




from multiprocessing import Lock, Pipe, Process

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Tuple:
     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()
               a     =				rr_cv[0].recv()
               process_lock.release()

               # take the lower value since you are on the left
               a     =				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()
               a     =				lr_cv[0].recv()
               process_lock.release()

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					List[str]:
     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=_UpperCAmelCase		, 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(_UpperCAmelCase) - 1):
          a     =				Pipe()
          a     =				Pipe()
          process_array_.append(
              Process(
                  target=_UpperCAmelCase		, 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=_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)):
          a     =				result_pipe[p][0].recv()
          process_array_[p].join()
     return arr

def  SCREAMING_SNAKE_CASE							(       )      ->					Tuple:
     a     =				list(range(10		, 0		, -1))
     print("Initial List")
     print(*_UpperCAmelCase)
     a     =				odd_even_transposition(_UpperCAmelCase)
     print("Sorted List\n")
     print(*_UpperCAmelCase)


if __name__ == "__main__":
       main()







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__				:      Tuple	   = logging.get_logger(__name__)

lowercase__				:      int	   = {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}




class       a__	( UpperCamelCase__       , UpperCamelCase__				):
     a		:      Any		 =     """resnet"""
     a		:      Tuple		 =     ["""basic""", """bottleneck"""]







     def __init__(     self  ,		A=3  ,		A=64  ,		A=[256, 512, 1024, 2048]  ,		A=[3, 4, 6, 3]  ,		A="bottleneck"  ,		A="relu"  ,		A=False  ,		A=None  ,		A=None  ,		**A  ,		)  ->   Any:



          '''simple docstring'''



          super().__init__(**A			)
          if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
          a     =				num_channels
          a     =				embedding_size
          a     =				hidden_sizes
          a     =				depths
          a     =				layer_type
          a     =				hidden_act
          a     =				downsample_in_first_stage
          a     =				["stem"] + [F'''stage{idx}''' for idx in range(1  ,		len(A			) + 1			)]
          a  ,				a     =				get_aligned_output_features_output_indices(
              out_features=A  ,		out_indices=A  ,		stage_names=self.stage_names			)







class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-3

