



"""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 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=1_3 ,				lowerCamelCase__=7 ,				lowerCamelCase__=True ,				lowerCamelCase__=True ,				lowerCamelCase__=False ,				lowerCamelCase__=True ,				lowerCamelCase__=9_9 ,				lowerCamelCase__=6_4 ,				lowerCamelCase__=2 ,				lowerCamelCase__=4 ,				lowerCamelCase__=6_4 ,				lowerCamelCase__="gelu" ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=5_1_2 ,				lowerCamelCase__=1_6 ,				lowerCamelCase__=2 ,				lowerCamelCase__=0.0_2 ,				lowerCamelCase__=3 ,				lowerCamelCase__=4 ,				lowerCamelCase__=None ,				):
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = seq_length
									_lowerCamelCase					     = is_training
									_lowerCamelCase					     = use_input_mask
									_lowerCamelCase					     = use_token_type_ids
									_lowerCamelCase					     = use_labels
									_lowerCamelCase					     = vocab_size
									_lowerCamelCase					     = hidden_size
									_lowerCamelCase					     = num_hidden_layers
									_lowerCamelCase					     = num_attention_heads
									_lowerCamelCase					     = intermediate_size
									_lowerCamelCase					     = hidden_act
									_lowerCamelCase					     = hidden_dropout_prob
									_lowerCamelCase					     = attention_probs_dropout_prob
									_lowerCamelCase					     = max_position_embeddings
									_lowerCamelCase					     = type_vocab_size
									_lowerCamelCase					     = type_sequence_label_size
									_lowerCamelCase					     = initializer_range
									_lowerCamelCase					     = num_labels
									_lowerCamelCase					     = num_choices
									_lowerCamelCase					     = scope

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

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

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

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

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = TFMPNetForMaskedLM(config=lowerCamelCase__      )
									_lowerCamelCase					     = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
									_lowerCamelCase					     = model(lowerCamelCase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = TFMPNetForQuestionAnswering(config=lowerCamelCase__      )
									_lowerCamelCase					     = {
									    '''input_ids''': input_ids,
									    '''attention_mask''': input_mask,
									}
									_lowerCamelCase					     = 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 ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_labels
									_lowerCamelCase					     = TFMPNetForSequenceClassification(lowerCamelCase__      )
									_lowerCamelCase					     = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
									_lowerCamelCase					     = model(lowerCamelCase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_choices
									_lowerCamelCase					     = TFMPNetForMultipleChoice(lowerCamelCase__      )
									_lowerCamelCase					     = tf.tile(tf.expand_dims(lowerCamelCase__ ,				1      ) ,				(1, self.num_choices, 1)      )
									_lowerCamelCase					     = tf.tile(tf.expand_dims(lowerCamelCase__ ,				1      ) ,				(1, self.num_choices, 1)      )
									_lowerCamelCase					     = {
									    '''input_ids''': multiple_choice_inputs_ids,
									    '''attention_mask''': multiple_choice_input_mask,
									}
									_lowerCamelCase					     = model(lowerCamelCase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_labels
									_lowerCamelCase					     = TFMPNetForTokenClassification(lowerCamelCase__      )
									_lowerCamelCase					     = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
									_lowerCamelCase					     = model(lowerCamelCase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)      )





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


@require_tf
class 							lowerCamelCase_(      A__, A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Tuple        =       (
					    (
					        TFMPNetForMaskedLM,
					        TFMPNetForMultipleChoice,
					        TFMPNetForQuestionAnswering,
					        TFMPNetForSequenceClassification,
					        TFMPNetForTokenClassification,
					        TFMPNetModel,
					    )
					    if is_tf_available()
					    else ()
					)
					lowercase__      :				str        =       (
					    {
					        'feature-extraction': TFMPNetModel,
					        'fill-mask': TFMPNetForMaskedLM,
					        'question-answering': TFMPNetForQuestionAnswering,
					        'text-classification': TFMPNetForSequenceClassification,
					        'token-classification': TFMPNetForTokenClassification,
					        'zero-shot': TFMPNetForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					lowercase__      :				List[Any]        =       False
					lowercase__      :				Tuple        =       False

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = TFMPNetModelTester(self      )
									_lowerCamelCase					     = ConfigTester(self ,				config_class=lowerCamelCase__ ,				hidden_size=3_7      )

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

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_masked_lm(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCamelCase__      )





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


@require_tf
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



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

									_lowerCamelCase					     = [1, 6, 7_6_8]
									self.assertEqual(output.shape ,				lowerCamelCase__      )

									_lowerCamelCase					     = 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      )





"""simple docstring"""
from .stack import Stack



def 				lowerCAmelCase_(      lowercase_   :							str							)		->				bool:
				_lowerCamelCase					     = Stack()
				_lowerCamelCase					     = {'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
				for bracket in parentheses:
								if bracket in bracket_pairs:
												stack.push(lowercase_							)
								elif bracket in (")", "]", "}"):
												if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
																return False
				return stack.is_empty()


if __name__ == "__main__":
			from doctest import testmod

			testmod()

			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					['''((()))''', '''((())''', '''(()))''']
			print('''Balanced parentheses demonstration:\n''')
			for example in examples:
						__SCREAMING_SNAKE_CASE     :  Any										=					'''''' if balanced_parentheses(example) else '''not '''
						print(F"""{example} is {not_str}balanced""")
