





from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.mpnet.modeling_tf_mpnet import (
			    TFMPNetForMaskedLM,
			    TFMPNetForMultipleChoice,
			    TFMPNetForQuestionAnswering,
			    TFMPNetForSequenceClassification,
			    TFMPNetForTokenClassification,
			    TFMPNetModel,
			)


class 			__lowerCAmelCase				:


	'''simple docstring'''





	def __init__(	self:	str,				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	Dict=13,				lowerCamelCase_:	List[str]=7,				lowerCamelCase_:	Any=True,				lowerCamelCase_:	Union[str, Any]=True,				lowerCamelCase_:	List[Any]=False,				lowerCamelCase_:	Dict=True,				lowerCamelCase_:	Any=99,				lowerCamelCase_:	Optional[Any]=64,				lowerCamelCase_:	Optional[Any]=2,				lowerCamelCase_:	List[str]=4,				lowerCamelCase_:	Tuple=64,				lowerCamelCase_:	Optional[int]="gelu",				lowerCamelCase_:	Union[str, Any]=0.1,				lowerCamelCase_:	int=0.1,				lowerCamelCase_:	Optional[Any]=512,				lowerCamelCase_:	Optional[Any]=16,				lowerCamelCase_:	Optional[int]=2,				lowerCamelCase_:	Optional[Any]=0.0_2,				lowerCamelCase_:	str=3,				lowerCamelCase_:	Union[str, Any]=4,				lowerCamelCase_:	Optional[int]=None,				):
			lowercase__				:			Any            =		parent
			lowercase__				:			Any            =		batch_size
			lowercase__				:			List[str]            =		seq_length
			lowercase__				:			Any            =		is_training
			lowercase__				:			str            =		use_input_mask
			lowercase__				:			Any            =		use_token_type_ids
			lowercase__				:			Any            =		use_labels
			lowercase__				:			str            =		vocab_size
			lowercase__				:			Dict            =		hidden_size
			lowercase__				:			str            =		num_hidden_layers
			lowercase__				:			List[Any]            =		num_attention_heads
			lowercase__				:			Union[str, Any]            =		intermediate_size
			lowercase__				:			str            =		hidden_act
			lowercase__				:			Union[str, Any]            =		hidden_dropout_prob
			lowercase__				:			Dict            =		attention_probs_dropout_prob
			lowercase__				:			List[str]            =		max_position_embeddings
			lowercase__				:			Dict            =		type_vocab_size
			lowercase__				:			List[Any]            =		type_sequence_label_size
			lowercase__				:			Union[str, Any]            =		initializer_range
			lowercase__				:			List[str]            =		num_labels
			lowercase__				:			Any            =		num_choices
			lowercase__				:			Optional[int]            =		scope

	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			str            =		ids_tensor([self.batch_size, self.seq_length],				self.vocab_size )

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

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

			lowercase__				:			Optional[Any]            =		MPNetConfig(
			    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,				initializer_range=self.initializer_range,				)
			return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

	def       snake_case__(	self:	Any,				lowerCamelCase_:	str,				lowerCamelCase_:	Tuple,				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	str,				lowerCamelCase_:	List[str],				lowerCamelCase_:	List[str] ):
			lowercase__				:			List[Any]            =		TFMPNetModel(config=lowerCamelCase_ )
			lowercase__				:			Any            =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
			lowercase__				:			Optional[int]            =		model(lowerCamelCase_ )
			lowercase__				:			Optional[Any]            =		[input_ids, input_mask]
			lowercase__				:			int            =		model(lowerCamelCase_ )
			self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size) )

	def       snake_case__(	self:	str,				lowerCamelCase_:	List[str],				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	Tuple,				lowerCamelCase_:	Any,				lowerCamelCase_:	Dict,				lowerCamelCase_:	Dict ):
			lowercase__				:			int            =		TFMPNetForMaskedLM(config=lowerCamelCase_ )
			lowercase__				:			Tuple            =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
			lowercase__				:			List[str]            =		model(lowerCamelCase_ )
			self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size) )

	def       snake_case__(	self:	int,				lowerCamelCase_:	int,				lowerCamelCase_:	str,				lowerCamelCase_:	Tuple,				lowerCamelCase_:	List[Any],				lowerCamelCase_:	int,				lowerCamelCase_:	Optional[Any] ):
			lowercase__				:			List[Any]            =		TFMPNetForQuestionAnswering(config=lowerCamelCase_ )
			lowercase__				:			int            =		{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			}
			lowercase__				:			int            =		model(lowerCamelCase_ )
			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:	Dict,				lowerCamelCase_:	int,				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Dict,				lowerCamelCase_:	Optional[Any] ):
			lowercase__				:			Optional[Any]            =		self.num_labels
			lowercase__				:			List[str]            =		TFMPNetForSequenceClassification(lowerCamelCase_ )
			lowercase__				:			str            =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
			lowercase__				:			Dict            =		model(lowerCamelCase_ )
			self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_labels) )

	def       snake_case__(	self:	List[str],				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	int,				lowerCamelCase_:	Union[str, Any] ):
			lowercase__				:			int            =		self.num_choices
			lowercase__				:			Dict            =		TFMPNetForMultipleChoice(lowerCamelCase_ )
			lowercase__				:			str            =		tf.tile(tf.expand_dims(lowerCamelCase_,				1 ),				(1, self.num_choices, 1) )
			lowercase__				:			Union[str, Any]            =		tf.tile(tf.expand_dims(lowerCamelCase_,				1 ),				(1, self.num_choices, 1) )
			lowercase__				:			Tuple            =		{
			    '''input_ids''': multiple_choice_inputs_ids,
			    '''attention_mask''': multiple_choice_input_mask,
			}
			lowercase__				:			List[Any]            =		model(lowerCamelCase_ )
			self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_choices) )

