


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_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					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						,							1_0  ):
										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()
															lowercase_  							=			rr_cv[0].recv()
															process_lock.release()

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

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


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			[]
					lowercase_  							=			[]
					# 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
					lowercase_  							=			Pipe()
					lowercase_  							=			Pipe()
					process_array_.append(
					    Process(
					        target=UpperCAmelCase__						,							args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])						,							)  )
					lowercase_  							=			temp_rs
					lowercase_  							=			temp_rr

					for i in range(1						,							len(UpperCAmelCase__  ) - 1  ):
										lowercase_  							=			Pipe()
										lowercase_  							=			Pipe()
										process_array_.append(
										    Process(
										        target=UpperCAmelCase__						,							args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])						,							)  )
										lowercase_  							=			temp_rs
										lowercase_  							=			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__  )  ):
										lowercase_  							=			result_pipe[p][0].recv()
										process_array_[p].join()
					return arr


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			list(range(1_0						,							0						,							-1  )  )
					print("""Initial List"""  )
					print(*UpperCAmelCase__  )
					lowercase_  							=			odd_even_transposition(UpperCAmelCase__  )
					print("""Sorted List\n"""  )
					print(*UpperCAmelCase__  )


if __name__ == "__main__":
							main()






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


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   ['pixel_values']

					def __init__(     self       :   List[str]							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   PILImageResampling = PILImageResampling.BICUBIC							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Union[int, float] = 1 / 255							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )
										lowercase_  							=			size if size is not None else {"""shortest_edge""": 224}
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
										lowercase_  							=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					param_name="""crop_size"""       )

										lowercase_  							=			do_resize
										lowercase_  							=			size
										lowercase_  							=			resample
										lowercase_  							=			do_center_crop
										lowercase_  							=			crop_size
										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_normalize
										lowercase_  							=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										lowercase_  							=			image_std if image_std is not None else IMAGENET_DEFAULT_STD

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Dict[str, int]							,					UpperCamelCase__       :   PILImageResampling = PILImageResampling.BICUBIC							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
										# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
										if "shortest_edge" in size:
															lowercase_  							=			int((256 / 224) * size["""shortest_edge"""]       )
															lowercase_  							=			get_resize_output_image_size(UpperCamelCase__							,					size=UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
															lowercase_  							=			{"""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(
										    UpperCamelCase__							,					size=(size_dict["""height"""], size_dict["""width"""])							,					resample=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Dict[str, int]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(UpperCamelCase__       )
										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(UpperCamelCase__							,					size=(size["""height"""], size["""width"""])							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Union[int, float]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										return rescale(UpperCamelCase__							,					scale=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Union[float, List[float]]							,					UpperCamelCase__       :   Union[float, List[float]]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										return normalize(UpperCamelCase__							,					mean=UpperCamelCase__							,					std=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Dict							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Dict[str, int]] = None							,					UpperCamelCase__       :   PILImageResampling = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Dict[str, int]] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = None							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = None							,					UpperCamelCase__       :   Optional[TensorType] = None							,					UpperCamelCase__       :   ChannelDimension = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										lowercase_  							=			do_resize if do_resize is not None else self.do_resize
										lowercase_  							=			resample if resample is not None else self.resample
										lowercase_  							=			do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase_  							=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_  							=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_  							=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_  							=			image_mean if image_mean is not None else self.image_mean
										lowercase_  							=			image_std if image_std is not None else self.image_std

										lowercase_  							=			size if size is not None else self.size
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
										lowercase_  							=			crop_size if crop_size is not None else self.crop_size
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					param_name="""crop_size"""       )

										lowercase_  							=			make_list_of_images(UpperCamelCase__       )

										if not valid_images(UpperCamelCase__       ):
															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.
										lowercase_  							=			[to_numpy_array(UpperCamelCase__       ) for image in images]

										if do_resize:
															lowercase_  							=			[self.resize(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_center_crop:
															lowercase_  							=			[self.center_crop(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_rescale:
															lowercase_  							=			[self.rescale(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_normalize:
															lowercase_  							=			[self.normalize(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			[to_channel_dimension_format(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			{"""pixel_values""": images}
										return BatchFeature(data=UpperCamelCase__							,					tensor_type=UpperCamelCase__       )


