






from collections.abc import Iterable
from typing import Any
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ = None      )		->       Tuple:
       UpperCamelCase__         =    value
       UpperCamelCase__         =    None  # Added in order to delete a node easier
       UpperCamelCase__         =    None
       UpperCamelCase__         =    None







   def __repr__(					self      )		->       str:
       from pprint import pformat

       if self.left is None and self.right is None:
           return str(self.value      )
       return pformat({F'{self.value}': (self.left, self.right)} ,				indent=1      )







class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ = None      )		->       List[str]:
       UpperCamelCase__         =    root

   def __str__(					self      )		->       str:
       return str(self.root      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       if new_children is not None:  # reset its kids
           UpperCamelCase__         =    node.parent
       if node.parent is not None:  # reset its parent
           if self.is_right(snake_case_      ):  # If it is the right children
               UpperCamelCase__         =    new_children
           else:
               UpperCamelCase__         =    new_children
       else:
           UpperCamelCase__         =    new_children

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       bool:
       if node.parent and node.parent.right:
           return node == node.parent.right
       return False

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       bool:
       return self.root is None

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    Node(snake_case_      )  # create a new Node
       if self.empty():  # if Tree is empty
           UpperCamelCase__         =    new_node  # set its root
       else:  # Tree is not empty
           UpperCamelCase__         =    self.root  # from root
           if parent_node is None:
               return
           while True:  # While we don't get to a leaf
               if value < parent_node.value:  # We go left
                   if parent_node.left is None:
                       UpperCamelCase__         =    new_node  # We insert the new node in a leaf
                       break
                   else:
                       UpperCamelCase__         =    parent_node.left
               else:
                   if parent_node.right is None:
                       UpperCamelCase__         =    new_node
                       break
                   else:
                       UpperCamelCase__         =    parent_node.right
           UpperCamelCase__         =    parent_node

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_      )		->       None:
       for value in values:
           self.__insert(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Node | None:
       if self.empty():
           raise IndexError('Warning: Tree is empty! please use another.'      )
       else:
           UpperCamelCase__         =    self.root
           # use lazy evaluation here to avoid NoneType Attribute error
           while node is not None and node.value is not value:
               UpperCamelCase__         =    node.left if value < node.value else node.right
           return node

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ = None      )		->       Node | None:
       if node is None:
           if self.root is None:
               return None
           UpperCamelCase__         =    self.root

       if not self.empty():
           while node.right is not None:
               UpperCamelCase__         =    node.right
       return node

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ = None      )		->       Node | None:
       if node is None:
           UpperCamelCase__         =    self.root
       if self.root is None:
           return None
       if not self.empty():
           UpperCamelCase__         =    self.root
           while node.left is not None:
               UpperCamelCase__         =    node.left
       return node

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    self.search(snake_case_      )  # Look for the node with that label
       if node is not None:
           if node.left is None and node.right is None:  # If it has no children
               self.__reassign_nodes(snake_case_ ,				snake_case_      )
           elif node.left is None:  # Has only right children
               self.__reassign_nodes(snake_case_ ,				node.right      )
           elif node.right is None:  # Has only left children
               self.__reassign_nodes(snake_case_ ,				node.left      )
           else:
               UpperCamelCase__         =    self.get_max(
                   node.left      )  # Gets the max value of the left branch
               self.remove(tmp_node.value      )  # type: ignore
               UpperCamelCase__         =    (
                   tmp_node.value  # type: ignore
               )  # Assigns the value to the node to delete and keep tree structure

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Iterable:
       if node is not None:
           yield node  # Preorder Traversal
           yield from self.preorder_traverse(node.left      )
           yield from self.preorder_traverse(node.right      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=None      )		->       Any:
       if traversal_function is None:
           return self.preorder_traverse(self.root      )
       else:
           return traversal_function(self.root      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       if node:
           self.inorder(snake_case_ ,				node.left      )
           arr.append(node.value      )
           self.inorder(snake_case_ ,				node.right      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       UpperCamelCase__         =    []
       self.inorder(snake_case_ ,				snake_case_      )  # append all values to list using inorder traversal
       return arr[k - 1]







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	list[Node]:
    """simple docstring"""

    UpperCamelCase__         =    []
    if curr_node is not None:
        UpperCamelCase__         =    postorder(curr_node.left					) + postorder(curr_node.right					) + [curr_node]
    return node_list







def   lowerCAmelCase_( )					->	None:
    """simple docstring"""

    UpperCamelCase__         =    (8, 3, 6, 1, 10, 14, 13, 4, 7)
    UpperCamelCase__         =    BinarySearchTree()
    for i in testlist:
        t.insert(SCREAMING_SNAKE_CASE					)