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	Dict,				lowerCamelCase_:	List[str],				lowerCamelCase_:	int,				lowerCamelCase_:	Any,				lowerCamelCase_:	Dict,				lowerCamelCase_:	Optional[int] ):
			lowercase__				:			Dict            =		self.num_labels
			lowercase__				:			List[str]            =		TFMPNetForTokenClassification(lowerCamelCase_ )
			lowercase__				:			Union[str, Any]            =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
			lowercase__				:			List[Any]            =		model(lowerCamelCase_ )
			self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.num_labels) )





	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			List[str]            =		self.prepare_config_and_inputs()
			(lowercase__)				:			int            =		config_and_inputs
			lowercase__				:			int            =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
			return config, inputs_dict


@require_tf
class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							(
	    (
	        TFMPNetForMaskedLM,
	        TFMPNetForMultipleChoice,
	        TFMPNetForQuestionAnswering,
	        TFMPNetForSequenceClassification,
	        TFMPNetForTokenClassification,
	        TFMPNetModel,
	    )
	    if is_tf_available()
	    else ()
	)
	_A       	=							(
	    {
	        "feature-extraction": TFMPNetModel,
	        "fill-mask": TFMPNetForMaskedLM,
	        "question-answering": TFMPNetForQuestionAnswering,
	        "text-classification": TFMPNetForSequenceClassification,
	        "token-classification": TFMPNetForTokenClassification,
	        "zero-shot": TFMPNetForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	_A       	=							False
	_A       	=							False

	def       snake_case__(	self:	Dict ):
			lowercase__				:			List[str]            =		TFMPNetModelTester(self )
			lowercase__				:			Optional[Any]            =		ConfigTester(self,				config_class=lowerCamelCase_,				hidden_size=37 )

	def       snake_case__(	self:	List[Any] ):
			self.config_tester.run_common_tests()

	def       snake_case__(	self:	Optional[int] ):
			lowercase__				:			int            =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_model(*lowerCamelCase_ )

	def       snake_case__(	self:	str ):
			lowercase__				:			int            =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_masked_lm(*lowerCamelCase_ )

	def       snake_case__(	self:	Tuple ):
			lowercase__				:			Optional[Any]            =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCamelCase_ )

	def       snake_case__(	self:	Dict ):
			lowercase__				:			Optional[int]            =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCamelCase_ )

	def       snake_case__(	self:	str ):
			lowercase__				:			Optional[int]            =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCamelCase_ )

	def       snake_case__(	self:	Dict ):
			lowercase__				:			Union[str, Any]            =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCamelCase_ )





	@slow
	def       snake_case__(	self:	Any ):
			for model_name in ["microsoft/mpnet-base"]:
					lowercase__				:			List[Any]            =		TFMPNetModel.from_pretrained(lowerCamelCase_ )
					self.assertIsNotNone(lowerCamelCase_ )


@require_tf
class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	@slow
	def       snake_case__(	self:	str ):
			lowercase__				:			Any            =		TFMPNetModel.from_pretrained('microsoft/mpnet-base' )
			lowercase__				:			str            =		tf.constant([[0, 1, 2, 3, 4, 5]] )
			lowercase__				:			int            =		model(lowerCamelCase_ )[0]

			lowercase__				:			Optional[int]            =		[1, 6, 768]
			self.assertEqual(output.shape,				lowerCamelCase_ )

			lowercase__				:			List[Any]            =		tf.constant(
			    [
			        [
			            [-0.1_0_6_7_1_7_2, 0.0_8_2_1_6_4_7_3, 0.0_0_2_4_5_4_3],
			            [-0.0_3_4_6_5_8_7_9, 0.8_3_5_4_1_1_8, -0.0_3_2_5_2_2_8_8],
			            [-0.0_6_5_6_9_4_7_6, -0.1_2_4_2_4_1_1_1, -0.0_4_9_4_4_3_6],
			        ]
			    ] )
			tf.debugging.assert_near(output[:, :3, :3],				lowerCamelCase_,				atol=1E-4 )






def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int )      ->       bool:




		'''simple docstring'''
		lowercase__				:			Optional[int]            =		9

		while num > 0:
				if num % 10 != digit:
						return False
				num //= 100
				digit -= 1

		return True


def 			SCREAMING_SNAKE_CASE__     (					)      ->       int:




		'''simple docstring'''
		lowercase__				:			List[str]            =		138_902_663

		while not is_square_form(num * num ):
				if num % 10 == 3:
						num -= 6  # (3 - 6) % 10 = 7
				else:
						num -= 4  # (7 - 4) % 10 = 3

		return num * 10


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


