from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

_SCREAMING_SNAKE_CASE      						=	logging.get_logger(__name__)





def        lowercase(     UpperCamelCase_    )						->     List[Any]:







 '''simple docstring'''




 if isinstance(_UpperCamelCase				, (list, tuple)    ) and isinstance(videos[0]				, (list, tuple)    ) and is_valid_image(videos[0][0]    ):
  return videos

 elif isinstance(_UpperCamelCase				, (list, tuple)    ) and is_valid_image(videos[0]    ):
  return [videos]

 elif is_valid_image(_UpperCamelCase    ):
  return [[videos]]

 raise ValueError(f"""Could not make batched video from {videos}"""    )


class SCREAMING_SNAKE_CASE_						(	lowerCAmelCase__						):
   __lowerCAmelCase      =							["""pixel_values"""]







   def __init__(			self		:		Optional[Any]					,					lowerCamelCase_		:		Tuple = True					,					lowerCamelCase_		:		Optional[int] = None					,					lowerCamelCase_		:		Optional[Any] = PILImageResampling.BILINEAR					,					lowerCamelCase_		:		List[Any] = True					,					lowerCamelCase_		:		str = None					,					lowerCamelCase_		:		Optional[int] = True					,					lowerCamelCase_		:		Any = 1 / 255					,					lowerCamelCase_		:		int = True					,					lowerCamelCase_		:		Optional[int] = True					,					lowerCamelCase_		:		List[str] = None					,					lowerCamelCase_		:		List[Any] = None					,					**lowerCamelCase_		:		Any					,					):





    """simple docstring"""


    super().__init__(**__a							)
    UpperCamelCase          =	size if size is not None else {"""shortest_edge""": 256}
    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					,					param_name="""crop_size"""							)

    UpperCamelCase          =	do_resize
    UpperCamelCase          =	size
    UpperCamelCase          =	do_center_crop
    UpperCamelCase          =	crop_size
    UpperCamelCase          =	resample
    UpperCamelCase          =	do_rescale
    UpperCamelCase          =	rescale_factor
    UpperCamelCase          =	offset
    UpperCamelCase          =	do_normalize
    UpperCamelCase          =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    UpperCamelCase          =	image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 		lowerCamelCase_     (			self		:		Optional[int]					,					lowerCamelCase_		:		str					,					lowerCamelCase_		:		Optional[int]					,					lowerCamelCase_		:		Dict = PILImageResampling.BILINEAR					,					lowerCamelCase_		:		Optional[int] = None					,					**lowerCamelCase_		:		Optional[int]					,					):





    """simple docstring"""


    UpperCamelCase          =	get_size_dict(__a					,					default_to_square=__a							)
    if "shortest_edge" in size:
     UpperCamelCase          =	get_resize_output_image_size(__a					,					size["""shortest_edge"""]					,					default_to_square=__a							)
    elif "height" in size and "width" in size:
     UpperCamelCase          =	(size["""height"""], size["""width"""])
    else:
     raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""							)
    return resize(__a					,					size=__a					,					resample=__a					,					data_format=__a					,					**__a							)







   def 		lowerCamelCase_     (			self		:		int					,					lowerCamelCase_		:		Tuple					,					lowerCamelCase_		:		List[Any]					,					lowerCamelCase_		:		Optional[int] = None					,					**lowerCamelCase_		:		Any					,					):





    """simple docstring"""


    UpperCamelCase          =	get_size_dict(__a							)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""							)
    return center_crop(__a					,					size=(size["""height"""], size["""width"""])					,					data_format=__a					,					**__a							)







   def 		lowerCamelCase_     (			self		:		List[str]					,					lowerCamelCase_		:		Any					,					lowerCamelCase_		:		str					,					lowerCamelCase_		:		Optional[int] = True					,					lowerCamelCase_		:		Any = None					,					**lowerCamelCase_		:		int					,					):





    """simple docstring"""


    UpperCamelCase          =	image.astype(np.floataa							)
    if offset:
     UpperCamelCase          =	image - (scale / 2)
    return rescale(__a					,					scale=__a					,					data_format=__a					,					**__a							)







   def 		lowerCamelCase_     (			self		:		int					,					lowerCamelCase_		:		Optional[int]					,					lowerCamelCase_		:		List[Any]					,					lowerCamelCase_		:		Any					,					lowerCamelCase_		:		Optional[Any] = None					,					**lowerCamelCase_		:		Dict					,					):





    """simple docstring"""


