



"""simple docstring"""




from multiprocessing import Lock, Pipe, Process

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						List[Any]    )  ->       int:





     '''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()
               _UpperCAmelCase      :     Optional[int]        =   rr_cv[0].recv()
               process_lock.release()

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

               # take the higher value since you are on the right
               _UpperCAmelCase      :     Optional[Any]        =   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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     str        =   []
     _UpperCAmelCase      :     List[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      :     Union[str, Any]        =   Pipe()
     _UpperCAmelCase      :     str        =   Pipe()
     process_array_.append(
         Process(
             target=SCREAMING_SNAKE_CASE__						,  args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])						,  )    )
     _UpperCAmelCase      :     Optional[Any]        =   temp_rs
     _UpperCAmelCase      :     str        =   temp_rr

     for i in range(1						,  len(SCREAMING_SNAKE_CASE__    ) - 1    ):
          _UpperCAmelCase      :     Optional[int]        =   Pipe()
          _UpperCAmelCase      :     str        =   Pipe()
          process_array_.append(
              Process(
                  target=SCREAMING_SNAKE_CASE__						,  args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])						,  )    )
          _UpperCAmelCase      :     Tuple        =   temp_rs
          _UpperCAmelCase      :     List[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__    )    ):
          _UpperCAmelCase      :     Tuple        =   result_pipe[p][0].recv()
          process_array_[p].join()
     return arr

def 						__snake_case						(				)  ->       Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   list(range(10						,  0						,  -1    )    )
     print("Initial List"    )
     print(*SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     str        =   odd_even_transposition(SCREAMING_SNAKE_CASE__    )
     print("Sorted List\n"    )
     print(*SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 main()










"""simple docstring"""




from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase    : Tuple							=							logging.get_logger(__name__)






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       str								=			['pixel_values']



      def __init__(					self					:     List[Any]					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     bool = True					,     A					:     Union[int, float] = 1 / 2_5_5					,     A					:     bool = True					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD					,     **A					:     List[Any]					,     ):
           super().__init__(**A						)
           _UpperCAmelCase      :     int        =   size if size is not None else {"shortest_edge": 2_2_4}
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     int        =   crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           _UpperCAmelCase      :     Union[str, Any]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     str        =   do_resize
           _UpperCAmelCase      :     Union[str, Any]        =   size
           _UpperCAmelCase      :     Tuple        =   resample
           _UpperCAmelCase      :     List[Any]        =   do_center_crop
           _UpperCAmelCase      :     List[Any]        =   crop_size
           _UpperCAmelCase      :     Tuple        =   do_rescale
           _UpperCAmelCase      :     List[Any]        =   rescale_factor
           _UpperCAmelCase      :     Dict        =   do_normalize
           _UpperCAmelCase      :     Optional[Any]        =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           _UpperCAmelCase      :     Any        =   image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Union[str, Any]					,     ):
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                _UpperCAmelCase      :     Any        =   int((2_5_6 / 2_2_4) * size["shortest_edge"]						)
                _UpperCAmelCase      :     Union[str, Any]        =   get_resize_output_image_size(A					,     size=A					,     default_to_square=A						)
                _UpperCAmelCase      :     str        =   {"height": output_size[0], "width": output_size[1]}
           if "height" not in size_dict or "width" not in size_dict:
                raise ValueError(
                    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'						)
           return resize(
               A					,     size=(size_dict["height"], size_dict["width"])					,     resample=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     int					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Optional[Any]					,     ):
           _UpperCAmelCase      :     Dict        =   get_size_dict(A						)
           if "height" not in size or "width" not in size:
                raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'						)
           return center_crop(A					,     size=(size["height"], size["width"])					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     np.ndarray					,     A					:     Union[int, float]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Dict					,     ):
           return rescale(A					,     scale=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     np.ndarray					,     A					:     Union[float, List[float]]					,     A					:     Union[float, List[float]]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     str					,     ):
           return normalize(A					,     mean=A					,     std=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     ImageInput					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     PILImageResampling = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[float] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[TensorType] = None					,     A					:     ChannelDimension = ChannelDimension.FIRST					,     **A					:     Dict					,     ):
           _UpperCAmelCase      :     Dict        =   do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase      :     List[str]        =   resample if resample is not None else self.resample
           _UpperCAmelCase      :     int        =   do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase      :     str        =   do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase      :     Tuple        =   rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase      :     Optional[int]        =   do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase      :     List[str]        =   image_mean if image_mean is not None else self.image_mean
           _UpperCAmelCase      :     Tuple        =   image_std if image_std is not None else self.image_std

           _UpperCAmelCase      :     Any        =   size if size is not None else self.size
           _UpperCAmelCase      :     Optional[int]        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     List[str]        =   crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase      :     List[str]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     List[Any]        =   make_list_of_images(A						)

           if not valid_images(A						):
                raise ValueError(
                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                    "torch.Tensor, tf.Tensor or jax.ndarray."						)

           if do_resize and size is None:
                raise ValueError("Size must be specified if do_resize is True."						)

           if do_center_crop and crop_size is None:
                raise ValueError("Crop size must be specified if do_center_crop is True."						)

           if do_rescale and rescale_factor is None:
                raise ValueError("Rescale factor must be specified if do_rescale is True."						)

           if do_normalize and (image_mean is None or image_std is None):
                raise ValueError("Image mean and std must be specified if do_normalize is True."						)

           # All transformations expect numpy arrays.
           _UpperCAmelCase      :     Any        =   [to_numpy_array(A						) for image in images]

           if do_resize:
                _UpperCAmelCase      :     str        =   [self.resize(A					,     A					,     A						) for image in images]

           if do_center_crop:
                _UpperCAmelCase      :     Dict        =   [self.center_crop(A					,     A						) for image in images]

           if do_rescale:
                _UpperCAmelCase      :     Dict        =   [self.rescale(A					,     A						) for image in images]

           if do_normalize:
                _UpperCAmelCase      :     Optional[int]        =   [self.normalize(A					,     A					,     A						) for image in images]

           _UpperCAmelCase      :     Union[str, Any]        =   [to_channel_dimension_format(A					,     A						) for image in images]

           _UpperCAmelCase      :     List[Any]        =   {"pixel_values": images}
           return BatchFeature(data=A					,     tensor_type=A						)
