
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	(      SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Dict       =(
							    (
							        TFMobileBertModel,
							        TFMobileBertForMaskedLM,
							        TFMobileBertForNextSentencePrediction,
							        TFMobileBertForPreTraining,
							        TFMobileBertForQuestionAnswering,
							        TFMobileBertForSequenceClassification,
							        TFMobileBertForTokenClassification,
							        TFMobileBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							lowercase_ : Dict       =(
							    {
							        '''feature-extraction''': TFMobileBertModel,
							        '''fill-mask''': TFMobileBertForMaskedLM,
							        '''question-answering''': TFMobileBertForQuestionAnswering,
							        '''text-classification''': TFMobileBertForSequenceClassification,
							        '''token-classification''': TFMobileBertForTokenClassification,
							        '''zero-shot''': TFMobileBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase_ : Tuple       =False
							lowercase_ : Any       =False




							def 				A__   (       self     ,A__     ,A__     ,A__=False):
												lowercase					=		super()._prepare_for_class(A__     ,A__     ,return_labels=A__)

												if return_labels:
																	if model_class in get_values(A__):
																						lowercase					=		tf.zeros(self.model_tester.batch_size     ,dtype=tf.intaa)

												return inputs_dict







							class        lowercase	(      SCREAMING_SNAKE_CASE__	):




														def __init__(       self     ,A__     ,A__=1_3     ,A__=7     ,A__=True     ,A__=True     ,A__=True     ,A__=True     ,A__=9_9     ,A__=3_2     ,A__=3_2     ,A__=2     ,A__=4     ,A__=3_7     ,A__="gelu"     ,A__=0.1     ,A__=0.1     ,A__=5_1_2     ,A__=1_6     ,A__=2     ,A__=0.02     ,A__=3     ,A__=4     ,A__=None     ,):
																			lowercase					=		parent
																			lowercase					=		batch_size
																			lowercase					=		seq_length
																			lowercase					=		is_training
																			lowercase					=		use_input_mask
																			lowercase					=		use_token_type_ids
																			lowercase					=		use_labels
																			lowercase					=		vocab_size
																			lowercase					=		hidden_size
																			lowercase					=		num_hidden_layers
																			lowercase					=		num_attention_heads
																			lowercase					=		intermediate_size
																			lowercase					=		hidden_act
																			lowercase					=		hidden_dropout_prob
																			lowercase					=		attention_probs_dropout_prob
																			lowercase					=		max_position_embeddings
																			lowercase					=		type_vocab_size
																			lowercase					=		type_sequence_label_size
																			lowercase					=		initializer_range
																			lowercase					=		num_labels
																			lowercase					=		num_choices
																			lowercase					=		scope
																			lowercase					=		embedding_size




														def 				A__   (       self):
																			lowercase					=		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

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

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

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

																			lowercase					=		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 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
																			lowercase					=		TFMobileBertModel(config=A__)
																			lowercase					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																			lowercase					=		model(A__)

																			lowercase					=		[input_ids, input_mask]
																			lowercase					=		model(A__)

																			lowercase					=		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 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
																			lowercase					=		TFMobileBertForMaskedLM(config=A__)
																			lowercase					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																			lowercase					=		model(A__)
																			self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size))




														def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
																			lowercase					=		TFMobileBertForNextSentencePrediction(config=A__)
																			lowercase					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																			lowercase					=		model(A__)
																			self.parent.assertEqual(result.logits.shape     ,(self.batch_size, 2))




														def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
																			lowercase					=		TFMobileBertForPreTraining(config=A__)
																			lowercase					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																			lowercase					=		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 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
																			lowercase					=		self.num_labels
																			lowercase					=		TFMobileBertForSequenceClassification(config=A__)
																			lowercase					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																			lowercase					=		model(A__)
																			self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))




														def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
																			lowercase					=		self.num_choices
																			lowercase					=		TFMobileBertForMultipleChoice(config=A__)
																			lowercase					=		tf.tile(tf.expand_dims(A__     ,1)     ,(1, self.num_choices, 1))
																			lowercase					=		tf.tile(tf.expand_dims(A__     ,1)     ,(1, self.num_choices, 1))
																			lowercase					=		tf.tile(tf.expand_dims(A__     ,1)     ,(1, self.num_choices, 1))
																			lowercase					=		{
																			    '''input_ids''': multiple_choice_inputs_ids,
																			    '''attention_mask''': multiple_choice_input_mask,
																			    '''token_type_ids''': multiple_choice_token_type_ids,
																			}
																			lowercase					=		model(A__)
																			self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_choices))