    # Prints all the elements of the list in order traversal
    print(SCREAMING_SNAKE_CASE					)

    if t.search(6					) is not None:
        print('The value 6 exists'					)
    else:
        print('The value 6 doesn\'t exist'					)

    if t.search(-1					) is not None:
        print('The value -1 exists'					)
    else:
        print('The value -1 doesn\'t exist'					)

    if not t.empty():
        print('Max Value: ' ,	t.get_max().value					)  # type: ignore
        print('Min Value: ' ,	t.get_min().value					)  # type: ignore

    for i in testlist:
        t.remove(SCREAMING_SNAKE_CASE					)
        print(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)












"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
  import jax

  from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor
class    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=100 ,				snake_case_=13 ,				snake_case_=30 ,				snake_case_=2 ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=10 ,				snake_case_=0.02 ,				snake_case_=3 ,				)		->       Optional[int]:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    vocab_size
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    is_training
       UpperCamelCase__         =    use_labels
       UpperCamelCase__         =    hidden_size
       UpperCamelCase__         =    num_hidden_layers
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    intermediate_size
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    type_sequence_label_size
       UpperCamelCase__         =    initializer_range

       # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__         =    (image_size // patch_size) ** 2
       UpperCamelCase__         =    num_patches + 1

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       UpperCamelCase__         =    None
       if self.use_labels:
           UpperCamelCase__         =    ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )

       UpperCamelCase__         =    BeitConfig(
           vocab_size=self.vocab_size ,				image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				is_decoder=snake_case_ ,				initializer_range=self.initializer_range ,				)

       return config, pixel_values, labels

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    FlaxBeitModel(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    FlaxBeitForMaskedImageModeling(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length - 1, self.vocab_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    self.type_sequence_label_size
       UpperCamelCase__         =    FlaxBeitForImageClassification(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       UpperCamelCase__         =    1
       UpperCamelCase__         =    FlaxBeitForImageClassification(snake_case_      )

       UpperCamelCase__         =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       UpperCamelCase__         =    model(snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )       ,						(
           UpperCamelCase__
       )       ,						(
           UpperCamelCase__
       )       ,						
       )         =    config_and_inputs
       UpperCamelCase__         =    {'pixel_values': pixel_values}
       return config, inputs_dict







@require_flax
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				int 						=(
       (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
   )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       None:
       UpperCamelCase__         =    FlaxBeitModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=snake_case_ ,				has_text_modality=snake_case_ ,				hidden_size=37      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       self.config_tester.run_common_tests()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(snake_case_      )
           UpperCamelCase__         =    inspect.signature(model.__call__      )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase__         =    [*signature.parameters.keys()]

           UpperCamelCase__         =    ['pixel_values']
           self.assertListEqual(arg_names[:1] ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__      ):
               UpperCamelCase__         =    self._prepare_for_class(snake_case_ ,				snake_case_      )
               UpperCamelCase__         =    model_class(snake_case_      )

               @jax.jit
               def model_jitted(snake_case_ ,				**snake_case_      ):
                   return model(pixel_values=snake_case_ ,				**snake_case_      )

               with self.subTest('JIT Enabled'      ):
                   UpperCamelCase__         =    model_jitted(**snake_case_      ).to_tuple()

               with self.subTest('JIT Disabled'      ):
                   with jax.disable_jit():
                       UpperCamelCase__         =    model_jitted(**snake_case_      ).to_tuple()

               self.assertEqual(len(snake_case_      ) ,				len(snake_case_      )      )
               for jitted_output, output in zip(snake_case_ ,				snake_case_      ):
                   self.assertEqual(jitted_output.shape ,				output.shape      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__         =    model_class_name.from_pretrained('microsoft/beit-base-patch16-224'      )
           UpperCamelCase__         =    model(np.ones((1, 3, 224, 224)      )      )
           self.assertIsNotNone(snake_case_      )







def   lowerCAmelCase_( )					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image





@require_vision
@require_flax
class    __lowerCamelCase    (  unittest.TestCase  ):

   @cached_property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'      ) if is_vision_available() else None

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      ).pixel_values

       # prepare bool_masked_pos
       UpperCamelCase__         =    np.ones((1, 196) ,				dtype=snake_case_      )

       # forward pass
       UpperCamelCase__         =    model(pixel_values=snake_case_ ,				bool_masked_pos=snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 196, 8192)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array(
           [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]      )

       self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] ,				snake_case_ ,				atol=1E-2      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      )

       # forward pass
       UpperCamelCase__         =    model(**snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 1000)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array([-1.2_385, -1.0_987, -1.0_108]      )

       self.assertTrue(np.allclose(logits[0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       UpperCamelCase__         =    281
       self.assertEqual(logits.argmax(-1      ).item() ,				snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      )

       # forward pass
       UpperCamelCase__         =    model(**snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 2_1841)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array([1.6_881, -0.2_787, 0.5_901]      )

       self.assertTrue(np.allclose(logits[0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       UpperCamelCase__         =    2396
       self.assertEqual(logits.argmax(-1      ).item() ,				snake_case_      )




