

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,
    get_resize_output_image_size,
    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, logging


_UpperCamelCase     			=     logging.get_logger(__name__)



class        lowercase		(			A_   ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE   				=							["pixel_values"]




 def __init__(self			,			__a = True			,			__a = None			,			__a = PILImageResampling.BILINEAR			,			__a = True			,			__a = None			,			__a = True			,			__a = 1 / 255			,			__a = True			,			__a = None			,			__a = None			,			**__a			,			)					->   None:






  """simple docstring"""





  super().__init__(**snake_case__					)
  UpperCAmelCase__     							=							size if size is not None else {"shortest_edge": 256}
  UpperCAmelCase__     							=							get_size_dict(snake_case__			,			default_to_square=snake_case__					)
  UpperCAmelCase__     							=							crop_size if crop_size is not None else {"height": 224, "width": 224}
  UpperCAmelCase__     							=							get_size_dict(snake_case__					)
  UpperCAmelCase__     							=							do_resize
  UpperCAmelCase__     							=							size
  UpperCAmelCase__     							=							resample
  UpperCAmelCase__     							=							do_center_crop
  UpperCAmelCase__     							=							crop_size
  UpperCAmelCase__     							=							do_rescale
  UpperCAmelCase__     							=							rescale_factor
  UpperCAmelCase__     							=							do_normalize
  UpperCAmelCase__     							=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  UpperCAmelCase__     							=							image_std if image_std is not None else IMAGENET_STANDARD_STD




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a = PILImageResampling.BICUBIC			,			__a = None			,			**__a			,			)					->   np.ndarray:






  """simple docstring"""





  UpperCAmelCase__     							=							get_size_dict(snake_case__			,			default_to_square=snake_case__					)
  if "shortest_edge" not in size:
   raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"					)
  UpperCAmelCase__     							=							get_resize_output_image_size(snake_case__			,			size=size['shortest_edge']			,			default_to_square=snake_case__					)
  return resize(snake_case__			,			size=snake_case__			,			resample=snake_case__			,			data_format=snake_case__			,			**snake_case__					)




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a = None			,			**__a			,			)					->   np.ndarray:






  """simple docstring"""





  UpperCAmelCase__     							=							get_size_dict(snake_case__					)
  return center_crop(snake_case__			,			size=(size['height'], size['width'])			,			data_format=snake_case__			,			**snake_case__					)




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a = None			,			**__a					)					->   np.ndarray:






  """simple docstring"""





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




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a			,			__a = None			,			**__a			,			)					->   np.ndarray:






  """simple docstring"""





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




 def     UpperCamelCase__				(self			,			__a			,			__a = None			,			__a = None			,			__a = None			,			__a = None			,			__a = None			,			__a = None			,			__a = None			,			__a = None			,			__a = None			,			__a = None			,			__a = None			,			__a = ChannelDimension.FIRST			,			**__a			,			)					->   str:






  """simple docstring"""





  UpperCAmelCase__     							=							do_resize if do_resize is not None else self.do_resize
  UpperCAmelCase__     							=							size if size is not None else self.size
  UpperCAmelCase__     							=							get_size_dict(snake_case__			,			default_to_square=snake_case__					)
  UpperCAmelCase__     							=							resample if resample is not None else self.resample
  UpperCAmelCase__     							=							do_center_crop if do_center_crop is not None else self.do_center_crop
  UpperCAmelCase__     							=							crop_size if crop_size is not None else self.crop_size
  UpperCAmelCase__     							=							get_size_dict(snake_case__					)
  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__     							=							make_list_of_images(snake_case__					)

  if not valid_images(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:
   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.
  UpperCAmelCase__     							=							[to_numpy_array(snake_case__					) for image in images]

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

  if do_center_crop:
   UpperCAmelCase__     							=							[self.center_crop(image=snake_case__			,			size=snake_case__					) for image in images]

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

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

  UpperCAmelCase__     							=							[to_channel_dimension_format(snake_case__			,			snake_case__					) for image in images]

  UpperCAmelCase__     							=							{"pixel_values": images}
  return BatchFeature(data=snake_case__			,			tensor_type=snake_case__					)





class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   str = ""       ,		snake_case__						:   bool = False					)						->      None:


          '''simple docstring'''


          snake_case			:  dict[str, RadixNode]     							= {}

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

          snake_case			:  Dict     							= prefix



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str					)						->      tuple[str, str, str]:


          '''simple docstring'''


          snake_case			:  Any     							= 0
          for q, w in zip(self.prefix       ,		snake_case__					):
                 if q != w:
                        break

                 x += 1

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   list[str]					)						->      None:


          '''simple docstring'''


          for word in words:
                 self.insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   str					)						->      None:


          '''simple docstring'''


          if self.prefix == word:
                 snake_case			:  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:
                 snake_case			:  Optional[int]     							= RadixNode(prefix=snake_case__       ,		is_leaf=snake_case__					)

          else:
                 snake_case			:  Dict     							= self.nodes[word[0]]
                 snake_case							,   snake_case							,   snake_case			:  int     							= incoming_node.match(
                     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(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:
                        snake_case			:  Tuple     							= remaining_prefix

                        snake_case			:  Dict     							= self.nodes[matching_string[0]]
                        snake_case			:  Tuple     							= RadixNode(snake_case__       ,		snake_case__					)
                        snake_case			:  List[str]     							= aux_node

                        if remaining_word == "":
                               snake_case			:  Tuple     							= True
                        else:
                               self.nodes[matching_string[0]].insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[int]     							= incoming_node.match(
                     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(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[Any]     							= incoming_node.match(
                     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(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:
                                             snake_case			:  List[Any]     							= list(self.nodes.values()					)[0]
                                             snake_case			:  Optional[int]     							= merging_node.is_leaf
                                             self.prefix += merging_node.prefix
                                             snake_case			:  str     							= 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			:  List[Any]     							= False
                               # If there is 1 edge, we merge it with its child
                               else:
                                      snake_case			:  Any     							= list(incoming_node.nodes.values()					)[0]
                                      snake_case			:  Any     							= merging_node.is_leaf
                                      incoming_node.prefix += merging_node.prefix
                                      snake_case			:  Union[str, Any]     							= merging_node.nodes

                               return True



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   int = 0					)						->      None:


          '''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      UpperCamelCase   (       ):
       snake_case			:  Dict     							= "banana bananas bandana band apple all beast".split()
       snake_case			:  Tuple     							= RadixNode()
       root.insert_many(__lowerCamelCase       )

       assert all(root.find(__lowerCamelCase       ) 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      UpperCamelCase   (       ):
       assert test_trie()


def      UpperCamelCase   (       ):
       snake_case			:  Union[str, Any]     							= RadixNode()
       snake_case			:  List[str]     							= "banana bananas bandanas bandana band apple all beast".split()
       root.insert_many(__lowerCamelCase       )

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


if __name__ == "__main__":
   main()





