

class A__    :



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	str = ""     ,						A_ :	bool = False       ):






      '''simple docstring'''




      _lowerCAmelCase   :					dict[str, RadixNode]        =    {}

      # A node will be a leaf if the tree contains its word
      _lowerCAmelCase   :					Optional[int]        =    is_leaf

      _lowerCAmelCase   :					Tuple        =    prefix

 def    __magic_name__       (		self :	Tuple     ,						A_ :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    0
      for q, w in zip(self.prefix     ,						A_       ):
           if q != w:
                break

           x += 1

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

 def    __magic_name__       (		self :	str     ,						A_ :	list[str]       ):






      '''simple docstring'''




      for word in words:
           self.insert(A_       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	str       ):






      '''simple docstring'''




      if self.prefix == word:
           _lowerCAmelCase   :					List[str]        =    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:
           _lowerCAmelCase   :					int        =    RadixNode(prefix=A_     ,						is_leaf=A_       )

      else:
           _lowerCAmelCase   :					List[str]        =    self.nodes[word[0]]
           _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					int        =    incoming_node.match(
               A_       )

           # 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(A_       )

           # 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:
                _lowerCAmelCase   :					Union[str, Any]        =    remaining_prefix

                _lowerCAmelCase   :					str        =    self.nodes[matching_string[0]]
                _lowerCAmelCase   :					List[Any]        =    RadixNode(A_     ,						A_       )
                _lowerCAmelCase   :					Dict        =    aux_node

                if remaining_word == "":
                     _lowerCAmelCase   :					List[str]        =    True
                else:
                     self.nodes[matching_string[0]].insert(A_       )

 def    __magic_name__       (		self :	Dict     ,						A_ :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.nodes.get(word[0]     ,						A_       )
      if not incoming_node:
           return False
      else:
           _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    incoming_node.match(
               A_       )
           # 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(A_       )

 def    __magic_name__       (		self :	str     ,						A_ :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.nodes.get(word[0]     ,						A_       )
      if not incoming_node:
           return False
      else:
           _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    incoming_node.match(
               A_       )
           # 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(A_       )
           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:
                               _lowerCAmelCase   :					List[str]        =    list(self.nodes.values()       )[0]
                               _lowerCAmelCase   :					List[str]        =    merging_node.is_leaf
                               self.prefix += merging_node.prefix
                               _lowerCAmelCase   :					Optional[Any]        =    merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                     elif len(incoming_node.nodes       ) > 1:
                          _lowerCAmelCase   :					Any        =    False
                     # If there is 1 edge, we merge it with its child
                     else:
                          _lowerCAmelCase   :					Dict        =    list(incoming_node.nodes.values()       )[0]
                          _lowerCAmelCase   :					List[str]        =    merging_node.is_leaf
                          incoming_node.prefix += merging_node.prefix
                          _lowerCAmelCase   :					List[str]        =    merging_node.nodes

                     return True


 def    __magic_name__       (		self :	List[Any]     ,						A_ :	int = 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 	_snake_case		(    )       ->      bool:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    "banana bananas bandana band apple all beast".split()
     _lowerCAmelCase   :					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 	_snake_case		(    )       ->      None:

     """simple docstring"""







     assert test_trie()







def 	_snake_case		(    )       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    RadixNode()
     _lowerCAmelCase   :					List[str]        =    "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()









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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	List[str]     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					str        =    size if size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					str        =    do_resize
      _lowerCAmelCase   :					str        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Union[str, Any]        =    do_center_crop
      _lowerCAmelCase   :					Union[str, Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					List[str]        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return resize(
          A_     ,						size=(size["height"], size["width"])     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Any     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[str, Any]=None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					int        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					Optional[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					int        =    do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase   :					str        =    image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase   :					List[Any]        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					int        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Tuple        =    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_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."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Union[str, Any]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					List[str]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Any        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					str        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      if do_normalize:
           _lowerCAmelCase   :					Dict        =    [self.normalize(image=A_     ,						mean=A_     ,						std=A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					int        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )






