




from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__lowerCAmelCase		:				str       			=		Lock()




def 			__magic_name__				( A		:					Optional[Any],  A		:					Any,  A		:					Union[str, Any],  A		:					List[str],  A		:					int,  A		:					Optional[int],  A		:					Dict   ):






			'''simple docstring'''







			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(A   )
									process_lock.release()

									# receive your right neighbor's value
									process_lock.acquire()
									a    =			rr_cv[0].recv()
									process_lock.release()

									# take the lower value since you are on the left
									a    =			min(A,  A   )
						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(A   )
									process_lock.release()

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

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




def 			__magic_name__				( A		:					int   ):






			'''simple docstring'''







			a    =			[]
			a    =			[]
			# 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
			a    =			Pipe()
			a    =			Pipe()
			process_array_.append(
			    Process(
			        target=A,  args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),  )   )
			a    =			temp_rs
			a    =			temp_rr

			for i in range(1,  len(A   ) - 1   ):
						a    =			Pipe()
						a    =			Pipe()
						process_array_.append(
						    Process(
						        target=A,  args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),  )   )
						a    =			temp_rs
						a    =			temp_rr

			process_array_.append(
			    Process(
			        target=A,  args=(
			            len(A   ) - 1,
			            arr[len(A   ) - 1],
			            temp_ls,
			            None,
			            temp_lr,
			            None,
			            result_pipe[len(A   ) - 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(A   )   ):
						a    =			result_pipe[p][0].recv()
						process_array_[p].join()
			return arr




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			list(range(10,  0,  -1   )   )
			print("Initial List"   )
			print(*A   )
			a    =			odd_even_transposition(A   )
			print("Sorted List\n"   )
			print(*A   )


if __name__ == "__main__":
		main()












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__lowerCAmelCase		:				int       			=		{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					"""albert"""





				def __init__(      self				:     List[str]					,      __lowerCamelCase				:     Union[str, Any]=3_00_00					,      __lowerCamelCase				:     Union[str, Any]=1_28					,      __lowerCamelCase				:     Any=40_96					,      __lowerCamelCase				:     str=12					,      __lowerCamelCase				:     Union[str, Any]=1					,      __lowerCamelCase				:     Union[str, Any]=64					,      __lowerCamelCase				:     Dict=1_63_84					,      __lowerCamelCase				:     List[Any]=1					,      __lowerCamelCase				:     Optional[Any]="gelu_new"					,      __lowerCamelCase				:     List[str]=0					,      __lowerCamelCase				:     List[str]=0					,      __lowerCamelCase				:     Tuple=5_12					,      __lowerCamelCase				:     Optional[Any]=2					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     Optional[Any]=1e-12					,      __lowerCamelCase				:     Union[str, Any]=0.1					,      __lowerCamelCase				:     Any="absolute"					,      __lowerCamelCase				:     List[str]=0					,      __lowerCamelCase				:     List[str]=2					,      __lowerCamelCase				:     Any=3					,      **__lowerCamelCase				:     int					,      )					->     int:
							super().__init__(pad_token_id=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase							)

							a    =			vocab_size
							a    =			embedding_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_hidden_groups
							a    =			num_attention_heads
							a    =			inner_group_num
							a    =			hidden_act
							a    =			intermediate_size
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			initializer_range
							a    =			layer_norm_eps
							a    =			classifier_dropout_prob
							a    =			position_embedding_type




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										a    =			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										a    =			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							    ]							)






