"""simple docstring"""




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        __SCREAMING_SNAKE_CASE     :







				def __init__(    self    :Any      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Union[str, Any]=13      ,__UpperCAmelCase    :str=7      ,__UpperCAmelCase    :Any=True      ,__UpperCAmelCase    :int=True      ,__UpperCAmelCase    :Dict=False      ,__UpperCAmelCase    :List[Any]=True      ,__UpperCAmelCase    :Union[str, Any]=99      ,__UpperCAmelCase    :str=64      ,__UpperCAmelCase    :int=2      ,__UpperCAmelCase    :Union[str, Any]=4      ,__UpperCAmelCase    :List[Any]=64      ,__UpperCAmelCase    :Tuple="gelu"      ,__UpperCAmelCase    :List[str]=0.1      ,__UpperCAmelCase    :Tuple=0.1      ,__UpperCAmelCase    :Optional[int]=5_12      ,__UpperCAmelCase    :Any=16      ,__UpperCAmelCase    :str=2      ,__UpperCAmelCase    :Dict=0.02      ,__UpperCAmelCase    :Tuple=3      ,__UpperCAmelCase    :Union[str, Any]=4      ,__UpperCAmelCase    :Any=None      ,)      ->					Dict:




									"""simple docstring"""



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







				def   lowercase_		(    self    :List[Any]  )      ->					Optional[int]:




									"""simple docstring"""



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

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

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

									lowerCamelCase__		:   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   lowercase_		(    self    :str      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :int  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					TFMPNetModel(config=__UpperCAmelCase  )
									lowerCamelCase__		:   Any  				=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									lowerCamelCase__		:   List[str]  				=					model(__UpperCAmelCase  )
									lowerCamelCase__		:   List[str]  				=					[input_ids, input_mask]
									lowerCamelCase__		:   List[str]  				=					model(__UpperCAmelCase  )
									self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)  )







				def   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Any  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					TFMPNetForMaskedLM(config=__UpperCAmelCase  )
									lowerCamelCase__		:   str  				=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									lowerCamelCase__		:   List[Any]  				=					model(__UpperCAmelCase  )
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)  )







				def   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Optional[int]  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Union[str, Any]  				=					TFMPNetForQuestionAnswering(config=__UpperCAmelCase  )
									lowerCamelCase__		:   Tuple  				=					{
									    '''input_ids''': input_ids,
									    '''attention_mask''': input_mask,
									}
									lowerCamelCase__		:   Optional[int]  				=					model(__UpperCAmelCase  )
									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   lowercase_		(    self    :Union[str, Any]      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Optional[Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					self.num_labels
									lowerCamelCase__		:   int  				=					TFMPNetForSequenceClassification(__UpperCAmelCase  )
									lowerCamelCase__		:   List[Any]  				=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									lowerCamelCase__		:   Dict  				=					model(__UpperCAmelCase  )
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)  )







				def   lowercase_		(    self    :List[Any]      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Optional[Any]  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					self.num_choices
									lowerCamelCase__		:   Dict  				=					TFMPNetForMultipleChoice(__UpperCAmelCase  )
									lowerCamelCase__		:   int  				=					tf.tile(tf.expand_dims(__UpperCAmelCase      ,1  )      ,(1, self.num_choices, 1)  )
									lowerCamelCase__		:   Optional[Any]  				=					tf.tile(tf.expand_dims(__UpperCAmelCase      ,1  )      ,(1, self.num_choices, 1)  )
									lowerCamelCase__		:   int  				=					{
									    '''input_ids''': multiple_choice_inputs_ids,
									    '''attention_mask''': multiple_choice_input_mask,
									}
									lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase  )
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)  )







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Dict  )      ->					List[str]:




									"""simple docstring"""



