






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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


__magic_name__   :  List[Any]        =							logging.get_logger(__name__)





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					["""pixel_values"""]







     def __init__( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int] = None   ,						_SCREAMING_SNAKE_CASE      :    PILImageResampling = PILImageResampling.BICUBIC   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Union[int, float] = 1 / 255   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, List[float]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, List[float]]] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						**_SCREAMING_SNAKE_CASE      :    List[Any]   ,						):






           """simple docstring"""




           super().__init__(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  size if size is not None else {'height': 384, 'width': 384}
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  do_resize
           UpperCamelCase					       =  size
           UpperCamelCase					       =  resample
           UpperCamelCase					       =  do_rescale
           UpperCamelCase					       =  rescale_factor
           UpperCamelCase					       =  do_normalize
           UpperCamelCase					       =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           UpperCamelCase					       =  image_std if image_std is not None else OPENAI_CLIP_STD
           UpperCamelCase					       =  do_convert_rgb







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int]   ,						_SCREAMING_SNAKE_CASE      :    PILImageResampling = PILImageResampling.BICUBIC   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    List[Any]   ,						):






           """simple docstring"""




           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           if "height" not in size or "width" not in size:
                 raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
           UpperCamelCase					       =  (size['height'], size['width'])
           return resize(_SCREAMING_SNAKE_CASE   ,						size=_SCREAMING_SNAKE_CASE   ,						resample=_SCREAMING_SNAKE_CASE   ,						data_format=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )







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






           """simple docstring"""




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







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






           """simple docstring"""




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







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






           """simple docstring"""




           UpperCamelCase					       =  do_resize if do_resize is not None else self.do_resize
           UpperCamelCase					       =  resample if resample is not None else self.resample
           UpperCamelCase					       =  do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase					       =  rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase					       =  do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase					       =  image_mean if image_mean is not None else self.image_mean
           UpperCamelCase					       =  image_std if image_std is not None else self.image_std
           UpperCamelCase					       =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           UpperCamelCase					       =  size if size is not None else self.size
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  make_list_of_images(_SCREAMING_SNAKE_CASE )

           if not valid_images(_SCREAMING_SNAKE_CASE ):
                 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_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.' )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 UpperCamelCase					       =  [convert_to_rgb(_SCREAMING_SNAKE_CASE ) for image in images]

           # All transformations expect numpy arrays.
           UpperCamelCase					       =  [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]

           if do_resize:
                 UpperCamelCase					       =  [self.resize(image=_SCREAMING_SNAKE_CASE   ,						size=_SCREAMING_SNAKE_CASE   ,						resample=_SCREAMING_SNAKE_CASE ) for image in images]

           if do_rescale:
                 UpperCamelCase					       =  [self.rescale(image=_SCREAMING_SNAKE_CASE   ,						scale=_SCREAMING_SNAKE_CASE ) for image in images]

           if do_normalize:
                 UpperCamelCase					       =  [self.normalize(image=_SCREAMING_SNAKE_CASE   ,						mean=_SCREAMING_SNAKE_CASE   ,						std=_SCREAMING_SNAKE_CASE ) for image in images]

           UpperCamelCase					       =  [to_channel_dimension_format(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) for image in images]

           UpperCamelCase					       =  BatchFeature(data={'pixel_values': images}   ,						tensor_type=_SCREAMING_SNAKE_CASE )

           return encoded_outputs











class 					A__     :

     '''simple docstring'''






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






           """simple docstring"""




           UpperCamelCase					       =  {}

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

           UpperCamelCase					       =  prefix







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  0
           for q, w in zip(self.prefix   ,						_SCREAMING_SNAKE_CASE ):
                 if q != w:
                       break

                 x += 1

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    list[str] ):






           """simple docstring"""




           for word in words:
                 self.insert(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           if self.prefix == word:
                 UpperCamelCase					       =  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:
                 UpperCamelCase					       =  RadixNode(prefix=_SCREAMING_SNAKE_CASE   ,						is_leaf=_SCREAMING_SNAKE_CASE )

           else:
                 UpperCamelCase					       =  self.nodes[word[0]]
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )

                 # 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(_SCREAMING_SNAKE_CASE )

                 # 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:
                       UpperCamelCase					       =  remaining_prefix

                       UpperCamelCase					       =  self.nodes[matching_string[0]]
                       UpperCamelCase					       =  RadixNode(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  aux_node

                       if remaining_word == "":
                             UpperCamelCase					       =  True
                       else:
                             self.nodes[matching_string[0]].insert(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  self.nodes.get(word[0]   ,						_SCREAMING_SNAKE_CASE )
           if not incoming_node:
                 return False
           else:
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )
                 # 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(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




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

                             return True







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    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 		lowercase__  (				) ->			bool:
      """simple docstring"""

      UpperCamelCase					       =  'banana bananas bandana band apple all beast'.split()
      UpperCamelCase					       =  RadixNode()
      root.insert_many(_UpperCamelCase)

      assert all(root.find(_UpperCamelCase) 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__  (				) ->			None:
      """simple docstring"""

      assert test_trie()
def 		lowercase__  (				) ->			None:
      """simple docstring"""

      UpperCamelCase					       =  RadixNode()
      UpperCamelCase					       =  'banana bananas bandanas bandana band apple all beast'.split()
      root.insert_many(_UpperCamelCase)

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


if __name__ == "__main__":
       main()



