


class 					SCREAMING_SNAKE_CASE__    :
      def __init__(			self							:			Union[str, Any]	)						->     None:
             a_   :						Optional[Any]				=    {}  # Mapping from char to TrieNode
             a_   :						Dict				=    False
      def 			SCREAMING_SNAKE_CASE				(			self							:			Dict	,				SCREAMING_SNAKE_CASE__							:			int	)						->     None:



             for word in words:
                    self.insert(SCREAMING_SNAKE_CASE__	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			int	,				SCREAMING_SNAKE_CASE__							:			Tuple	)						->     None:
             a_   :						Optional[Any]				=    self
             for char in word:
                    if char not in curr.nodes:
                           a_   :						Dict				=    TrieNode()
                    a_   :						List[Any]				=    curr.nodes[char]
             a_   :						Tuple				=    True
      def 			SCREAMING_SNAKE_CASE				(			self							:			str	,				SCREAMING_SNAKE_CASE__							:			Dict	)						->     bool:
             a_   :						Dict				=    self
             for char in word:
                    if char not in curr.nodes:
                           return False
                    a_   :						str				=    curr.nodes[char]
             return curr.is_leaf

      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[int]	,				SCREAMING_SNAKE_CASE__							:			Optional[Any]	)						->     None:



             def _delete(SCREAMING_SNAKE_CASE__							:			Union[str, Any]	,				SCREAMING_SNAKE_CASE__							:			List[str]	,				SCREAMING_SNAKE_CASE__							:			Any	) -> bool:
                    if index == len(SCREAMING_SNAKE_CASE__	):
                           # If word does not exist
                           if not curr.is_leaf:
                                  return False
                           a_   :						List[str]				=    False
                           return len(curr.nodes	) == 0
                    a_   :						Optional[Any]				=    word[index]
                    a_   :						Union[str, Any]				=    curr.nodes.get(SCREAMING_SNAKE_CASE__	)
                    # If char not in current trie node
                    if not char_node:
                           return False
                    # Flag to check if node can be deleted
                    a_   :						List[Any]				=    _delete(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	,				index + 1	)
                    if delete_curr:
                           del curr.nodes[char]
                           return len(curr.nodes	) == 0
                    return delete_curr

             _delete(self	,				SCREAMING_SNAKE_CASE__	,				0	)







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

       """simple docstring"""

       if node.is_leaf:
              print(__A    ,       end=' '							)

       for key, value in node.nodes.items():
              print_words(__A    ,       word + key							)







def 			SCREAMING_SNAKE_CASE_		(     )      ->						int:

       """simple docstring"""

       a_   :						List[Any]				=    'banana bananas bandana band apple all beast'.split()
       a_   :						Any				=    TrieNode()
       root.insert_many(__A							)
       # print_words(root, "")
       assert all(root.find(__A							) for word in words							)
       assert root.find('banana'							)
       assert not root.find('bandanas'							)
       assert not root.find('apps'							)
       assert root.find('apple'							)
       assert root.find('all'							)
       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_		(     __A					:     Optional[int]    ,       __A					:     Union[str, Any]							)      ->						int:

       """simple docstring"""

       print(str(__A							)    ,       'works!' if passes else 'doesn\'t work :('							)







def 			SCREAMING_SNAKE_CASE_		(     )      ->						Optional[Any]:

       """simple docstring"""

       assert test_trie()







def 			SCREAMING_SNAKE_CASE_		(     )      ->						Optional[Any]:

       """simple docstring"""

       print_results('Testing trie functionality'    ,       test_trie()							)


if __name__ == "__main__":
   main()







'''simple docstring'''





import unittest

import numpy as np

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

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 DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



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


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



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


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



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


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



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

      # Test not batched input
      _a           =    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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



