class __snake_case				:




   def __init__(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {}  # Mapping from char to TrieNode
     SCREAMING_SNAKE_CASE__										=      False




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				list[str]      ):



     """simple docstring"""






     for word in words:
       self.insert(_lowercase      )




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self
     for char in word:
       if char not in curr.nodes:
         SCREAMING_SNAKE_CASE__										=      TrieNode()
       SCREAMING_SNAKE_CASE__										=      curr.nodes[char]
     SCREAMING_SNAKE_CASE__										=      True




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self
     for char in word:
       if char not in curr.nodes:
         return False
       SCREAMING_SNAKE_CASE__										=      curr.nodes[char]
     return curr.is_leaf




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				str      ):



     """simple docstring"""






     def _delete(_lowercase			:				TrieNode       ,	_lowercase			:				str       ,	_lowercase			:				int      ) -> bool:
       if index == len(_lowercase      ):
         # If word does not exist
         if not curr.is_leaf:
           return False
         SCREAMING_SNAKE_CASE__										=      False
         return len(curr.nodes      ) == 0
       SCREAMING_SNAKE_CASE__										=      word[index]
       SCREAMING_SNAKE_CASE__										=      curr.nodes.get(_lowercase      )
       # If char not in current trie node
       if not char_node:
         return False
       # Flag to check if node can be deleted
       SCREAMING_SNAKE_CASE__										=      _delete(_lowercase       ,	_lowercase       ,	index + 1      )
       if delete_curr:
         del curr.nodes[char]
         return len(curr.nodes      ) == 0
       return delete_curr

     _delete(self       ,	_lowercase       ,	0      )





def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       TrieNode ,     __UpperCamelCase    :       str	)							->					None:







  """simple docstring"""





  if node.is_leaf:
    print(__UpperCamelCase ,     end=""" """	)

  for key, value in node.nodes.items():
    print_words(__UpperCamelCase ,     word + key	)



def 	__SCREAMING_SNAKE_CASE  (					)							->					bool:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      """banana bananas bandana band apple all beast""".split()
  SCREAMING_SNAKE_CASE__										=      TrieNode()
  root.insert_many(__UpperCamelCase	)
  # print_words(root, "")
  assert all(root.find(__UpperCamelCase	) for word in words	)
  assert root.find("""banana"""	)
  assert not root.find("""bandanas"""	)
  assert not root.find("""apps"""	)
  assert root.find("""apple"""	)
  assert root.find("""all"""	)
  root.delete("""all"""	)
  assert not root.find("""all"""	)
  root.delete("""banana"""	)
  assert not root.find("""banana"""	)
  assert root.find("""bananas"""	)
  return True



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       bool	)							->					None:







  """simple docstring"""





  print(str(__UpperCamelCase	) ,     """works!""" if passes else """doesn't work :("""	)



def 	__SCREAMING_SNAKE_CASE  (					)							->					None:







  """simple docstring"""





  assert test_trie()



def 	__SCREAMING_SNAKE_CASE  (					)							->					None:







  """simple docstring"""





  print_results("""Testing trie functionality""" ,     test_trie()	)


if __name__ == "__main__":
   main()

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase			: Union[str, Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Any       ,	_lowercase			:				bool = True       ,	_lowercase			:				int = 32       ,	_lowercase			:				Any=PILImageResampling.BILINEAR       ,	_lowercase			:				bool = True       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor
     SCREAMING_SNAKE_CASE__										=      resample
     super().__init__(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_image_size(_lowercase      )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__										=      height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      resize(_lowercase       ,	(new_h, new_w)       ,	resample=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )
     return image




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				float       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return rescale(image=_lowercase       ,	scale=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[Union[TensorType, str]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__										=      resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
       raise ValueError("""size_divisor is required for resizing"""      )

     SCREAMING_SNAKE_CASE__										=      make_list_of_images(_lowercase      )

     if not valid_images(_lowercase      ):
       raise ValueError("""Invalid image(s)"""      )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__										=      [to_numpy_array(_lowercase      ) for img in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__										=      [self.resize(_lowercase       ,	size_divisor=_lowercase       ,	resample=_lowercase      ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(_lowercase       ,	scale=1 / 2_55      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )
