

'''simple docstring'''







from typing import List, Optional, Union

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


__lowerCamelCase						    =      logging.get_logger(__name__)

__lowerCamelCase						    =      {
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class    A__ ( _snake_case     ):
    lowercase		   =						"time_series_transformer"
    lowercase		   =						{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
        "num_hidden_layers": "encoder_layers",
    }



    def __init__(   self						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = "student_t"						,			UpperCamelCase__ = "nll"						,			UpperCamelCase__ = 1						,			UpperCamelCase__ = [1, 2, 3, 4, 5, 6, 7]						,			UpperCamelCase__ = "mean"						,			UpperCamelCase__ = 0						,			UpperCamelCase__ = 0						,			UpperCamelCase__ = 0						,			UpperCamelCase__ = 0						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = 32						,			UpperCamelCase__ = 32						,			UpperCamelCase__ = 2						,			UpperCamelCase__ = 2						,			UpperCamelCase__ = 2						,			UpperCamelCase__ = 2						,			UpperCamelCase__ = True						,			UpperCamelCase__ = "gelu"						,			UpperCamelCase__ = 64						,			UpperCamelCase__ = 0.1						,			UpperCamelCase__ = 0.1						,			UpperCamelCase__ = 0.1						,			UpperCamelCase__ = 0.1						,			UpperCamelCase__ = 0.1						,			UpperCamelCase__ = 100						,			UpperCamelCase__ = 0.02						,			UpperCamelCase__=True						,			**UpperCamelCase__						,			)  -> Optional[int]:





           '''simple docstring'''
           # time series specific configuration
           A_		   = prediction_length
           A_		   = context_length or prediction_length
           A_		   = distribution_output
           A_		   = loss
           A_		   = input_size
           A_		   = num_time_features
           A_		   = lags_sequence
           A_		   = scaling
           A_		   = num_dynamic_real_features
           A_		   = num_static_real_features
           A_		   = num_static_categorical_features
           if cardinality and num_static_categorical_features > 0:
                  if len(UpperCamelCase__    ) != num_static_categorical_features:
                         raise ValueError(
                             """The cardinality should be a list of the same length as `num_static_categorical_features`"""    )
                  A_		   = cardinality
           else:
                  A_		   = [0]
           if embedding_dimension and num_static_categorical_features > 0:
                  if len(UpperCamelCase__    ) != num_static_categorical_features:
                         raise ValueError(
                             """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""    )
                  A_		   = embedding_dimension
           else:
                  A_		   = [min(50						,			(cat + 1) // 2    ) for cat in self.cardinality]
           A_		   = num_parallel_samples

           # Transformer architecture configuration
           A_		   = input_size * len(UpperCamelCase__    ) + self._number_of_features
           A_		   = d_model
           A_		   = encoder_attention_heads
           A_		   = decoder_attention_heads
           A_		   = encoder_ffn_dim
           A_		   = decoder_ffn_dim
           A_		   = encoder_layers
           A_		   = decoder_layers

           A_		   = dropout
           A_		   = attention_dropout
           A_		   = activation_dropout
           A_		   = encoder_layerdrop
           A_		   = decoder_layerdrop

           A_		   = activation_function
           A_		   = init_std

           A_		   = use_cache

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







    @property
    def    snake_case_     (   self    )  -> int:





           '''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
           )



'''simple docstring'''







from __future__ import annotations

from typing import Any
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if not postfix_notation:
              return 0

       A_		   = {"""+""", """-""", """*""", """/"""}
       A_		   = []

       for token in postfix_notation:
              if token in operations:
                     A_   ,     A_		   = stack.pop(), stack.pop()
                     if token == "+":
                            stack.append(a + b			)
                     elif token == "-":
                            stack.append(a - b			)
                     elif token == "*":
                            stack.append(a * b			)
                     else:
                            if a * b < 0 and a % b != 0:
                                   stack.append(a // b + 1			)
                            else:
                                   stack.append(a // b			)
              else:
                     stack.append(int(UpperCAmelCase__			)			)

       return stack.pop()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
