


"""simple docstring"""







from typing import List, Optional

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


lowerCAmelCase__				    =       logging.get_logger(__name__)

lowerCAmelCase__				    =       {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Dict       ="autoformer"
       a  :    Dict       ={
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
           "num_hidden_layers": "encoder_layers",
       }

       def __init__(						self		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = "student_t"		,					snake_case__ = "nll"		,					snake_case__ = 1		,					snake_case__ = [1, 2, 3, 4, 5, 6, 7]		,					snake_case__ = True		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = 64		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 32		,					snake_case__ = 32		,					snake_case__ = "gelu"		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 100		,					snake_case__ = 0.02		,					snake_case__ = True		,					snake_case__=True		,					snake_case__ = 10		,					snake_case__ = 25		,					snake_case__ = 3		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							prediction_length
         lowerCAmelCase					:					Dict								=							context_length if context_length is not None else prediction_length
         lowerCAmelCase					:					Tuple								=							distribution_output
         lowerCAmelCase					:					List[Any]								=							loss
         lowerCAmelCase					:					int								=							input_size
         lowerCAmelCase					:					str								=							num_time_features
         lowerCAmelCase					:					str								=							lags_sequence
         lowerCAmelCase					:					List[str]								=							scaling
         lowerCAmelCase					:					List[Any]								=							num_dynamic_real_features
         lowerCAmelCase					:					Tuple								=							num_static_real_features
         lowerCAmelCase					:					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`"						)
           lowerCAmelCase					:					Any								=							cardinality
         else:
           lowerCAmelCase					:					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`"						)
           lowerCAmelCase					:					Tuple								=							embedding_dimension
         else:
           lowerCAmelCase					:					Any								=							[min(50		,					(cat + 1) // 2						) for cat in self.cardinality]
         lowerCAmelCase					:					Any								=							num_parallel_samples

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

         lowerCAmelCase					:					List[Any]								=							dropout
         lowerCAmelCase					:					Optional[int]								=							attention_dropout
         lowerCAmelCase					:					Union[str, Any]								=							activation_dropout
         lowerCAmelCase					:					Optional[int]								=							encoder_layerdrop
         lowerCAmelCase					:					Dict								=							decoder_layerdrop

         lowerCAmelCase					:					Tuple								=							activation_function
         lowerCAmelCase					:					Optional[Any]								=							init_std

         lowerCAmelCase					:					List[Any]								=							use_cache

         # Autoformer
         lowerCAmelCase					:					Any								=							label_length
         lowerCAmelCase					:					Any								=							moving_average
         lowerCAmelCase					:					Optional[Any]								=							autocorrelation_factor

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







       @property
       def 	lowercase__							(						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
         )









"""simple docstring"""







from typing import Any


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							data
         lowerCAmelCase					:					Tuple								=							None


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							None

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.head
         while temp is not None:
           print(temp.data		,					end=" "						)
           lowerCAmelCase					:					List[str]								=							temp.next
         print()

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							Node(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							self.head
         lowerCAmelCase					:					Optional[Any]								=							new_node







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         if node_data_a == node_data_a:
           return
         else:
           lowerCAmelCase					:					str								=							self.head
           while node_a is not None and node_a.data != node_data_a:
             lowerCAmelCase					:					Union[str, Any]								=							node_a.next

           lowerCAmelCase					:					Any								=							self.head
           while node_a is not None and node_a.data != node_data_a:
             lowerCAmelCase					:					List[Any]								=							node_a.next

           if node_a is None or node_a is None:
             return

           lowerCAmelCase						,  lowerCAmelCase					:					str								=							node_a.data, node_a.data


if __name__ == "__main__":
     lowerCAmelCase__				    =       LinkedList()
     for i in range(5, 0, -1):
          ll.push(i)

     ll.print_list()

     ll.swap_nodes(1, 4)
     print('''After swapping''')
     ll.print_list()





