


"""simple docstring"""






from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase__				:Optional[Any]			   =	Lock()





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[Any]:
			global process_lock

			# we perform n swaps since after n swaps we know we are sorted
			# we *could* stop early if we are sorted already, but it takes as long to
			# find out we are sorted as it does to sort the list with this algorithm
			for i in range(0       ,					10		):
						if (i + position) % 2 == 0 and r_send is not None:
									# send your value to your right neighbor
									process_lock.acquire()
									r_send[1].send(UpperCamelCase_		)
									process_lock.release()

									# receive your right neighbor's value
									process_lock.acquire()
									_UpperCamelCase					:Union[str, Any]					     =	rr_cv[0].recv()
									process_lock.release()

									# take the lower value since you are on the left
									_UpperCamelCase					:List[Any]					     =	min(UpperCamelCase_       ,					UpperCamelCase_		)
						elif (i + position) % 2 != 0 and l_send is not None:
									# send your value to your left neighbor
									process_lock.acquire()
									l_send[1].send(UpperCamelCase_		)
									process_lock.release()

									# receive your left neighbor's value
									process_lock.acquire()
									_UpperCamelCase					:List[str]					     =	lr_cv[0].recv()
									process_lock.release()

									# take the higher value since you are on the right
									_UpperCamelCase					:List[Any]					     =	max(UpperCamelCase_       ,					UpperCamelCase_		)
    # after all swaps are performed, send the values back to main
			result_pipe[1].send(UpperCamelCase_		)





def 				A_       ( snake_case__		)					->  str:
			_UpperCamelCase					:List[str]					     =	[]
			_UpperCamelCase					:Any					     =	[]
			# initialize the list of pipes where the values will be retrieved
			for _ in arr:
						result_pipe.append(Pipe()		)
			# creates the processes
			# the first and last process only have one neighbor so they are made outside
			# of the loop
			_UpperCamelCase					:str					     =	Pipe()
			_UpperCamelCase					:Dict					     =	Pipe()
			process_array_.append(
			    Process(
			        target=UpperCamelCase_       ,					args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])       ,					)		)
			_UpperCamelCase					:Optional[Any]					     =	temp_rs
			_UpperCamelCase					:Any					     =	temp_rr

			for i in range(1       ,					len(UpperCamelCase_		) - 1		):
						_UpperCamelCase					:Optional[int]					     =	Pipe()
						_UpperCamelCase					:List[str]					     =	Pipe()
						process_array_.append(
						    Process(
						        target=UpperCamelCase_       ,					args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])       ,					)		)
						_UpperCamelCase					:Optional[Any]					     =	temp_rs
						_UpperCamelCase					:str					     =	temp_rr

			process_array_.append(
			    Process(
			        target=UpperCamelCase_       ,					args=(
			            len(UpperCamelCase_		) - 1,
			            arr[len(UpperCamelCase_		) - 1],
			            temp_ls,
			            None,
			            temp_lr,
			            None,
			            result_pipe[len(UpperCamelCase_		) - 1],
			        )       ,					)		)

			# start the processes
			for p in process_array_:
						p.start()

			# wait for the processes to end and write their values to the list
			for p in range(0       ,					len(UpperCamelCase_		)		):
						_UpperCamelCase					:List[str]					     =	result_pipe[p][0].recv()
						process_array_[p].join()
			return arr





def 				A_       ( )					->  Optional[Any]:
			_UpperCamelCase					:Optional[Any]					     =	list(range(10       ,					0       ,					-1		)		)
			print('''Initial List'''		)
			print(*UpperCamelCase_		)
			_UpperCamelCase					:str					     =	odd_even_transposition(UpperCamelCase_		)
			print('''Sorted List\n'''		)
			print(*UpperCamelCase_		)


if __name__ == "__main__":
					main()





'''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 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"autoformer"
 UpperCAmelCase				=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None 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`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None 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`''' )
       UpperCAmelCase__	:List[str]      =      embedding_dimension
    else:
       UpperCAmelCase__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

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

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

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

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





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    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
    )



