

class 						_A     :





  '''simple docstring'''



  def __init__(  self    ):

      '''simple docstring'''
      snake_case							:		dict[str, TrieNode]        =		{}  # Mapping from char to TrieNode
      snake_case							:		Optional[int]        =		False





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      for word in words:
          self.insert(SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		int        =		self
      for char in word:
          if char not in curr.nodes:
              snake_case							:		Optional[Any]        =		TrieNode()
          snake_case							:		Optional[Any]        =		curr.nodes[char]
      snake_case							:		List[Any]        =		True





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		self
      for char in word:
          if char not in curr.nodes:
              return False
          snake_case							:		Any        =		curr.nodes[char]
      return curr.is_leaf





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''

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

      _delete(self   ,SCREAMING_SNAKE_CASE_   ,0    )







def       lowercase					(	__A :					TrieNode ,    __A :					str		)				->			None:





    '''simple docstring'''




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

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







def       lowercase					(	)				->			bool:





    '''simple docstring'''




    snake_case							:		Any        =		"""banana bananas bandana band apple all beast""".split()
    snake_case							:		Dict        =		TrieNode()
    root.insert_many(__A		)
    # print_words(root, "")
    assert all(root.find(__A		) 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       lowercase					(	__A :					str ,    __A :					bool		)				->			None:





    '''simple docstring'''




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







def       lowercase					(	)				->			None:





    '''simple docstring'''




    assert test_trie()







def       lowercase					(	)				->			None:





    '''simple docstring'''




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


if __name__ == "__main__":
     main()







'''simple docstring'''


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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


_UpperCamelCase 							=  logging.get_logger(__name__)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							b.T
     __A	:       Optional[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=1							)
     __A	:       List[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=0							)
     __A	:       List[Any]      =							np.matmul(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     __A	:       str      =							aa[:, None] - 2 * ab + ba[None, :]
     return d


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							x.reshape(-1       ,					3							)
     __A	:       Any      =							squared_euclidean_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     return np.argmin(SCREAMING_SNAKE_CASE       ,					axis=1							)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				['pixel_values']


     def __init__(   self			,     lowerCamelCase = None			,     lowerCamelCase = True			,     lowerCamelCase = None			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = True			,     lowerCamelCase = True			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A	:       Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
          __A	:       int      =							get_size_dict(lowerCamelCase		)
          __A	:       str      =							np.array(lowerCamelCase		) if clusters is not None else None
          __A	:       List[Any]      =							do_resize
          __A	:       Dict      =							size
          __A	:       str      =							resample
          __A	:       Optional[Any]      =							do_normalize
          __A	:       Dict      =							do_color_quantize


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[Any]      =							get_size_dict(lowerCamelCase		)
          if "height" not in size or "width" not in size:
               raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}"		)
          return resize(
              lowerCamelCase			,     size=(size["height"], size["width"])			,     resample=lowerCamelCase			,     data_format=lowerCamelCase			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     ):


          '''simple docstring'''





          __A	:       List[str]      =							rescale(image=lowerCamelCase			,     scale=1 / 127.5			,     data_format=lowerCamelCase		)
          __A	:       List[Any]      =							image - 1
          return image


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = ChannelDimension.FIRST			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       List[Any]      =							do_resize if do_resize is not None else self.do_resize
          __A	:       int      =							size if size is not None else self.size
          __A	:       Tuple      =							get_size_dict(lowerCamelCase		)
          __A	:       Tuple      =							resample if resample is not None else self.resample
          __A	:       Union[str, Any]      =							do_normalize if do_normalize is not None else self.do_normalize
          __A	:       Tuple      =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
          __A	:       Any      =							clusters if clusters is not None else self.clusters
          __A	:       Dict      =							np.array(lowerCamelCase		)

          __A	:       Optional[int]      =							make_list_of_images(lowerCamelCase		)

          if not valid_images(lowerCamelCase		):
               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_color_quantize and clusters is None:
               raise ValueError("Clusters must be specified if do_color_quantize is True."		)

          # All transformations expect numpy arrays.
          __A	:       Optional[Any]      =							[to_numpy_array(lowerCamelCase		) for image in images]

          if do_resize:
               __A	:       Dict      =							[self.resize(image=lowerCamelCase			,     size=lowerCamelCase			,     resample=lowerCamelCase		) for image in images]

          if do_normalize:
               __A	:       List[Any]      =							[self.normalize(image=lowerCamelCase		) for image in images]

          if do_color_quantize:
               __A	:       Union[str, Any]      =							[to_channel_dimension_format(lowerCamelCase			,     ChannelDimension.LAST		) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               __A	:       int      =							np.array(lowerCamelCase		)
               __A	:       Any      =							color_quantize(lowerCamelCase			,     lowerCamelCase		).reshape(images.shape[:-1]		)

               # flatten to (batch_size, height*width)
               __A	:       Tuple      =							images.shape[0]
               __A	:       List[Any]      =							images.reshape(lowerCamelCase			,     -1		)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               __A	:       Optional[int]      =							list(lowerCamelCase		)
          else:
               __A	:       Tuple      =							[to_channel_dimension_format(lowerCamelCase			,     lowerCamelCase		) for image in images]

          __A	:       Optional[int]      =							{"input_ids": images}
          return BatchFeature(data=lowerCamelCase			,     tensor_type=lowerCamelCase		)

