


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      __snake_case				:
					def __init__(						self       :						List[Any]	,      _lowerCAmelCase       :						List[Any]	,      _lowerCAmelCase       :						List[str]=1_3	,      _lowerCAmelCase       :						Dict=7	,      _lowerCAmelCase       :						Optional[Any]=True	,      _lowerCAmelCase       :						List[Any]=True	,      _lowerCAmelCase       :						Dict=False	,      _lowerCAmelCase       :						str=True	,      _lowerCAmelCase       :						Optional[int]=9_9	,      _lowerCAmelCase       :						Optional[int]=6_4	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						Optional[int]=4	,      _lowerCAmelCase       :						str=6_4	,      _lowerCAmelCase       :						List[Any]="gelu"	,      _lowerCAmelCase       :						Union[str, Any]=0.1	,      _lowerCAmelCase       :						str=0.1	,      _lowerCAmelCase       :						List[Any]=5_1_2	,      _lowerCAmelCase       :						str=1_6	,      _lowerCAmelCase       :						Union[str, Any]=2	,      _lowerCAmelCase       :						Tuple=0.02	,      _lowerCAmelCase       :						Any=3	,      _lowerCAmelCase       :						List[str]=4	,      _lowerCAmelCase       :						List[Any]=None	,      )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      parent
											__magic_name__					=      batch_size
											__magic_name__					=      seq_length
											__magic_name__					=      is_training
											__magic_name__					=      use_input_mask
											__magic_name__					=      use_token_type_ids
											__magic_name__					=      use_labels
											__magic_name__					=      vocab_size
											__magic_name__					=      hidden_size
											__magic_name__					=      num_hidden_layers
											__magic_name__					=      num_attention_heads
											__magic_name__					=      intermediate_size
											__magic_name__					=      hidden_act
											__magic_name__					=      hidden_dropout_prob
											__magic_name__					=      attention_probs_dropout_prob
											__magic_name__					=      max_position_embeddings
											__magic_name__					=      type_vocab_size
											__magic_name__					=      type_sequence_label_size
											__magic_name__					=      initializer_range
											__magic_name__					=      num_labels
											__magic_name__					=      num_choices
											__magic_name__					=      scope
					def 	a			(						self       :						Optional[int]  )							->					str:



											'''simple docstring'''





											__magic_name__					=      ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size  )

											__magic_name__					=      None
											if self.use_input_mask:
																	__magic_name__					=      random_attention_mask([self.batch_size, self.seq_length]  )

											__magic_name__					=      None
											__magic_name__					=      None
											__magic_name__					=      None
											if self.use_labels:
																	__magic_name__					=      ids_tensor([self.batch_size]	,      self.type_sequence_label_size  )
																	__magic_name__					=      ids_tensor([self.batch_size, self.seq_length]	,      self.num_labels  )
																	__magic_name__					=      ids_tensor([self.batch_size]	,      self.num_choices  )

											__magic_name__					=      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 	a			(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[int]	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						Optional[int]	,      _lowerCAmelCase       :						List[str]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						Dict  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      TFMPNetModel(config=_lowerCAmelCase  )
											__magic_name__					=      {'input_ids': input_ids, 'attention_mask': input_mask}
											__magic_name__					=      model(_lowerCAmelCase  )
											__magic_name__					=      [input_ids, input_mask]
											__magic_name__					=      model(_lowerCAmelCase  )
											self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)  )
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						Tuple  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      TFMPNetForMaskedLM(config=_lowerCAmelCase  )
											__magic_name__					=      {'input_ids': input_ids, 'attention_mask': input_mask}
											__magic_name__					=      model(_lowerCAmelCase  )
											self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)  )
					def 	a			(						self       :						List[str]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						Dict	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						Optional[int]	,      _lowerCAmelCase       :						Union[str, Any]  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      TFMPNetForQuestionAnswering(config=_lowerCAmelCase  )
											__magic_name__					=      {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											}
											__magic_name__					=      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 	a			(						self       :						Any	,      _lowerCAmelCase       :						List[Any]	,      _lowerCAmelCase       :						List[Any]	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						List[str]	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[int]  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.num_labels
											__magic_name__					=      TFMPNetForSequenceClassification(_lowerCAmelCase  )
											__magic_name__					=      {'input_ids': input_ids, 'attention_mask': input_mask}
											__magic_name__					=      model(_lowerCAmelCase  )
											self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)  )
					def 	a			(						self       :						Optional[int]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Dict	,      _lowerCAmelCase       :						Union[str, Any]  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      self.num_choices
											__magic_name__					=      TFMPNetForMultipleChoice(_lowerCAmelCase  )
											__magic_name__					=      tf.tile(tf.expand_dims(_lowerCAmelCase	,      1  )	,      (1, self.num_choices, 1)  )
											__magic_name__					=      tf.tile(tf.expand_dims(_lowerCAmelCase	,      1  )	,      (1, self.num_choices, 1)  )
											__magic_name__					=      {
											    'input_ids': multiple_choice_inputs_ids,
											    'attention_mask': multiple_choice_input_mask,
											}
											__magic_name__					=      model(_lowerCAmelCase  )
											self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_choices)  )
					def 	a			(						self       :						Optional[int]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						List[str]	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						List[Any]	,      _lowerCAmelCase       :						Any  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      self.num_labels
											__magic_name__					=      TFMPNetForTokenClassification(_lowerCAmelCase  )
											__magic_name__					=      {'input_ids': input_ids, 'attention_mask': input_mask}
											__magic_name__					=      model(_lowerCAmelCase  )
											self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)  )







