


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]				):
     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    							=rr_cv[0].recv()
               process_lock.release()

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

               # take the higher value since you are on the right
               __UpperCamelCase    							=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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=[]
     __UpperCamelCase    							=[]
     # 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    							=Pipe()
     __UpperCamelCase    							=Pipe()
     process_array_.append(
         Process(
             target=SCREAMING_SNAKE_CASE__			,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])			,     )				)
     __UpperCamelCase    							=temp_rs
     __UpperCamelCase    							=temp_rr

     for i in range(1			,     len(SCREAMING_SNAKE_CASE__				) - 1				):
          __UpperCamelCase    							=Pipe()
          __UpperCamelCase    							=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    							=temp_rs
          __UpperCamelCase    							=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    							=result_pipe[p][0].recv()
          process_array_[p].join()
     return arr

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							=list(range(10			,     0			,     -1				)				)
     print('Initial List'				)
     print(*SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=odd_even_transposition(SCREAMING_SNAKE_CASE__				)
     print('Sorted List\n'				)
     print(*SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
   main()









from typing import Dict, List, Optional, Union

import numpy as np

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


_A		       =						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Union[str, Any]      =  ["pixel_values"]






    def __init__(			self					,   A_ = True					,   A_ = None					,   A_ = PILImageResampling.BICUBIC					,   A_ = True					,   A_ = None					,   A_ = True					,   A_ = 1 / 255					,   A_ = True					,   A_ = None					,   A_ = None					,   A_ = True					,   **A_					,   )  ->       None:
         super().__init__(**A_    )
         __UpperCamelCase    							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase    							=get_size_dict(A_					,   default_to_square=A_    )
         __UpperCamelCase    							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __UpperCamelCase    							=get_size_dict(A_					,   default_to_square=A_					,   param_name='crop_size'    )

         __UpperCamelCase    							=do_resize
         __UpperCamelCase    							=size
         __UpperCamelCase    							=resample
         __UpperCamelCase    							=do_center_crop
         __UpperCamelCase    							=crop_size
         __UpperCamelCase    							=do_rescale
         __UpperCamelCase    							=rescale_factor
         __UpperCamelCase    							=do_normalize
         __UpperCamelCase    							=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         __UpperCamelCase    							=image_std if image_std is not None else OPENAI_CLIP_STD
         __UpperCamelCase    							=do_convert_rgb






    def _a					(			self					,   A_					,   A_					,   A_ = PILImageResampling.BICUBIC					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         __UpperCamelCase    							=get_size_dict(A_					,   default_to_square=A_    )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'    )
         __UpperCamelCase    							=get_resize_output_image_size(A_					,   size=size['shortest_edge']					,   default_to_square=A_    )
         return resize(A_					,   size=A_					,   resample=A_					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         __UpperCamelCase    							=get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'    )
         return center_crop(A_					,   size=(size['height'], size['width'])					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       Union[str, Any]:
         return rescale(A_					,   scale=A_					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         return normalize(A_					,   mean=A_					,   std=A_					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = ChannelDimension.FIRST					,   **A_					,   )  ->       PIL.Image.Image:
         __UpperCamelCase    							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase    							=size if size is not None else self.size
         __UpperCamelCase    							=get_size_dict(A_					,   param_name='size'					,   default_to_square=A_    )
         __UpperCamelCase    							=resample if resample is not None else self.resample
         __UpperCamelCase    							=do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase    							=crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase    							=get_size_dict(A_					,   param_name='crop_size'					,   default_to_square=A_    )
         __UpperCamelCase    							=do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase    							=rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase    							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase    							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase    							=image_std if image_std is not None else self.image_std
         __UpperCamelCase    							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __UpperCamelCase    							=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.'    )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              __UpperCamelCase    							=[convert_to_rgb(A_    ) for image in images]

         # All transformations expect numpy arrays.
         __UpperCamelCase    							=[to_numpy_array(A_    ) for image in images]

         if do_resize:
              __UpperCamelCase    							=[self.resize(image=A_					,   size=A_					,   resample=A_    ) for image in images]

         if do_center_crop:
              __UpperCamelCase    							=[self.center_crop(image=A_					,   size=A_    ) for image in images]

         if do_rescale:
              __UpperCamelCase    							=[self.rescale(image=A_					,   scale=A_    ) for image in images]

         if do_normalize:
              __UpperCamelCase    							=[self.normalize(image=A_					,   mean=A_					,   std=A_    ) for image in images]

         __UpperCamelCase    							=[to_channel_dimension_format(A_					,   A_    ) for image in images]

         __UpperCamelCase    							={'pixel_values': images}
         return BatchFeature(data=A_					,   tensor_type=A_    )





