




"""simple docstring"""


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


A_      =    logging.get_logger(__name__)






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = ["pixel_values"]


 def __init__(    self:		Union[str, Any],				a_:		bool = True,				a_:		Union[int, float] = 1 / 255,				a_:		bool = True,				a_:		int = 8,				**a_:		List[str],				):




   '''simple docstring'''



   super().__init__(**a_		)

   _snake_case							:		int								=  do_rescale
   _snake_case							:		List[str]								=  rescale_factor
   _snake_case							:		Optional[Any]								=  do_pad
   _snake_case							:		Tuple								=  pad_size


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		np.ndarray,				a_:		float,				a_:		Optional[Union[str, ChannelDimension]] = None,				**a_:		str		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Any,				a_:		np.ndarray,				a_:		int,				a_:		Optional[Union[str, ChannelDimension]] = None		):




   '''simple docstring'''



   _snake_case		, _snake_case							:		str								=  get_image_size(a_		)
   _snake_case							:		str								=  (old_height // size + 1) * size - old_height
   _snake_case							:		Optional[Any]								=  (old_width // size + 1) * size - old_width

   return pad(a_,				((0, pad_height), (0, pad_width)),				mode="""symmetric""",				data_format=a_		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		ImageInput,				a_:		Optional[bool] = None,				a_:		Optional[float] = None,				a_:		Optional[bool] = None,				a_:		Optional[int] = None,				a_:		Optional[Union[str, TensorType]] = None,				a_:		Union[str, ChannelDimension] = ChannelDimension.FIRST,				**a_:		str,				):




   '''simple docstring'''



   _snake_case							:		Dict								=  do_rescale if do_rescale is not None else self.do_rescale
   _snake_case							:		Union[str, Any]								=  rescale_factor if rescale_factor is not None else self.rescale_factor
   _snake_case							:		str								=  do_pad if do_pad is not None else self.do_pad
   _snake_case							:		Optional[int]								=  pad_size if pad_size is not None else self.pad_size

   _snake_case							:		Union[str, Any]								=  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_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

   # All transformations expect numpy arrays.
   _snake_case							:		List[Any]								=  [to_numpy_array(a_		) for image in images]

   if do_rescale:
     _snake_case							:		int								=  [self.rescale(image=a_,				scale=a_		) for image in images]

   if do_pad:
     _snake_case							:		List[str]								=  [self.pad(a_,				size=a_		) for image in images]

   _snake_case							:		Dict								=  [to_channel_dimension_format(a_,				a_		) for image in images]

   _snake_case							:		int								=  {"""pixel_values""": images}
   return BatchFeature(data=a_,				tensor_type=a_		)








"""simple docstring"""


from typing import Any






class  lowercase:







 '''simple docstring'''





 def __init__(    self:		Dict,				a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Dict								=  data
   _snake_case							:		Optional[Any]								=  None







class  lowercase:







 '''simple docstring'''





 def __init__(    self:		str		):




   '''simple docstring'''



   _snake_case							:		Any								=  None


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.head
   while temp is not None:
     print(temp.data,				end=""" """		)
     _snake_case							:		int								=  temp.next
   print()


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  Node(a_		)
   _snake_case							:		Union[str, Any]								=  self.head
   _snake_case							:		List[Any]								=  new_node


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		List[str],				a_:		Union[str, Any]		):




   '''simple docstring'''



   if node_data_a == node_data_a:
     return
   else:
     _snake_case							:		int								=  self.head
     while node_a is not None and node_a.data != node_data_a:
       _snake_case							:		List[Any]								=  node_a.next

     _snake_case							:		List[Any]								=  self.head
     while node_a is not None and node_a.data != node_data_a:
       _snake_case							:		List[Any]								=  node_a.next

     if node_a is None or node_a is None:
       return

     _snake_case		, _snake_case							:		int								=  node_a.data, node_a.data


if __name__ == "__main__":
     A_      =    LinkedList()
     for i in range(5, 0, -1):
          ll.push(i)

     ll.print_list()

     ll.swap_nodes(1, 4)
     print('''After swapping''')
     ll.print_list()


