





'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:  List[Any]		 =						logging.get_logger(__name__)

A_		:  Union[str, Any]		 =						{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''swinv2'''

   lowerCamelCase__    							=      {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }

   def __init__(							self	,		__SCREAMING_SNAKE_CASE=2_2_4	,		__SCREAMING_SNAKE_CASE=4	,		__SCREAMING_SNAKE_CASE=3	,		__SCREAMING_SNAKE_CASE=9_6	,		__SCREAMING_SNAKE_CASE=[2, 2, 6, 2]	,		__SCREAMING_SNAKE_CASE=[3, 6, 1_2, 2_4]	,		__SCREAMING_SNAKE_CASE=7	,		__SCREAMING_SNAKE_CASE=4.0	,		__SCREAMING_SNAKE_CASE=True	,		__SCREAMING_SNAKE_CASE=0.0	,		__SCREAMING_SNAKE_CASE=0.0	,		__SCREAMING_SNAKE_CASE=0.1	,		__SCREAMING_SNAKE_CASE="gelu"	,		__SCREAMING_SNAKE_CASE=False	,		__SCREAMING_SNAKE_CASE=0.02	,		__SCREAMING_SNAKE_CASE=1e-5	,		__SCREAMING_SNAKE_CASE=3_2	,		**__SCREAMING_SNAKE_CASE	,		):
    super().__init__(**__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[Any]    =  image_size
    snake_case__     :					Any    =  patch_size
    snake_case__     :					str    =  num_channels
    snake_case__     :					Tuple    =  embed_dim
    snake_case__     :					Optional[int]    =  depths
    snake_case__     :					List[Any]    =  len(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  num_heads
    snake_case__     :					int    =  window_size
    snake_case__     :					List[Any]    =  mlp_ratio
    snake_case__     :					Dict    =  qkv_bias
    snake_case__     :					Any    =  hidden_dropout_prob
    snake_case__     :					List[Any]    =  attention_probs_dropout_prob
    snake_case__     :					Any    =  drop_path_rate
    snake_case__     :					str    =  hidden_act
    snake_case__     :					Dict    =  use_absolute_embeddings
    snake_case__     :					Any    =  layer_norm_eps
    snake_case__     :					Dict    =  initializer_range
    snake_case__     :					Optional[int]    =  encoder_stride
    # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    snake_case__     :					Any    =  int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE       ) - 1)       )
    snake_case__     :					Tuple    =  (0, 0, 0, 0)







__lowercase				      =						"""Alexander Joslin"""

import operator as op

from .stack import Stack







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''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__":
      __lowercase				      =						"""(5 + ((4 * 2) * (2 + 3)))"""
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





