

from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__a    :Dict      =			{
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			List[str]      		=       'ernie_m'
      _lowerCamelCase       :			Dict[str, str]      		=       {"dropout": "classifier_dropout", "num_classes": "num_labels"}





      def __init__(   self							:				List[str]						,			UpperCAmelCase							:				int = 250002						,			UpperCAmelCase							:				int = 768						,			UpperCAmelCase							:				int = 12						,			UpperCAmelCase							:				int = 12						,			UpperCAmelCase							:				int = 3072						,			UpperCAmelCase							:				str = "gelu"						,			UpperCAmelCase							:				float = 0.1						,			UpperCAmelCase							:				float = 0.1						,			UpperCAmelCase							:				int = 514						,			UpperCAmelCase							:				float = 0.02						,			UpperCAmelCase							:				int = 1						,			UpperCAmelCase							:				float = 1E-05						,			UpperCAmelCase							:				Any=None						,			UpperCAmelCase							:				Optional[int]=False						,			UpperCAmelCase							:				Any=0.0						,			**UpperCAmelCase							:				str						,			):
            super().__init__(pad_token_id=UpperCAmelCase						,			**UpperCAmelCase	)
            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_						  =				initializer_range
            A_						  =				layer_norm_eps
            A_						  =				classifier_dropout
            A_						  =				is_decoder
            A_						  =				act_dropout






__A							:			Dict         =			"Alexander Joslin"

import operator as op

from .stack import Stack





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    _A     =							Stack()
    _A     =							Stack()

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

            _A     =							operators[opr](_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
            operand_stack.push(_SCREAMING_SNAKE_CASE				)

    # RULE 5
    return operand_stack.peek()


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





