




import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		from transformers.models.bert.modeling_flax_bert import (
		    FlaxBertForMaskedLM,
		    FlaxBertForMultipleChoice,
		    FlaxBertForNextSentencePrediction,
		    FlaxBertForPreTraining,
		    FlaxBertForQuestionAnswering,
		    FlaxBertForSequenceClassification,
		    FlaxBertForTokenClassification,
		    FlaxBertModel,
		)




class 			_snake_case		(     unittest.TestCase							):




			def __init__( self    ,							a__    ,							a__=13    ,							a__=7    ,							a__=True    ,							a__=True    ,							a__=True    ,							a__=True    ,							a__=99    ,							a__=32    ,							a__=5    ,							a__=4    ,							a__=37    ,							a__="gelu"    ,							a__=0.1    ,							a__=0.1    ,							a__=512    ,							a__=16    ,							a__=2    ,							a__=0.0_2    ,							a__=4    ,							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				parent
										snake_case_      =				batch_size
										snake_case_      =				seq_length
										snake_case_      =				is_training
										snake_case_      =				use_attention_mask
										snake_case_      =				use_token_type_ids
										snake_case_      =				use_labels
										snake_case_      =				vocab_size
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				intermediate_size
										snake_case_      =				hidden_act
										snake_case_      =				hidden_dropout_prob
										snake_case_      =				attention_probs_dropout_prob
										snake_case_      =				max_position_embeddings
										snake_case_      =				type_vocab_size
										snake_case_      =				type_sequence_label_size
										snake_case_      =				initializer_range
										snake_case_      =				num_choices




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




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

										snake_case_      =				None
										if self.use_attention_mask:
																	snake_case_      =				random_attention_mask([self.batch_size, self.seq_length]							)

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

										snake_case_      =				BertConfig(
										    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    ,							is_decoder=a__    ,							initializer_range=self.initializer_range    ,							)

										return config, input_ids, token_type_ids, attention_mask




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				self.prepare_config_and_inputs()
										snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				config_and_inputs
										snake_case_      =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				self.prepare_config_and_inputs()
										snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				config_and_inputs

										snake_case_      =				True
										snake_case_      =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
										snake_case_      =				ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2							)

										return (
										    config,
										    input_ids,
										    attention_mask,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)




@require_flax
class 			_snake_case		(     lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_					:				Union[str, Any]							=   True

			lowerCAmelCase_					:				int							=   (
			    (
			        FlaxBertModel,
			        FlaxBertForPreTraining,
			        FlaxBertForMaskedLM,
			        FlaxBertForMultipleChoice,
			        FlaxBertForQuestionAnswering,
			        FlaxBertForNextSentencePrediction,
			        FlaxBertForSequenceClassification,
			        FlaxBertForTokenClassification,
			        FlaxBertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				FlaxBertModelTester(self							)




			@slow
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				FlaxBertModel.from_pretrained("bert-base-cased"							)
										snake_case_      =				model(np.ones((1, 1)							)							)
										self.assertIsNotNone(a__							)










'''simple docstring'''





_SCREAMING_SNAKE_CASE							:       Optional[int]						=			"Alexander Joslin"

import operator as op

from .stack import Stack


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






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

							snake_case_      =				Stack()
							snake_case_      =				Stack()

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

																					snake_case_      =				operators[opr](snake_case							,    snake_case					)
																					operand_stack.push(snake_case					)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[Any]						=			"(5 + ((4 * 2) * (2 + 3)))"
		# answer = 45
		print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")




