





__lowercase	: List[Any]       ="""Alexander Joslin"""

import operator as op

from .stack import Stack




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           ={"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

   UpperCAmelCase_           =Stack()
   UpperCAmelCase_           =Stack()

   for i in equation:
      if i.isdigit():
         # RULE 1
         operand_stack.push(int(lowercase__	)	)
      elif i in operators:
         # RULE 2
         operator_stack.push(lowercase__	)
      elif i == ")":
         # RULE 4
         UpperCAmelCase_           =operator_stack.peek()
         operator_stack.pop()
         UpperCAmelCase_           =operand_stack.peek()
         operand_stack.pop()
         UpperCAmelCase_           =operand_stack.peek()
         operand_stack.pop()

         UpperCAmelCase_           =operators[opr](lowercase__	,     lowercase__	)
         operand_stack.push(lowercase__	)

    # RULE 5
   return operand_stack.peek()


if __name__ == "__main__":
     __lowercase	: str       ="""(5 + ((4 * 2) * (2 + 3)))"""
     # answer = 45
     print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")







from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 import (
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
      )

class    _lowercase				:

   def __init__(				self	:    str			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]=1_3			,						lowerCamelCase__	:    Dict=7			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Tuple=True			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Any=9_9			,						lowerCamelCase__	:    Union[str, Any]=3_2			,						lowerCamelCase__	:    Dict=2			,						lowerCamelCase__	:    Tuple=4			,						lowerCamelCase__	:    int=3_7			,						lowerCamelCase__	:    List[str]="gelu"			,						lowerCamelCase__	:    str=0.1			,						lowerCamelCase__	:    Any=0.1			,						lowerCamelCase__	:    int=5_1_2			,						lowerCamelCase__	:    Optional[Any]=1_6			,						lowerCamelCase__	:    Optional[Any]=2			,						lowerCamelCase__	:    List[str]=0.02			,						lowerCamelCase__	:    Dict=False			,						lowerCamelCase__	:    Union[str, Any]=True			,						lowerCamelCase__	:    List[Any]="None"			,						lowerCamelCase__	:    int=3			,						lowerCamelCase__	:    List[Any]=4			,						lowerCamelCase__	:    List[str]=None			,						) ->			List[str]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		seq_length
         A_							       =		is_training
         A_							       =		use_input_mask
         A_							       =		use_token_type_ids
         A_							       =		use_labels
         A_							       =		vocab_size
         A_							       =		hidden_size
         A_							       =		num_hidden_layers
         A_							       =		num_attention_heads
         A_							       =		intermediate_size
         A_							       =		hidden_act
         A_							       =		hidden_dropout_prob
         A_							       =		attention_probs_dropout_prob
         A_							       =		max_position_embeddings
         A_							       =		type_vocab_size
         A_							       =		type_sequence_label_size
         A_							       =		initializer_range
         A_							       =		num_labels
         A_							       =		num_choices
         A_							       =		relative_attention
         A_							       =		position_biased_input
         A_							       =		pos_att_type
         A_							       =		scope

   def     UpperCamelCase					(				self	:    str       ) ->			str:
         """simple docstring"""





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

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

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

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

         A_							       =		DebertaVaConfig(
             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			,						relative_attention=self.relative_attention			,						position_biased_input=self.position_biased_input			,						initializer_range=self.initializer_range			,						return_dict=lowerCamelCase__			,						)

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaModel(config=lowerCamelCase__       )
         A_							       =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         A_							       =		[input_ids, input_mask]
         A_							       =		model(lowerCamelCase__       )

         A_							       =		model(lowerCamelCase__       )

         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)       )

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[str]       ) ->			str:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		TFDebertaVaForSequenceClassification(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    str       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		TFDebertaVaForTokenClassification(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)       )

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaForQuestionAnswering(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_							       =		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     UpperCamelCase					(				self	:    int       ) ->			Optional[int]:
         """simple docstring"""





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



@require_tf
class    _lowercase				(					__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Union[str, Any]  					=    (
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   _lowercase     :       Optional[Any]  					=    (
       {
           'feature-extraction': TFDebertaVaModel,
           'fill-mask': TFDebertaVaForMaskedLM,
           'question-answering': TFDebertaVaForQuestionAnswering,
           'text-classification': TFDebertaVaForSequenceClassification,
           'token-classification': TFDebertaVaForTokenClassification,
           'zero-shot': TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   _lowercase     :       List[str]  					=    False
   _lowercase     :       Any  					=    False

   def     UpperCamelCase					(				self	:    int       ) ->			List[str]:
         """simple docstring"""





         A_							       =		TFDebertaVaModelTester(self       )
         A_							       =		ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=3_7       )

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         self.config_tester.run_common_tests()

   def     UpperCamelCase					(				self	:    Any       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )





   @slow
   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         A_							       =		TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''       )
         self.assertIsNotNone(lowerCamelCase__       )



@require_tf
class    _lowercase				(					unittest.TestCase			):

   @unittest.skip(reason='''Model not available yet'''       )
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         pass





   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''       )
         A_							       =		tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       )
         A_							       =		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
         A_							       =		model(lowerCamelCase__			,						attention_mask=lowerCamelCase__       )[0]

         A_							       =		tf.constant(
             [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]       )
         tf.debugging.assert_near(output[:, 1:4, 1:4]			,						lowerCamelCase__			,						atol=1e-4       )





