






"""simple docstring"""



from typing import List, Optional

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


_lowerCAmelCase	:	Tuple				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Any				=      {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''autoformer'''
   SCREAMING_SNAKE_CASE_    ={
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
       '''num_hidden_layers''': '''encoder_layers''',
   }
   def __init__(		self      :	Optional[int]							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	str = "student_t"							,			snake_case__      :	str = "nll"							,			snake_case__      :	int = 1							,			snake_case__      :	List[int] = [1, 2, 3, 4, 5, 6, 7]							,			snake_case__      :	bool = True							,			snake_case__      :	int = 0							,			snake_case__      :	int = 0							,			snake_case__      :	int = 0							,			snake_case__      :	int = 0							,			snake_case__      :	Optional[List[int]] = None							,			snake_case__      :	Optional[List[int]] = None							,			snake_case__      :	int = 6_4							,			snake_case__      :	int = 2							,			snake_case__      :	int = 2							,			snake_case__      :	int = 2							,			snake_case__      :	int = 2							,			snake_case__      :	int = 3_2							,			snake_case__      :	int = 3_2							,			snake_case__      :	str = "gelu"							,			snake_case__      :	float = 0.1							,			snake_case__      :	float = 0.1							,			snake_case__      :	float = 0.1							,			snake_case__      :	float = 0.1							,			snake_case__      :	float = 0.1							,			snake_case__      :	int = 1_0_0							,			snake_case__      :	float = 0.02							,			snake_case__      :	bool = True							,			snake_case__      :	Dict=True							,			snake_case__      :	int = 1_0							,			snake_case__      :	int = 2_5							,			snake_case__      :	int = 3							,			**snake_case__      :	Any							,			):


        '''simple docstring'''
        # time series specific configuration
        UpperCAmelCase__						:    str							=							prediction_length
        UpperCAmelCase__						:    Any							=							context_length if context_length is not None else prediction_length
        UpperCAmelCase__						:    Union[str, Any]							=							distribution_output
        UpperCAmelCase__						:    Union[str, Any]							=							loss
        UpperCAmelCase__						:    Union[str, Any]							=							input_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_time_features
        UpperCAmelCase__						:    List[str]							=							lags_sequence
        UpperCAmelCase__						:    int							=							scaling
        UpperCAmelCase__						:    Dict							=							num_dynamic_real_features
        UpperCAmelCase__						:    Union[str, Any]							=							num_static_real_features
        UpperCAmelCase__						:    Dict							=							num_static_categorical_features
        if cardinality is not None and num_static_categorical_features > 0:
             if len(snake_case__							) != num_static_categorical_features:
                  raise ValueError(
                      "The cardinality should be a list of the same length as `num_static_categorical_features`"							)
             UpperCAmelCase__						:    int							=							cardinality
        else:
             UpperCAmelCase__						:    Union[str, Any]							=							[0]
        if embedding_dimension is not None and num_static_categorical_features > 0:
             if len(snake_case__							) != num_static_categorical_features:
                  raise ValueError(
                      "The embedding dimension should be a list of the same length as `num_static_categorical_features`"							)
             UpperCAmelCase__						:    Optional[int]							=							embedding_dimension
        else:
             UpperCAmelCase__						:    Dict							=							[min(5_0							,			(cat + 1) // 2							) for cat in self.cardinality]
        UpperCAmelCase__						:    Optional[Any]							=							num_parallel_samples

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

        UpperCAmelCase__						:    List[str]							=							dropout
        UpperCAmelCase__						:    str							=							attention_dropout
        UpperCAmelCase__						:    Dict							=							activation_dropout
        UpperCAmelCase__						:    Optional[Any]							=							encoder_layerdrop
        UpperCAmelCase__						:    Tuple							=							decoder_layerdrop

        UpperCAmelCase__						:    Any							=							activation_function
        UpperCAmelCase__						:    Optional[int]							=							init_std

        UpperCAmelCase__						:    Union[str, Any]							=							use_cache

        # Autoformer
        UpperCAmelCase__						:    Optional[int]							=							label_length
        UpperCAmelCase__						:    int							=							moving_average
        UpperCAmelCase__						:    str							=							autocorrelation_factor

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




   @property
   def       __a							(		self      :	Optional[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"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : list				)->		list:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCAmelCase__						:    List[Any]							=							True
          for i in range(0			,					len(snake_case				) - 1			,					2				):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    int							=							False

          for i in range(1			,					len(snake_case				) - 1			,					2				):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    str							=							False
     return input_list


if __name__ == "__main__":
 print("""Enter list to be sorted""")
 _lowerCAmelCase	:	List[str]				=      [int(x) for x in input().split()]
 # inputing elements of the list in one line
 _lowerCAmelCase	:	Optional[int]				=      odd_even_sort(input_list)
 print("""The sorted list is""")
 print(sorted_list)


