






from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE__      :			str							=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''informer'''
				a__					       =  {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''num_hidden_layers''': '''encoder_layers''',
				}







				def __init__(						self       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = "student_t"       ,					__lowerCAmelCase = "nll"       ,					__lowerCAmelCase = 1       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = "mean"       ,					__lowerCAmelCase = 0       ,					__lowerCAmelCase = 0       ,					__lowerCAmelCase = 0       ,					__lowerCAmelCase = 0       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = 6_4       ,					__lowerCAmelCase = 3_2       ,					__lowerCAmelCase = 3_2       ,					__lowerCAmelCase = 2       ,					__lowerCAmelCase = 2       ,					__lowerCAmelCase = 2       ,					__lowerCAmelCase = 2       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = "gelu"       ,					__lowerCAmelCase = 0.05       ,					__lowerCAmelCase = 0.1       ,					__lowerCAmelCase = 0.1       ,					__lowerCAmelCase = 0.1       ,					__lowerCAmelCase = 0.1       ,					__lowerCAmelCase = 1_0_0       ,					__lowerCAmelCase = 0.02       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase = "prob"       ,					__lowerCAmelCase = 5       ,					__lowerCAmelCase = True       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								# time series specific configuration
								__magic_name__		:str        =      prediction_length
								__magic_name__		:List[Any]        =      context_length or prediction_length
								__magic_name__		:Dict        =      distribution_output
								__magic_name__		:Optional[int]        =      loss
								__magic_name__		:Tuple        =      input_size
								__magic_name__		:Optional[Any]        =      num_time_features
								__magic_name__		:Optional[int]        =      lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
								__magic_name__		:Dict        =      scaling
								__magic_name__		:Union[str, Any]        =      num_dynamic_real_features
								__magic_name__		:Union[str, Any]        =      num_static_real_features
								__magic_name__		:List[str]        =      num_static_categorical_features

								# set cardinality
								if cardinality and num_static_categorical_features > 0:
												if len(__lowerCAmelCase     ) != num_static_categorical_features:
																raise ValueError(
																    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''     )
												__magic_name__		:Optional[int]        =      cardinality
								else:
												__magic_name__		:List[Any]        =      [0]

								# set embedding_dimension
								if embedding_dimension and num_static_categorical_features > 0:
												if len(__lowerCAmelCase     ) != num_static_categorical_features:
																raise ValueError(
																    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''     )
												__magic_name__		:Tuple        =      embedding_dimension
								else:
												__magic_name__		:Any        =      [min(5_0       ,					(cat + 1) // 2     ) for cat in self.cardinality]

								__magic_name__		:List[str]        =      num_parallel_samples

								# Transformer architecture configuration
								__magic_name__		:Optional[Any]        =      input_size * len(self.lags_sequence     ) + self._number_of_features
								__magic_name__		:List[str]        =      d_model
								__magic_name__		:Union[str, Any]        =      encoder_attention_heads
								__magic_name__		:Optional[int]        =      decoder_attention_heads
								__magic_name__		:Optional[Any]        =      encoder_ffn_dim
								__magic_name__		:Dict        =      decoder_ffn_dim
								__magic_name__		:List[Any]        =      encoder_layers
								__magic_name__		:List[str]        =      decoder_layers

								__magic_name__		:int        =      dropout
								__magic_name__		:Union[str, Any]        =      attention_dropout
								__magic_name__		:str        =      activation_dropout
								__magic_name__		:str        =      encoder_layerdrop
								__magic_name__		:List[Any]        =      decoder_layerdrop

								__magic_name__		:int        =      activation_function
								__magic_name__		:List[Any]        =      init_std

								__magic_name__		:List[str]        =      use_cache

								# Informer
								__magic_name__		:List[str]        =      attention_type
								__magic_name__		:Union[str, Any]        =      sampling_factor
								__magic_name__		:List[Any]        =      distil

								super().__init__(is_encoder_decoder=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return (
								    sum(self.embedding_dimension     )
								    + self.num_dynamic_real_features
								    + self.num_time_features
								    + self.num_static_real_features
								    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
								)














SCREAMING_SNAKE_CASE__      :			str							=		"""Alexander Joslin"""

import operator as op

from .stack import Stack

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

				__magic_name__		:Stack[int]        =      Stack()
				__magic_name__		:Stack[str]        =      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
												__magic_name__		:Optional[int]        =      operator_stack.peek()
												operator_stack.pop()
												__magic_name__		:List[str]        =      operand_stack.peek()
												operand_stack.pop()
												__magic_name__		:Optional[Any]        =      operand_stack.peek()
												operand_stack.pop()

												__magic_name__		:Optional[int]        =      operators[opr](snake_case,  snake_case )
												operand_stack.push(snake_case )

    # RULE 5
				return operand_stack.peek()


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






