





class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   Any       , UpperCAmelCase_				:   str = ""       , UpperCAmelCase_				:   bool = False)       ->None:



   '''simple docstring'''



   lowerCamelCase__:      dict[str, RadixNode]			  ={}

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

   lowerCamelCase__:      List[str]			  =prefix

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   str)       ->tuple[str, str, str]:



   '''simple docstring'''



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

     x += 1

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   list[str])       ->None:



   '''simple docstring'''



   for word in words:
     self.insert(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   str)       ->None:



   '''simple docstring'''



   if self.prefix == word:
     lowerCamelCase__:      Optional[int]			  =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__:      Any			  =RadixNode(prefix=UpperCAmelCase_       , is_leaf=UpperCAmelCase_)

   else:
     lowerCamelCase__:      Union[str, Any]			  =self.nodes[word[0]]
     lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      Tuple			  =incoming_node.match(
         UpperCAmelCase_)

     # 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(UpperCAmelCase_)

     # 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__:      Optional[int]			  =self.nodes[matching_string[0]]
       lowerCamelCase__:      Dict			  =RadixNode(UpperCAmelCase_       , UpperCAmelCase_)
       lowerCamelCase__:      Tuple			  =aux_node

       if remaining_word == "":
         lowerCamelCase__:      Dict			  =True
       else:
         self.nodes[matching_string[0]].insert(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   str)       ->bool:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.nodes.get(word[0]       , UpperCAmelCase_)
   if not incoming_node:
     return False
   else:
     lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      str			  =incoming_node.match(
         UpperCAmelCase_)
     # 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(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   str)       ->bool:



   '''simple docstring'''



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

         return True







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   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      lowerCAmelCase_ (       )     ->		bool:







  """simple docstring"""





  lowerCamelCase__:      str			  ="banana bananas bandana band apple all beast".split()
  lowerCamelCase__:      List[Any]			  =RadixNode()
  root.insert_many(__a		)

  assert all(root.find(__a		) 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      lowerCAmelCase_ (       )     ->		None:







  """simple docstring"""





  assert test_trie()





def      lowerCAmelCase_ (       )     ->		None:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =RadixNode()
  lowerCamelCase__:      Optional[int]			  ="banana bananas bandanas bandana band apple all beast".split()
  root.insert_many(__a		)

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


if __name__ == "__main__":
     main()








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor



class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 def __init__(self				:   List[Any]       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   Dict=7       , UpperCAmelCase_				:   List[str]=3       , UpperCAmelCase_				:   str=30       , UpperCAmelCase_				:   List[str]=400       , UpperCAmelCase_				:   Optional[int]=True       , UpperCAmelCase_				:   Optional[int]=None       , UpperCAmelCase_				:   Tuple=0.9       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   int=True       , UpperCAmelCase_				:   Union[str, Any]=[0.5, 0.5, 0.5]       , UpperCAmelCase_				:   Optional[Any]=[0.5, 0.5, 0.5]       , )       ->str:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =size if size is not None else {"shortest_edge": 30}
   lowerCamelCase__:      Dict			  =crop_size if crop_size is not None else {"height": 30, "width": 30}
   lowerCamelCase__:      Any			  =parent
   lowerCamelCase__:      Any			  =batch_size
   lowerCamelCase__:      Optional[Any]			  =num_channels
   lowerCamelCase__:      Tuple			  =min_resolution
   lowerCamelCase__:      Union[str, Any]			  =max_resolution
   lowerCamelCase__:      Union[str, Any]			  =do_resize_and_center_crop
   lowerCamelCase__:      Optional[int]			  =size
   lowerCamelCase__:      str			  =crop_pct
   lowerCamelCase__:      Any			  =crop_size
   lowerCamelCase__:      List[str]			  =do_normalize
   lowerCamelCase__:      List[str]			  =image_mean
   lowerCamelCase__:      Tuple			  =image_std







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[int]:



   '''simple docstring'''



   return {
       "size": self.size,
       "do_resize_and_center_crop": self.do_resize_and_center_crop,
       "crop_pct": self.crop_pct,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }



@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			PoolFormerImageProcessor if is_vision_available() else None

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =PoolFormerImageProcessingTester(self)

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->int:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_resize_and_center_crop"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "size"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "crop_pct"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_normalize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_mean"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_std"))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.image_processing_class.from_dict(self.image_processor_dict)
   self.assertEqual(image_processor.size       , {"shortest_edge": 30})
   self.assertEqual(image_processor.crop_size       , {"height": 30, "width": 30})

   lowerCamelCase__:      Union[str, Any]			  =self.image_processing_class.from_dict(self.image_processor_dict       , size=42       , crop_size=84)
   self.assertEqual(image_processor.size       , {"shortest_edge": 42})
   self.assertEqual(image_processor.crop_size       , {"height": 84, "width": 84})

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[Any]:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowerCamelCase__:      Union[str, Any]			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , Image.Image)

   # Test not batched input
   lowerCamelCase__:      Dict			  =image_processing(image_inputs[0]       , return_tensors="pt").pixel_values

   self.assertEqual(
       encoded_images.shape       , (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

   # Test batched
   lowerCamelCase__:      int			  =image_processing(UpperCAmelCase_       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowerCamelCase__:      Tuple			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , numpify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , np.ndarray)

   # Test not batched input
   lowerCamelCase__:      Union[str, Any]			  =image_processing(image_inputs[0]       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

   # Test batched
   lowerCamelCase__:      List[str]			  =image_processing(UpperCAmelCase_       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )







 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowerCamelCase__:      Any			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , torchify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , torch.Tensor)

   # Test not batched input
   lowerCamelCase__:      Any			  =image_processing(image_inputs[0]       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

   # Test batched
   lowerCamelCase__:      str			  =image_processing(UpperCAmelCase_       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

