCase					:	List[str]	       =				batch_size
													__lowerCamelCase					:	str	       =				seq_length
													__lowerCamelCase					:	Optional[int]	       =				is_training
													__lowerCamelCase					:	List[str]	       =				use_input_mask
													__lowerCamelCase					:	Dict	       =				use_token_type_ids
													__lowerCamelCase					:	Union[str, Any]	       =				use_labels
													__lowerCamelCase					:	List[Any]	       =				vocab_size
													__lowerCamelCase					:	Dict	       =				hidden_size
													__lowerCamelCase					:	int	       =				num_hidden_layers
													__lowerCamelCase					:	str	       =				num_attention_heads
													__lowerCamelCase					:	str	       =				intermediate_size
													__lowerCamelCase					:	Union[str, Any]	       =				hidden_act
													__lowerCamelCase					:	str	       =				hidden_dropout_prob
													__lowerCamelCase					:	str	       =				attention_probs_dropout_prob
													__lowerCamelCase					:	Any	       =				max_position_embeddings
													__lowerCamelCase					:	Union[str, Any]	       =				type_vocab_size
													__lowerCamelCase					:	List[Any]	       =				type_sequence_label_size
													__lowerCamelCase					:	Union[str, Any]	       =				initializer_range
													__lowerCamelCase					:	List[Any]	       =				num_labels
													__lowerCamelCase					:	Any	       =				num_choices
													__lowerCamelCase					:	str	       =				scope
													__lowerCamelCase					:	Optional[Any]	       =				embedding_size
										def     snake_case_				(    self     ):
													__lowerCamelCase					:	str	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

													__lowerCamelCase					:	Tuple	       =				None
													if self.use_input_mask:
																__lowerCamelCase					:	List[str]	       =				random_attention_mask([self.batch_size, self.seq_length]     )

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

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

													__lowerCamelCase					:	Tuple	       =				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     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
													__lowerCamelCase					:	Dict	       =				TFMobileBertModel(config=__a     )
													__lowerCamelCase					:	List[Any]	       =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
													__lowerCamelCase					:	Union[str, Any]	       =				model(__a     )

													__lowerCamelCase					:	Tuple	       =				[input_ids, input_mask]
													__lowerCamelCase					:	str	       =				model(__a     )

													__lowerCamelCase					:	List[Any]	       =				model(__a     )

													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     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
													__lowerCamelCase					:	str	       =				TFMobileBertForMaskedLM(config=__a     )
													__lowerCamelCase					:	Optional[int]	       =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
													__lowerCamelCase					:	List[str]	       =				model(__a     )
													self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)     )
										def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
													__lowerCamelCase					:	List[Any]	       =				TFMobileBertForNextSentencePrediction(config=__a     )
													__lowerCamelCase					:	int	       =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
													__lowerCamelCase					:	List[Any]	       =				model(__a     )
													self.parent.assertEqual(result.logits.shape						,	(self.batch_size, 2)     )
										def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
													__lowerCamelCase					:	Union[str, Any]	       =				TFMobileBertForPreTraining(config=__a     )
													__lowerCamelCase					:	List[Any]	       =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
													__lowerCamelCase					:	int	       =				model(__a     )
													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     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
													__lowerCamelCase					:	Optional[int]	       =				self.num_labels
													__lowerCamelCase					:	List[str]	       =				TFMobileBertForSequenceClassification(config=__a     )
													__lowerCamelCase					:	Optional[int]	       =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
													__lowerCamelCase					:	Optional[Any]	       =				model(__a     )
													self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)     )
										def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
													__lowerCamelCase					:	str	       =				self.num_choices
													__lowerCamelCase					:	List[Any]	       =				TFMobileBertForMultipleChoice(config=__a     )
													__lowerCamelCase					:	Optional[Any]	       =				tf.tile(tf.expand_dims(__a						,	1     )						,	(1, self.num_choices, 1)     )
													__lowerCamelCase					:	Dict	       =				tf.tile(tf.expand_dims(__a						,	1     )						,	(1, self.num_choices, 1)     )
													__lowerCamelCase					:	int	       =				tf.tile(tf.expand_dims(__a						,	1     )						,	(1, self.num_choices, 1)     )
													__lowerCamelCase					:	Optional[int]	       =				{
													    'input_ids': multiple_choice_inputs_ids,
													    'attention_mask': multiple_choice_input_mask,
													    'token_type_ids': multiple_choice_token_type_ids,
													}
													__lowerCamelCase					:	List[str]	       =				model(__a     )
													self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)     )
										def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
													__lowerCamelCase					:	List[Any]	       =				self.num_labels
													__lowerCamelCase					:	Dict	       =				TFMobileBertForTokenClassification(config=__a     )
													__lowerCamelCase					:	Optional[int]	       =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
													__lowerCamelCase					:	str	       =				model(__a     )
													self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)     )
										def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
													__lowerCamelCase					:	List[Any]	       =				TFMobileBertForQuestionAnswering(config=__a     )
													__lowerCamelCase					:	Optional[Any]	       =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
													__lowerCamelCase					:	str	       =				model(__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     snake_case_				(    self     ):
													__lowerCamelCase					:	Dict	       =				self.prepare_config_and_inputs()
													(
													    (
													    __lowerCamelCase
													)       ,      (
													    __lowerCamelCase
													)       ,      (
													    __lowerCamelCase
													)       ,      (
													    __lowerCamelCase
													)       ,      (
													    __lowerCamelCase
													)       ,      (
													    __lowerCamelCase
													)       ,      (
													    __lowerCamelCase
													)       ,      
													)					:	int	       =				config_and_inputs
													__lowerCamelCase					:	Tuple	       =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
													return config, inputs_dict



					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				TFMobileBertModelTest.TFMobileBertModelTester(self     )
								__lowerCamelCase					:	str	       =				ConfigTester(self						,	config_class=__a						,	hidden_size=37     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*__a     )





					@slow
					def     snake_case_				(    self     ):
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
											__lowerCamelCase					:	Optional[Any]	       =				TFMobileBertModel.from_pretrained(__a     )
											self.assertIsNotNone(__a     )



@require_tf
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'     )
								__lowerCamelCase					:	Any	       =				tf.constant([[0, 1, 2, 3, 4, 5]]     )
								__lowerCamelCase					:	Union[str, Any]	       =				model(__a     )[0]

								__lowerCamelCase					:	Optional[int]	       =				[1, 6, 30522]
								self.assertEqual(output.shape						,	__a     )

								__lowerCamelCase					:	List[str]	       =				tf.constant(
								    [
								        [
								            [-4.5_919_547, -9.248_295, -9.645_256],
								            [-6.7_306_175, -6.440_284, -6.6_052_837],
								            [-7.2_743_506, -6.7_847_915, -6.024_673],
								        ]
								    ]     )
								tf.debugging.assert_near(output[:, :3, :3]						,	__a						,	atol=1E-4     )









"""simple docstring"""
import math





def 						UpperCAmelCase				(			A__:    int = 100     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				sum(i * i for i in range(1							,				n + 1     )     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(math.pow(sum(range(1							,				n + 1     )     )							,				2     )     )
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
					print(F"""{solution() = }""")



