"""simple docstring"""



class      _A	:




    """simple docstring"""




    def __init__(		self							:     Optional[int]	,      A_							:     str = ""	,      A_							:     bool = False    )    ->       None:
         # Mapping from the first character of the prefix of the node
         __snake_case				=							{}

         # A node will be a leaf if the tree contains its word
         __snake_case				=							is_leaf

         __snake_case				=							prefix
    def  lowercase					(		self							:     str	,      A_							:     str    )    ->       tuple[str, str, str]:
         __snake_case				=							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  lowercase					(		self							:     Any	,      A_							:     list[str]    )    ->       None:
         for word in words:
              self.insert(A_    )
    def  lowercase					(		self							:     Any	,      A_							:     str    )    ->       None:
         # Case 1: If the word is the prefix of the node
         # Solution: We set the current node as leaf
         if self.prefix == word:
              __snake_case				=							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:
              __snake_case				=							RadixNode(prefix=A_	,      is_leaf=A_    )

         else:
              __snake_case				=							self.nodes[word[0]]
              __snake_case		,	__snake_case		,	__snake_case				=							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:
                   __snake_case				=							remaining_prefix

                   __snake_case				=							self.nodes[matching_string[0]]
                   __snake_case				=							RadixNode(A_	,      A_    )
                   __snake_case				=							aux_node

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

                        return True







    def  lowercase					(		self							:     Optional[int]	,      A_							:     int = 0    )    ->       None:
         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    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							'''banana bananas bandana band apple all beast'''.split()
     __snake_case				=							RadixNode()
     root.insert_many(snake_case)

     assert all(root.find(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    SCREAMING_SNAKE_CASE       (     ):
     assert test_trie()





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							RadixNode()
     __snake_case				=							'''banana bananas bandanas bandana band apple all beast'''.split()
     root.insert_many(snake_case)

     print('''Words:''',      snake_case)
     print('''Tree:''')
     root.print_tree()


if __name__ == "__main__":
       main()
"""simple docstring"""



from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							do_resize
         __snake_case				=							size
         __snake_case				=							resample
         __snake_case				=							do_center_crop
         __snake_case				=							crop_size
         __snake_case				=							do_rescale
         __snake_case				=							rescale_factor
         __snake_case				=							do_normalize
         __snake_case				=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"    )
         return resize(
             A_	,      size=(size_dict['''height'''], size_dict['''width'''])	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"    )
         return center_crop(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[TensorType] = None	,      A_							:     ChannelDimension = ChannelDimension.FIRST	,      **A_							:     Any	,      )    ->       BatchFeature:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __snake_case				=							resample if resample is not None else self.resample
         __snake_case				=							do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case				=							do_rescale if do_rescale is not None else self.do_rescale
         __snake_case				=							rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case				=							do_normalize if do_normalize is not None else self.do_normalize
         __snake_case				=							image_mean if image_mean is not None else self.image_mean
         __snake_case				=							image_std if image_std is not None else self.image_std

         __snake_case				=							size if size is not None else self.size
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else self.crop_size
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							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:
              raise ValueError('''Size 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.
         __snake_case				=							[to_numpy_array(A_    ) for image in images]

         if do_resize:
              __snake_case				=							[self.resize(A_	,      A_	,      A_    ) for image in images]

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

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

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      A_    ) for image in images]

         __snake_case				=							[to_channel_dimension_format(A_	,      A_    ) for image in images]

         __snake_case				=							{'''pixel_values''': images}
         return BatchFeature(data=A_	,      tensor_type=A_    )