

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


UpperCamelCase__  :      Optional[Any]			=    logging.get_logger(__name__)


if is_vision_available():
   import PIL



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	List[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						,		):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'shortest_edge': 224}
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		default_to_square=_A)
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		default_to_square=_A						,		param_name='crop_size')

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = PILImageResampling.BICUBIC						,		_A = None						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						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()}""")
      SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = None						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = None						,		**_A						,		):
      return rescale(_A						,		scale=_A						,		data_format=_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A = None						,		**_A						,		):
      return normalize(_A						,		mean=_A						,		std=_A						,		data_format=_A						,		**_A)


  def 						lowerCAmelCase__     (					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						,		):
      SCREAMING_SNAKE_CASE_									=						do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_									=						size if size is not None else self.size
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		param_name='size'						,		default_to_square=_A)
      SCREAMING_SNAKE_CASE_									=						resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_									=						do_center_crop if do_center_crop is not None else self.do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else self.crop_size
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		param_name='crop_size'						,		default_to_square=_A)
      SCREAMING_SNAKE_CASE_									=						do_rescale if do_rescale is not None else self.do_rescale
      SCREAMING_SNAKE_CASE_									=						rescale_factor if rescale_factor is not None else self.rescale_factor
      SCREAMING_SNAKE_CASE_									=						do_normalize if do_normalize is not None else self.do_normalize
      SCREAMING_SNAKE_CASE_									=						image_mean if image_mean is not None else self.image_mean
      SCREAMING_SNAKE_CASE_									=						image_std if image_std is not None else self.image_std
      SCREAMING_SNAKE_CASE_									=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      SCREAMING_SNAKE_CASE_									=						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:
          SCREAMING_SNAKE_CASE_									=						[convert_to_rgb(_A) for image in images]

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

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

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

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

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

      SCREAMING_SNAKE_CASE_									=						[to_channel_dimension_format(_A						,		_A) for image in images]

      SCREAMING_SNAKE_CASE_									=						{'pixel_values': images}
      return BatchFeature(data=_A						,		tensor_type=_A)





from multiprocessing import Lock, Pipe, Process

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Any       ):





    """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()
            SCREAMING_SNAKE_CASE_									=						rr_cv[0].recv()
            process_lock.release()

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

            # take the higher value since you are on the right
            SCREAMING_SNAKE_CASE_									=						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					:		Union[str, Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    # 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
    SCREAMING_SNAKE_CASE_									=						Pipe()
    SCREAMING_SNAKE_CASE_									=						Pipe()
    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE ,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,   )       )
    SCREAMING_SNAKE_CASE_									=						temp_rs
    SCREAMING_SNAKE_CASE_									=						temp_rr

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


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						list(range(10 ,   0 ,   -1       )       )
    print('Initial List'       )
    print(*_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						odd_even_transposition(_SCREAMING_SNAKE_CASE       )
    print('Sorted List\n'       )
    print(*_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       main()