					def 	a			(						self       :						int  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      self.prepare_config_and_inputs()
											((__magic_name__)		,      (__magic_name__)		,      (__magic_name__)		,      (__magic_name__)		,      (__magic_name__)		,      (__magic_name__))					=      config_and_inputs
											__magic_name__					=      {'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict







@require_tf
class      __snake_case				(   A_	,      A_	,      unittest.TestCase):
					SCREAMING_SNAKE_CASE_		:       str								=			(
					    (
					        TFMPNetForMaskedLM,
					        TFMPNetForMultipleChoice,
					        TFMPNetForQuestionAnswering,
					        TFMPNetForSequenceClassification,
					        TFMPNetForTokenClassification,
					        TFMPNetModel,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:       str								=			(
					    {
					        'feature-extraction': TFMPNetModel,
					        'fill-mask': TFMPNetForMaskedLM,
					        'question-answering': TFMPNetForQuestionAnswering,
					        'text-classification': TFMPNetForSequenceClassification,
					        'token-classification': TFMPNetForTokenClassification,
					        'zero-shot': TFMPNetForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE_		:       List[str]								=			False
					SCREAMING_SNAKE_CASE_		:       str								=			False
					def 	a			(						self       :						Any  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      TFMPNetModelTester(self  )
											__magic_name__					=      ConfigTester(self	,      config_class=_lowerCAmelCase	,      hidden_size=3_7  )
					def 	a			(						self       :						List[Any]  )							->					Any:



											'''simple docstring'''





											self.config_tester.run_common_tests()
					def 	a			(						self       :						Dict  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_model(*_lowerCAmelCase  )
					def 	a			(						self       :						int  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_masked_lm(*_lowerCAmelCase  )
					def 	a			(						self       :						Optional[Any]  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_question_answering(*_lowerCAmelCase  )
					def 	a			(						self       :						Any  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_sequence_classification(*_lowerCAmelCase  )
					def 	a			(						self       :						Any  )							->					str:



											'''simple docstring'''





											__magic_name__					=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_multiple_choice(*_lowerCAmelCase  )
					def 	a			(						self       :						Optional[int]  )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_token_classification(*_lowerCAmelCase  )







					@slow
					def 	a			(						self       :						Dict  )							->					int:



											'''simple docstring'''





											for model_name in ["microsoft/mpnet-base"]:
																	__magic_name__					=      TFMPNetModel.from_pretrained(_lowerCAmelCase  )
																	self.assertIsNotNone(_lowerCAmelCase  )







@require_tf
class      __snake_case				(   unittest.TestCase):
					@slow
					def 	a			(						self       :						Tuple  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      TFMPNetModel.from_pretrained('microsoft/mpnet-base'  )
											__magic_name__					=      tf.constant([[0, 1, 2, 3, 4, 5]]  )
											__magic_name__					=      model(_lowerCAmelCase  )[0]

											__magic_name__					=      [1, 6, 7_6_8]
											self.assertEqual(output.shape	,      _lowerCAmelCase  )

											__magic_name__					=      tf.constant(
											    [
											        [
											            [-0.106_7172, 0.0821_6473, 0.002_4543],
											            [-0.0346_5879, 0.835_4118, -0.0325_2288],
											            [-0.0656_9476, -0.1242_4111, -0.049_4436],
											        ]
											    ]  )
											tf.debugging.assert_near(output[:, :3, :3]	,      _lowerCAmelCase	,      atol=1E-4  )






def    UpperCAmelCase					(  lowercase__						:					int = 998001	):



			'''simple docstring'''





			for number in range(n - 1				,					9999				,					-1	):
						a__					=  str(lowercase__	)

						# checks whether 'str_number' is a palindrome.
						if str_number == str_number[::-1]:
									a__					=  999

									# if 'number' is a product of two 3-digit numbers
									# then number is the answer otherwise fetch next number.
									while divisor != 99:
												if (number % divisor == 0) and (len(str(number // divisor	)	) == 3.0):
															return number
												divisor -= 1
			raise ValueError("""That number is larger than our acceptable range."""	)


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

