

'''simple docstring'''

from multiprocessing import Lock, Pipe, Process

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





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  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(snake_case )
      process_lock.release()

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

      # take the lower value since you are on the left
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					min(snake_case      ,       snake_case )
    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(snake_case )
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					lr_cv[0].recv()
      process_lock.release()

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





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					[]
  SCREAMING_SNAKE_CASE:Optional[int]						      =					[]
  # 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
  SCREAMING_SNAKE_CASE:Dict						      =					Pipe()
  SCREAMING_SNAKE_CASE:Any						      =					Pipe()
  process_array_.append(
      Process(
          target=snake_case      ,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])      ,       ) )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					temp_rs
  SCREAMING_SNAKE_CASE:List[str]						      =					temp_rr

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

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





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Dict						      =					list(range(10      ,       0      ,       -1 ) )
  print("Initial List" )
  print(*snake_case )
  SCREAMING_SNAKE_CASE:int						      =					odd_even_transposition(snake_case )
  print("Sorted List\n" )
  print(*snake_case )


if __name__ == "__main__":
    main()









'''simple docstring'''

import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_       						=							{
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     List[Any]       =					'''mask2former'''
   _A       :     str       =					['''swin''']
   _A       :     Tuple       =					{'''hidden_size''': '''hidden_dim'''}
   def __init__( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[Dict] = None     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 1_024     ,SCREAMING_SNAKE_CASE__			:      str = "relu"     ,SCREAMING_SNAKE_CASE__			:      int = 6     ,SCREAMING_SNAKE_CASE__			:      int = 10     ,SCREAMING_SNAKE_CASE__			:      int = 8     ,SCREAMING_SNAKE_CASE__			:      float = 0.0     ,SCREAMING_SNAKE_CASE__			:      int = 2_048     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      int = 4     ,SCREAMING_SNAKE_CASE__			:      int = 255     ,SCREAMING_SNAKE_CASE__			:      int = 100     ,SCREAMING_SNAKE_CASE__			:      float = 0.1     ,SCREAMING_SNAKE_CASE__			:      float = 2.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      int = 12_544     ,SCREAMING_SNAKE_CASE__			:      float = 3.0     ,SCREAMING_SNAKE_CASE__			:      float = 0.75     ,SCREAMING_SNAKE_CASE__			:      float = 0.02     ,SCREAMING_SNAKE_CASE__			:      float = 1.0     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      List[int] = [4, 8, 16, 32]     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     if backbone_config is None:
       logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					CONFIG_MAPPING["swin"](
           image_size=224     ,in_channels=3     ,patch_size=4     ,embed_dim=96     ,depths=[2, 2, 18, 2]     ,num_heads=[3, 6, 12, 24]     ,window_size=7     ,drop_path_rate=0.3     ,use_absolute_embeddings=SCREAMING_SNAKE_CASE__     ,out_features=["stage1", "stage2", "stage3", "stage4"]     ,)

     if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any						      =					backbone_config.pop("model_type"							)
       SCREAMING_SNAKE_CASE:List[str]						      =					CONFIG_MAPPING[backbone_model_type]
       SCREAMING_SNAKE_CASE:List[str]						      =					config_class.from_dict(SCREAMING_SNAKE_CASE__							)

     # verify that the backbone is supported
     if backbone_config.model_type not in self.backbones_supported:
       logger.warning_once(
           F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
           F'''Supported model types: {",".join(self.backbones_supported							)}'''							)

     SCREAMING_SNAKE_CASE:List[str]						      =					backbone_config
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					feature_size
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					mask_feature_size
     SCREAMING_SNAKE_CASE:str						      =					hidden_dim
     SCREAMING_SNAKE_CASE:Optional[int]						      =					encoder_feedforward_dim
     SCREAMING_SNAKE_CASE:Tuple						      =					activation_function
     SCREAMING_SNAKE_CASE:Optional[int]						      =					encoder_layers
     SCREAMING_SNAKE_CASE:List[Any]						      =					decoder_layers
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					num_attention_heads
     SCREAMING_SNAKE_CASE:Dict						      =					dropout
     SCREAMING_SNAKE_CASE:List[str]						      =					dim_feedforward
     SCREAMING_SNAKE_CASE:int						      =					pre_norm
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					enforce_input_projection
     SCREAMING_SNAKE_CASE:Any						      =					common_stride
     SCREAMING_SNAKE_CASE:int						      =					ignore_value
     SCREAMING_SNAKE_CASE:List[Any]						      =					num_queries
     SCREAMING_SNAKE_CASE:Dict						      =					no_object_weight
     SCREAMING_SNAKE_CASE:str						      =					class_weight
     SCREAMING_SNAKE_CASE:Tuple						      =					mask_weight
     SCREAMING_SNAKE_CASE:Optional[int]						      =					dice_weight
     SCREAMING_SNAKE_CASE:int						      =					train_num_points
     SCREAMING_SNAKE_CASE:str						      =					oversample_ratio
     SCREAMING_SNAKE_CASE:str						      =					importance_sample_ratio
     SCREAMING_SNAKE_CASE:str						      =					init_std
     SCREAMING_SNAKE_CASE:Any						      =					init_xavier_std
     SCREAMING_SNAKE_CASE:List[Any]						      =					use_auxiliary_loss
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					feature_strides
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					output_auxiliary_logits
     SCREAMING_SNAKE_CASE:Dict						      =					decoder_layers

     super().__init__(**SCREAMING_SNAKE_CASE__							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      PretrainedConfig     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     return cls(
         backbone_config=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,)







   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					copy.deepcopy(self.__dict__							)
     SCREAMING_SNAKE_CASE:str						      =					self.backbone_config.to_dict()
     SCREAMING_SNAKE_CASE:List[str]						      =					self.__class__.model_type
     return output






