






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


snake_case__   :						List[str]													=   logging.get_logger(__name__)


if is_vision_available():
 import PIL


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = ["""pixel_values"""]




  def __init__(  self		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = PILImageResampling.BICUBIC		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		_UpperCAmelCase = 1 / 255		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		**_UpperCAmelCase		,		)      ->			None:
     super().__init__(**_UpperCAmelCase			)
     UpperCamelCase_						=  size if size is not None else {'shortest_edge': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase		,		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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = PILImageResampling.BICUBIC		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     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(_UpperCAmelCase		,		size=size['shortest_edge']		,		default_to_square=_UpperCAmelCase			)
     return resize(_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase			)
     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(_UpperCAmelCase		,		size=(size['height'], size['width'])		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			str:
     return rescale(_UpperCAmelCase		,		scale=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = ChannelDimension.FIRST		,		**_UpperCAmelCase		,		)      ->			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(_UpperCAmelCase		,		param_name='size'		,		default_to_square=_UpperCAmelCase			)
     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(_UpperCAmelCase		,		param_name='crop_size'		,		default_to_square=_UpperCAmelCase			)
     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(_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.'			)

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

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

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

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

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

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

     UpperCamelCase_						=  [to_channel_dimension_format(_UpperCAmelCase		,		_UpperCAmelCase			) for image in images]

     UpperCamelCase_						=  {'pixel_values': images}
     return BatchFeature(data=_UpperCAmelCase		,		tensor_type=_UpperCAmelCase			)









from multiprocessing import Lock, Pipe, Process

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


def     lowercase	(    a     , a     , a     , a     , a     , a     , a ):



		'''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(a )
						process_lock.release()

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

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

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

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


def     lowercase	(    a ):



		'''simple docstring'''




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

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

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


def     lowercase	(    ):



		'''simple docstring'''




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


if __name__ == "__main__":
		main()






