


'''simple docstring'''


class 							__magic_name__	:

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase = ""			,     lowerCamelCase = False		):


          '''simple docstring'''





          __A	:       dict[str, RadixNode]      =							{}

          # A node will be a leaf if the tree contains its word
          __A	:       int      =							is_leaf

          __A	:       List[str]      =							prefix


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							0
          for q, w in zip(self.prefix			,     lowerCamelCase		):
               if q != w:
                    break

               x += 1

          return self.prefix[:x], self.prefix[x:], word[x:]


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          for word in words:
               self.insert(lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          if self.prefix == word:
               __A	:       Dict      =							True

          # Case 2: The node has no edges that have a prefix to the word
          # Solution: We create an edge from the current node to a new one
          # containing the word
          elif word[0] not in self.nodes:
               __A	:       str      =							RadixNode(prefix=lowerCamelCase			,     is_leaf=lowerCamelCase		)

          else:
               __A	:       Tuple      =							self.nodes[word[0]]
               __A  ,__A  ,__A	:       Optional[Any]      =							incoming_node.match(
                   lowerCamelCase		)

               # Case 3: The node prefix is equal to the matching
               # Solution: We insert remaining word on the next node
               if remaining_prefix == "":
                    self.nodes[matching_string[0]].insert(lowerCamelCase		)

               # Case 4: The word is greater equal to the matching
               # Solution: Create a node in between both nodes, change
               # prefixes and add the new node for the remaining word
               else:
                    __A	:       List[str]      =							remaining_prefix

                    __A	:       Union[str, Any]      =							self.nodes[matching_string[0]]
                    __A	:       List[Any]      =							RadixNode(lowerCamelCase			,     lowerCamelCase		)
                    __A	:       str      =							aux_node

                    if remaining_word == "":
                         __A	:       int      =							True
                    else:
                         self.nodes[matching_string[0]].insert(lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.nodes.get(word[0]			,     lowerCamelCase		)
          if not incoming_node:
               return False
          else:
               __A  ,__A  ,__A	:       str      =							incoming_node.match(
                   lowerCamelCase		)
               # If there is remaining prefix, the word can't be on the tree
               if remaining_prefix != "":
                    return False
               # This applies when the word and the prefix are equal
               elif remaining_word == "":
                    return incoming_node.is_leaf
               # We have word remaining so we check the next node
               else:
                    return incoming_node.find(lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.nodes.get(word[0]			,     lowerCamelCase		)
          if not incoming_node:
               return False
          else:
               __A  ,__A  ,__A	:       Any      =							incoming_node.match(
                   lowerCamelCase		)
               # If there is remaining prefix, the word can't be on the tree
               if remaining_prefix != "":
                    return False
               # We have word remaining so we check the next node
               elif remaining_word != "":
                    return incoming_node.delete(lowerCamelCase		)
               else:
                    # If it is not a leaf, we don't have to delete
                    if not incoming_node.is_leaf:
                         return False
                    else:
                         # We delete the nodes if no edges go from it
                         if len(incoming_node.nodes		) == 0:
                              del self.nodes[word[0]]
                              # We merge the current node with its only child
                              if len(self.nodes		) == 1 and not self.is_leaf:
                                   __A	:       Tuple      =							list(self.nodes.values()		)[0]
                                   __A	:       int      =							merging_node.is_leaf
                                   self.prefix += merging_node.prefix
                                   __A	:       Any      =							merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                         elif len(incoming_node.nodes		) > 1:
                              __A	:       Optional[int]      =							False
                         # If there is 1 edge, we merge it with its child
                         else:
                              __A	:       Union[str, Any]      =							list(incoming_node.nodes.values()		)[0]
                              __A	:       Optional[Any]      =							merging_node.is_leaf
                              incoming_node.prefix += merging_node.prefix
                              __A	:       Optional[int]      =							merging_node.nodes

                         return True


     def lowerCAmelCase__  (   self			,     lowerCamelCase = 0		):


          '''simple docstring'''





          if self.prefix != "":
               print("-" * height			,     self.prefix			,     "  (leaf)" if self.is_leaf else ""		)

          for value in self.nodes.values():
               value.print_tree(height + 1		)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[int]      =							"banana bananas bandana band apple all beast".split()
     __A	:       Tuple      =							RadixNode()
     root.insert_many(SCREAMING_SNAKE_CASE							)

     assert all(root.find(SCREAMING_SNAKE_CASE							) for word in words							)
     assert not root.find("bandanas"							)
     assert not root.find("apps"							)
     root.delete("all"							)
     assert not root.find("all"							)
     root.delete("banana"							)
     assert not root.find("banana"							)
     assert root.find("bananas"							)

     return True


def 		_lowercase ():




     '''simple docstring'''





     assert test_trie()


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[int]      =							RadixNode()
     __A	:       Union[str, Any]      =							"banana bananas bandanas bandana band apple all beast".split()
     root.insert_many(SCREAMING_SNAKE_CASE							)

     print("Words:"       ,					SCREAMING_SNAKE_CASE							)
     print("Tree:"							)
     root.print_tree()


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		)