														def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
																			lowercase					=		self.num_labels
																			lowercase					=		TFMobileBertForTokenClassification(config=A__)
																			lowercase					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																			lowercase					=		model(A__)
																			self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels))




														def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
																			lowercase					=		TFMobileBertForQuestionAnswering(config=A__)
																			lowercase					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																			lowercase					=		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 				A__   (       self):
																			lowercase					=		self.prepare_config_and_inputs()
																			(
																			    (
																			    lowercase		
																			)			,		(
																			    lowercase		
																			)			,		(
																			    lowercase		
																			)			,		(
																			    lowercase		
																			)			,		(
																			    lowercase		
																			)			,		(
																			    lowercase		
																			)			,		(
																			    lowercase		
																			)			,				
																			)					=		config_and_inputs
																			lowercase					=		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
																			return config, inputs_dict




							def 				A__   (       self):
												lowercase					=		TFMobileBertModelTest.TFMobileBertModelTester(self)
												lowercase					=		ConfigTester(self     ,config_class=A__     ,hidden_size=3_7)




							def 				A__   (       self):
												self.config_tester.run_common_tests()




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_model(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_masked_lm(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_multiple_choice(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_pretraining(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_question_answering(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_sequence_classification(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_token_classification(*A__)





							@slow
							def 				A__   (       self):
												# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												for model_name in ["google/mobilebert-uncased"]:
																	lowercase					=		TFMobileBertModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)







@require_tf
class        lowercase	(      unittest.TestCase	):




							@slow
							def 				A__   (       self):
												lowercase					=		TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased''')
												lowercase					=		tf.constant([[0, 1, 2, 3, 4, 5]])
												lowercase					=		model(A__)[0]

												lowercase					=		[1, 6, 3_0_5_2_2]
												self.assertEqual(output.shape     ,A__)

												lowercase					=		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]     ,A__     ,atol=1E-4)






from __future__ import annotations

from random import random







class        lowercase	:




							def __init__(       self     ,A__ = None):
												lowercase					=		value
												lowercase					=		random()
												lowercase					=		None
												lowercase					=		None




							def __repr__(       self):
												from pprint import pformat

												if self.left is None and self.right is None:
																	return f'\'{self.value}: {self.prior:.5}\''
												else:
																	return pformat(
																	    {f'{self.value}: {self.prior:.5}': (self.left, self.right)}     ,indent=1)





							def __str__(       self):
												lowercase					=		str(self.value) + ''' '''
												lowercase					=		str(self.left or '''''')
												lowercase					=		str(self.right or '''''')
												return value + left + right



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					if root is None:  # None tree is split into 2 Nones
										return None, None
					elif root.value is None:
										return None, None
					else:
										if value < root.value:
															lowercase			,		lowercase					=		split(root.left				,    lowerCAmelCase__	)
															return left, root
										else:
															lowercase			,		lowercase					=		split(root.right				,    lowerCAmelCase__	)
															return root, right



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					if (not left) or (not right):  # If one node is None, return the other
										return left or right
					elif left.prior < right.prior:
										lowercase					=		merge(left.right				,    lowerCAmelCase__	)
										return left
					else:
										lowercase					=		merge(lowerCAmelCase__				,    right.left	)
										return right



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		Node(lowerCAmelCase__	)
					lowercase			,		lowercase					=		split(lowerCAmelCase__				,    lowerCAmelCase__	)
					return merge(merge(lowerCAmelCase__				,    lowerCAmelCase__	)				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		split(lowerCAmelCase__				,    value - 1	)
					lowercase			,		lowercase					=		split(lowerCAmelCase__				,    lowerCAmelCase__	)
					return merge(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if not root:  # None
										return
					else:
										inorder(root.left	)
										print(root.value				,    end=''','''	)
										inorder(root.right	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					for arg in args.split():
										if arg[0] == "+":
															lowercase					=		insert(lowerCAmelCase__				,    int(arg[1:]	)	)

										elif arg[0] == "-":
															lowercase					=		erase(lowerCAmelCase__				,    int(arg[1:]	)	)

										else:
															print('''Unknown command'''	)

					return root



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		None
					print(
					    '''enter numbers to create a tree, + value to add value into treap, '''
					    '''- value to erase all nodes with value. \'q\' to quit. '''	)

					lowercase					=		input()
					while args != "q":
										lowercase					=		interact_treap(lowerCAmelCase__				,    lowerCAmelCase__	)
										print(lowerCAmelCase__	)
										lowercase					=		input()

					print('''good by!'''	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