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







				def   lowercase_		(    self    :Optional[int]  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					self.prepare_config_and_inputs()
									((lowerCamelCase__)				,						(lowerCamelCase__)				,						(lowerCamelCase__)				,						(lowerCamelCase__)				,						(lowerCamelCase__)				,						(lowerCamelCase__))		:   Dict  				=					config_and_inputs
									lowerCamelCase__		:   List[str]  				=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict





@require_tf
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      (
				    (
				        TFMPNetForMaskedLM,
				        TFMPNetForMultipleChoice,
				        TFMPNetForQuestionAnswering,
				        TFMPNetForSequenceClassification,
				        TFMPNetForTokenClassification,
				        TFMPNetModel,
				    )
				    if is_tf_available()
				    else ()
				)
				UpperCAmelCase	=      (
				    {
				        '''feature-extraction''': TFMPNetModel,
				        '''fill-mask''': TFMPNetForMaskedLM,
				        '''question-answering''': TFMPNetForQuestionAnswering,
				        '''text-classification''': TFMPNetForSequenceClassification,
				        '''token-classification''': TFMPNetForTokenClassification,
				        '''zero-shot''': TFMPNetForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCAmelCase	=      False
				UpperCAmelCase	=      False







				def   lowercase_		(    self    :int  )      ->					Tuple:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					TFMPNetModelTester(self  )
									lowerCamelCase__		:   Dict  				=					ConfigTester(self      ,config_class=__UpperCAmelCase      ,hidden_size=37  )







				def   lowercase_		(    self    :int  )      ->					List[Any]:




									"""simple docstring"""



									self.config_tester.run_common_tests()







				def   lowercase_		(    self    :Optional[Any]  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*__UpperCAmelCase  )







				def   lowercase_		(    self    :List[str]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_masked_lm(*__UpperCAmelCase  )







				def   lowercase_		(    self    :Tuple  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*__UpperCAmelCase  )







				def   lowercase_		(    self    :Tuple  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*__UpperCAmelCase  )







				def   lowercase_		(    self    :List[Any]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*__UpperCAmelCase  )







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




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*__UpperCAmelCase  )







				@slow
				def   lowercase_		(    self    :int  )      ->					Tuple:




									"""simple docstring"""



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





@require_tf
class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''  )
									lowerCamelCase__		:   List[str]  				=					tf.constant([[0, 1, 2, 3, 4, 5]]  )
									lowerCamelCase__		:   Optional[Any]  				=					model(__UpperCAmelCase  )[0]

									lowerCamelCase__		:   Union[str, Any]  				=					[1, 6, 7_68]
									self.assertEqual(output.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Dict  				=					tf.constant(
									    [
									        [
									            [-0.1_067_172, 0.08_216_473, 0.0_024_543],
									            [-0.03_465_879, 0.8_354_118, -0.03_252_288],
									            [-0.06_569_476, -0.12_424_111, -0.0_494_436],
									        ]
									    ]  )
									tf.debugging.assert_near(output[:, :3, :3]      ,__UpperCAmelCase      ,atol=1E-4  )







"""simple docstring"""




def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					if not root or root not in binary_tree_mirror_dictionary:
										return
					lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					binary_tree_mirror_dictionary[root][:2]
					lowerCamelCase__		:   Optional[Any]  				=					[right_child, left_child]
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)



def 				__a       (			_lowercase							, _lowercase = 1						):







					"""simple docstring"""





					if not binary_tree:
										raise ValueError('''binary tree cannot be empty'''						)
					if root not in binary_tree:
										lowerCamelCase__		:   Dict  				=					f"""root {root} is not present in the binary_tree"""
										raise ValueError(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					dict(_lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					return binary_tree_mirror_dictionary


if __name__ == "__main__":
					UpperCAmelCase       :    Any									=			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
					print(f'''Binary tree: {binary_tree}''')
					UpperCAmelCase       :    List[Any]									=			binary_tree_mirror(binary_tree, 5)
					print(f'''Binary tree mirror: {binary_tree_mirror_dictionary}''')