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







   def 		lowerCamelCase_     (			self		:		List[Any]					,					lowerCamelCase_		:		int					,					lowerCamelCase_		:		Optional[Any] = None					,					lowerCamelCase_		:		Dict = None					,					lowerCamelCase_		:		Tuple = None					,					lowerCamelCase_		:		Tuple = None					,					lowerCamelCase_		:		Optional[Any] = None					,					lowerCamelCase_		:		Tuple = None					,					lowerCamelCase_		:		Tuple = None					,					lowerCamelCase_		:		List[str] = None					,					lowerCamelCase_		:		Union[str, Any] = None					,					lowerCamelCase_		:		Union[str, Any] = None					,					lowerCamelCase_		:		List[Any] = None					,					lowerCamelCase_		:		Union[str, Any] = ChannelDimension.FIRST					,					):





    """simple docstring"""


    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_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."""							)

    if offset and not do_rescale:
     raise ValueError("""For offset, do_rescale must also be set to True."""							)

    # All transformations expect numpy arrays.
    UpperCamelCase          =	to_numpy_array(__a							)

    if do_resize:
     UpperCamelCase          =	self.resize(image=__a					,					size=__a					,					resample=__a							)

    if do_center_crop:
     UpperCamelCase          =	self.center_crop(__a					,					size=__a							)

    if do_rescale:
     UpperCamelCase          =	self.rescale(image=__a					,					scale=__a					,					offset=__a							)

    if do_normalize:
     UpperCamelCase          =	self.normalize(image=__a					,					mean=__a					,					std=__a							)

    UpperCamelCase          =	to_channel_dimension_format(__a					,					__a							)
    return image







   def 		lowerCamelCase_     (			self		:		Any					,					lowerCamelCase_		:		List[Any]					,					lowerCamelCase_		:		Dict = None					,					lowerCamelCase_		:		Optional[Any] = None					,					lowerCamelCase_		:		int = None					,					lowerCamelCase_		:		List[str] = None					,					lowerCamelCase_		:		str = None					,					lowerCamelCase_		:		Tuple = None					,					lowerCamelCase_		:		Union[str, Any] = None					,					lowerCamelCase_		:		Tuple = None					,					lowerCamelCase_		:		Any = None					,					lowerCamelCase_		:		Optional[int] = None					,					lowerCamelCase_		:		List[Any] = None					,					lowerCamelCase_		:		Optional[Any] = None					,					lowerCamelCase_		:		Optional[int] = ChannelDimension.FIRST					,					**lowerCamelCase_		:		List[Any]					,					):





    """simple docstring"""


    UpperCamelCase          =	do_resize if do_resize is not None else self.do_resize
    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          =	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          =	offset if offset is not None else self.offset
    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          =	size if size is not None else self.size
    UpperCamelCase          =	get_size_dict(__a					,					default_to_square=__a							)
    UpperCamelCase          =	crop_size if crop_size is not None else self.crop_size
    UpperCamelCase          =	get_size_dict(__a					,					param_name="""crop_size"""							)

    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."""							)

    UpperCamelCase          =	make_batched(__a							)

    UpperCamelCase          =	[
        [
            self._preprocess_image(
                image=__a					,					do_resize=__a					,					size=__a					,					resample=__a					,					do_center_crop=__a					,					crop_size=__a					,					do_rescale=__a					,					rescale_factor=__a					,					offset=__a					,					do_normalize=__a					,					image_mean=__a					,					image_std=__a					,					data_format=__a					,					)
            for img in video
        ]
        for video in videos
    ]

    UpperCamelCase          =	{"""pixel_values""": videos}
    return BatchFeature(data=__a					,					tensor_type=__a							)




"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			False
			while is_sorted is False:  # Until all the indices are traversed keep looping
						__lowerCAmelCase				    =			True
						for i in range(0    , len(_UpperCamelCase      ) - 1    , 2      ):  # iterating over all even indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase    ,	__lowerCAmelCase				    =			input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase				    =			False

						for i in range(1    , len(_UpperCamelCase      ) - 1    , 2      ):  # iterating over all odd indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase    ,	__lowerCAmelCase				    =			input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase				    =			False
			return input_list


if __name__ == "__main__":
	print("Enter list to be sorted")
	A     :   Union[str, Any]			    =			[int(x) for x in input().split()]
	# inputing elements of the list in one line
	A     :   str			    =			odd_even_sort(input_list)
	print("The sorted list is")
	print(sorted_list)




