


from multiprocessing import Lock, Pipe, Process

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''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(SCREAMING_SNAKE_CASE_	)
      process_lock.release()

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

      # take the lower value since you are on the left
      lowerCamelCase :					int    =				min(SCREAMING_SNAKE_CASE_      ,						SCREAMING_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(SCREAMING_SNAKE_CASE_	)
      process_lock.release()

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

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				[]
  lowerCamelCase :					Dict    =				[]
  # 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
  lowerCamelCase :					Any    =				Pipe()
  lowerCamelCase :					Dict    =				Pipe()
  process_array_.append(
      Process(
          target=SCREAMING_SNAKE_CASE_      ,						args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])      ,						)	)
  lowerCamelCase :					Tuple    =				temp_rs
  lowerCamelCase :					int    =				temp_rr

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

  process_array_.append(
      Process(
          target=SCREAMING_SNAKE_CASE_      ,						args=(
              len(SCREAMING_SNAKE_CASE_	) - 1,
              arr[len(SCREAMING_SNAKE_CASE_	) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(SCREAMING_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(SCREAMING_SNAKE_CASE_	)	):
    lowerCamelCase :					Optional[Any]    =				result_pipe[p][0].recv()
    process_array_[p].join()
  return arr






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Optional[int]    =				list(range(10      ,						0      ,						-1	)	)
  print("Initial List"	)
  print(*SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					int    =				odd_even_transposition(SCREAMING_SNAKE_CASE_	)
  print("Sorted List\n"	)
  print(*SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   main()









# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import torch




class     snake_case__							(TensorFormatter[Mapping, """torch.Tensor""", Mapping]   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]=None					,      **__lowerCamelCase				:     Any							)					->     Optional[Any]:
							super().__init__(features=__lowerCamelCase							)
							a    =			torch_tensor_kwargs
							import torch  # noqa import torch at initialization





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Dict							)					->     Dict:
							import torch

							if isinstance(__lowerCamelCase					,      __lowerCamelCase							) and column:
										if all(
										    isinstance(__lowerCamelCase					,      torch.Tensor							) and x.shape == column[0].shape and x.dtype == column[0].dtype
										    for x in column							):
													return torch.stack(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]							)					->     str:
							import torch

							if isinstance(__lowerCamelCase					,      (str, bytes, type(__lowerCamelCase							))							):
										return value
							elif isinstance(__lowerCamelCase					,      (np.character, np.ndarray)							) and np.issubdtype(value.dtype					,      np.character							):
										return value.tolist()

							a    =			{}

							if isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.integer							):
										a    =			{"dtype": torch.intaa}
							elif isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.floating							):
										a    =			{"dtype": torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
										import PIL.Image

										if isinstance(__lowerCamelCase					,      PIL.Image.Image							):
													a    =			np.asarray(__lowerCamelCase							)
							return torch.tensor(__lowerCamelCase					,      **{**default_dtype, **self.torch_tensor_kwargs}							)





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Tuple							)					->     List[str]:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(__lowerCamelCase					,      "__array__"							) and not isinstance(__lowerCamelCase					,      torch.Tensor							):
										a    =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(__lowerCamelCase					,      np.ndarray							):
										if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
													return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							elif isinstance(__lowerCamelCase					,      (list, tuple)							):
										return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							return self._tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     dict							)					->     str:
							return map_nested(self._recursive_tensorize					,      __lowerCamelCase					,      map_list=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_row(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_row(__lowerCamelCase							)
							return self.recursive_tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     pa.Table							)					->     "torch.Tensor":
							a    =			self.numpy_arrow_extractor().extract_column(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_column(__lowerCamelCase					,      pa_table.column_names[0]							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							a    =			self._consolidate(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_batch(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_batch(__lowerCamelCase							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							for column_name in batch:
										a    =			self._consolidate(batch[column_name]							)
							return batch






