


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 convert_to_rgb, 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


if is_vision_available():
		import PIL


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Dict       			=  ["""pixel_values"""]






		def __init__(						self				:     Tuple   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BICUBIC   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Union[int, float] = 1 / 2_55   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     bool = True   ,  **UpperCAmelCase				:     Any   ,  ):
				super().__init__(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						size if size is not None else {"height": 3_84, "width": 3_84}
				SCREAMING_SNAKE_CASE_				:str				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						do_resize
				SCREAMING_SNAKE_CASE_				:int				     =						size
				SCREAMING_SNAKE_CASE_				:int				     =						resample
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_rescale
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						rescale_factor
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_normalize
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				SCREAMING_SNAKE_CASE_				:Dict				     =						image_std if image_std is not None else OPENAI_CLIP_STD
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_convert_rgb






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Dict[str, int]   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BICUBIC   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     Any   ,  ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)
				if "height" not in size or "width" not in size:
						raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						(size["height"], size["width"])
				return resize(UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)






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






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







		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     ImageInput   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Dict[str, int]] = None   ,  UpperCAmelCase				:     PILImageResampling = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[float] = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[str, TensorType]] = None   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     ChannelDimension = ChannelDimension.FIRST   ,  **UpperCAmelCase				:     Dict   ,  ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_rescale if do_rescale is not None else self.do_rescale
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						rescale_factor if rescale_factor is not None else self.rescale_factor
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image_mean if image_mean is not None else self.image_mean
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						image_std if image_std is not None else self.image_std
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				SCREAMING_SNAKE_CASE_				:Dict				     =						size if size is not None else self.size
				SCREAMING_SNAKE_CASE_				:Tuple				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						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 or resample is None:
						raise ValueError("Size and resample must be specified if do_resize 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_				:Union[str, Any]				     =						[convert_to_rgb(UpperCAmelCase) for image in images]

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[to_numpy_array(UpperCAmelCase) for image in images]

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

				if do_rescale:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[self.rescale(image=UpperCAmelCase   ,  scale=UpperCAmelCase) for image in images]

				if do_normalize:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[self.normalize(image=UpperCAmelCase   ,  mean=UpperCAmelCase   ,  std=UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[to_channel_dimension_format(UpperCAmelCase   ,  UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						BatchFeature(data={"pixel_values": images}   ,  tensor_type=UpperCAmelCase)

				return encoded_outputs










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()






