





"""simple docstring"""






import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MPNetForMaskedLM,
							    MPNetForMultipleChoice,
							    MPNetForQuestionAnswering,
							    MPNetForSequenceClassification,
							    MPNetForTokenClassification,
							    MPNetModel,
							)




class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=7		,     _UpperCAmelCase=True		,     _UpperCAmelCase=True		,     _UpperCAmelCase=False		,     _UpperCAmelCase=True		,     _UpperCAmelCase=99		,     _UpperCAmelCase=64		,     _UpperCAmelCase=5		,     _UpperCAmelCase=4		,     _UpperCAmelCase=64		,     _UpperCAmelCase="gelu"		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=512		,     _UpperCAmelCase=16		,     _UpperCAmelCase=2		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=3		,     _UpperCAmelCase=4		,     _UpperCAmelCase=None		,     ):
									__a	:  Union[str, Any]         =					parent
									__a	:  List[str]         =					batch_size
									__a	:  List[str]         =					seq_length
									__a	:  Union[str, Any]         =					is_training
									__a	:  Optional[int]         =					use_input_mask
									__a	:  Union[str, Any]         =					use_token_type_ids
									__a	:  List[Any]         =					use_labels
									__a	:  str         =					vocab_size
									__a	:  Dict         =					hidden_size
									__a	:  List[str]         =					num_hidden_layers
									__a	:  List[str]         =					num_attention_heads
									__a	:  Tuple         =					intermediate_size
									__a	:  str         =					hidden_act
									__a	:  List[Any]         =					hidden_dropout_prob
									__a	:  Union[str, Any]         =					attention_probs_dropout_prob
									__a	:  Dict         =					max_position_embeddings
									__a	:  Dict         =					type_vocab_size
									__a	:  Dict         =					type_sequence_label_size
									__a	:  List[str]         =					initializer_range
									__a	:  int         =					num_labels
									__a	:  Optional[int]         =					num_choices
									__a	:  Optional[int]         =					scope







					def 	_lowerCamelCase						(  self       ):
									return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

									__a	:  Dict         =					None
									if self.use_input_mask:
													__a	:  str         =					random_attention_mask([self.batch_size, self.seq_length]       )

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

									__a	:  Dict         =					self.get_config()
									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







					def 	_lowerCamelCase						(  self       ):
									return 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		,     )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[Any]         =					MPNetModel(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  int         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )
									__a	:  Any         =					model(_UpperCAmelCase       )
									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 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Union[str, Any]         =					MPNetForQuestionAnswering(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  Tuple         =					model(
									    _UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     start_positions=_UpperCAmelCase		,     end_positions=_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 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[Any]         =					self.num_labels
									__a	:  Optional[int]         =					MPNetForSequenceClassification(_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  List[str]         =					model(_UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     labels=_UpperCAmelCase       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Tuple         =					self.num_choices
									__a	:  Union[str, Any]         =					MPNetForMultipleChoice(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  Dict         =					input_ids.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
									__a	:  int         =					input_mask.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
									__a	:  Optional[Any]         =					model(
									    _UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     labels=_UpperCAmelCase		,     )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Tuple         =					self.num_labels
									__a	:  str         =					MPNetForTokenClassification(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  Optional[Any]         =					model(_UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     labels=_UpperCAmelCase       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					self.prepare_config_and_inputs()
									(__a)	:  Dict         =					config_and_inputs
									__a	:  List[Any]         =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict






@require_torch
class 			__lowercase     (     _UpperCamelCase				, _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    (
					        MPNetForMaskedLM,
					        MPNetForMultipleChoice,
					        MPNetForQuestionAnswering,
					        MPNetForSequenceClassification,
					        MPNetForTokenClassification,
					        MPNetModel,
					    )
					    if is_torch_available()
					    else ()
					)
					__lowerCAmelCase					=					(
					    {
					        '''feature-extraction''': MPNetModel,
					        '''fill-mask''': MPNetForMaskedLM,
					        '''question-answering''': MPNetForQuestionAnswering,
					        '''text-classification''': MPNetForSequenceClassification,
					        '''token-classification''': MPNetForTokenClassification,
					        '''zero-shot''': MPNetForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__lowerCAmelCase					=					False
					__lowerCAmelCase					=					True







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					MPNetModelTester(self       )
									__a	:  List[str]         =					ConfigTester(self		,     config_class=_UpperCAmelCase		,     hidden_size=37       )







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*_UpperCAmelCase       )






@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					MPNetModel.from_pretrained('''microsoft/mpnet-base'''       )
									__a	:  List[Any]         =					torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
									__a	:  List[Any]         =					model(_UpperCAmelCase       )[0]
									__a	:  str         =					torch.Size((1, 11, 768)       )
									self.assertEqual(output.shape		,     _UpperCAmelCase       )
									__a	:  Tuple         =					torch.tensor(
									    [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]       )
									# compare the actual values for a slice.
									self.assertTrue(torch.allclose(output[:, :3, :3]		,     _UpperCAmelCase		,     atol=1e-4       )       )






"""simple docstring"""






def       __A (     a_  :int)						->						Union[str, Any]:
				__a	:  int         =					[]
				__a	:  Dict         =					[]
				__a	:  str         =					{
				    '''^''': 3,
				    '''*''': 2,
				    '''/''': 2,
				    '''%''': 2,
				    '''+''': 1,
				    '''-''': 1,
				}  # Priority of each operator
				__a	:  Tuple         =					len(a_) if (len(a_) > 7) else 7

				# Print table header for output
				print(
				    '''Symbol'''.center(8)				,    '''Stack'''.center(a_)				,    '''Postfix'''.center(a_)				,    sep=''' | '''				,    )
				print('''-''' * (print_width * 3 + 7))

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(a_)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(a_)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(a_) == 0:
																stack.append(a_)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(a_) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop())  # pop stack & add to Postfix
																stack.append(a_)  # push x to stack

								print(
								    x.center(8)				,    (''''''.join(a_)).ljust(a_)				,    (''''''.join(a_)).ljust(a_)				,    sep=''' | '''				,    )  # Output in tabular format

				while len(a_) > 0:  # while stack is not empty
								post_fix.append(stack.pop())  # pop stack & add to Postfix
								print(
								    ''' '''.center(8)				,    (''''''.join(a_)).ljust(a_)				,    (''''''.join(a_)).ljust(a_)				,    sep=''' | '''				,    )  # Output in tabular format

				return "".join(a_)  # return Postfix as str

def       __A (     a_  :int)						->						List[Any]:
				__a	:  Dict         =					list(infix[::-1])  # reverse the infix equation

				for i in range(len(a_)):
								if infix[i] == "(":
												__a	:  Union[str, Any]         =					''')'''  # change "(" to ")"
								elif infix[i] == ")":
												__a	:  List[str]         =					'''('''  # change ")" to "("

				return (infix_2_postfix(''''''.join(a_)))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		A	     =					input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
		A	     =					''''''.join(Infix.split())  # Remove spaces from the input
		print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')