






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

  '''simple docstring'''







  def __init__(      self		:			int       ,				lowerCamelCase		:			int | None = None							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    value
         _UpperCAmelCase							=    None  # Added in order to delete a node easier
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None





  def __repr__(      self		:			Any							)						->	str:


         """simple docstring"""
         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 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			Node | None = None							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    root


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


         """simple docstring"""
         return str(self.root							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			Node       ,				lowerCamelCase		:			Node | None							)						->	None:


         """simple docstring"""
         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(lowerCamelCase							):  # If it is the right children
                       _UpperCAmelCase							=    new_children
                else:
                       _UpperCAmelCase							=    new_children
         else:
                _UpperCAmelCase							=    new_children


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Node							)						->	bool:


         """simple docstring"""
         if node.parent and node.parent.right:
                return node == node.parent.right
         return False


  def  lowerCamelCase       (      self		:			int							)						->	bool:


         """simple docstring"""
         return self.root is None


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			int							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    Node(lowerCamelCase							)  # 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  lowerCamelCase       (      self		:			int       ,				*lowerCamelCase		:			List[str]							)						->	None:


         """simple docstring"""
         for value in values:
                self.__insert(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			Optional[Any]							)						->	Node | None:


         """simple docstring"""
         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  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			Node | None = None							)						->	Node | None:


         """simple docstring"""
         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  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			Node | None = None							)						->	Node | None:


         """simple docstring"""
         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  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			int							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    self.search(lowerCamelCase							)  # 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(lowerCamelCase       ,				lowerCamelCase							)
                elif node.left is None:  # Has only right children
                       self.__reassign_nodes(lowerCamelCase       ,				node.right							)
                elif node.right is None:  # Has only left children
                       self.__reassign_nodes(lowerCamelCase       ,				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  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Node | None							)						->	Iterable:


         """simple docstring"""
         if node is not None:
                yield node  # Preorder Traversal
                yield from self.preorder_traverse(node.left							)
                yield from self.preorder_traverse(node.right							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Any=None							)						->	Any:


         """simple docstring"""
         if traversal_function is None:
                return self.preorder_traverse(self.root							)
         else:
                return traversal_function(self.root							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			list       ,				lowerCamelCase		:			Node | None							)						->	None:


         """simple docstring"""
         if node:
                self.inorder(lowerCamelCase       ,				node.left							)
                arr.append(node.value							)
                self.inorder(lowerCamelCase       ,				node.right							)





  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Node							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    []
         self.inorder(lowerCamelCase       ,				lowerCamelCase							)  # append all values to list using inorder traversal
         return arr[k - 1]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						list[Node]:
       _UpperCAmelCase							=    []
       if curr_node is not None:
              _UpperCAmelCase							=    postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
       return node_list




def       _SCREAMING_SNAKE_CASE (							)				->						None:
       _UpperCAmelCase							=    (8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
       _UpperCAmelCase							=    BinarySearchTree()
       for i in testlist:
              t.insert(__snake_case )

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


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Any							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Any=3							,							_lowerCAmelCase    :						Tuple=64							,							_lowerCAmelCase    :						List[str]=None		):
       SCREAMING_SNAKE_CASE_      		=     np.random.default_rng(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     length
       SCREAMING_SNAKE_CASE_      		=     rng.normal(size=(length,)		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     a * self.x + b + rng.normal(scale=0.1							,							size=(length,)		).astype(np.floataa		)



 def __len__(				self    :						Optional[int]		):
       return self.length




 def __getitem__(				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]		):
       return {"x": self.x[i], "y": self.y[i]}





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=0							,							_lowerCAmelCase    :						List[str]=0							,							_lowerCAmelCase    :						str=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a[0] + self.b[0]





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Optional[Any]=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[int]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a + self.b


def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      int = 16				)   ->					Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

      SCREAMING_SNAKE_CASE_      		=     AutoTokenizer.from_pretrained('bert-base-cased'				)
      SCREAMING_SNAKE_CASE_      		=     {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
      SCREAMING_SNAKE_CASE_      		=     load_dataset('csv'     ,     data_files=__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     datasets['train'].unique('label'				)

      SCREAMING_SNAKE_CASE_      		=     {v: i for i, v in enumerate(__UpperCAmelCase				)}

      def tokenize_function(__UpperCAmelCase		:      Optional[int]				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_      		=     tokenizer(
                examples['sentence1']     ,     examples['sentence2']     ,     truncation=__UpperCAmelCase     ,     max_length=__UpperCAmelCase     ,     padding='max_length'				)
            if "label" in examples:
                  SCREAMING_SNAKE_CASE_      		=     [label_to_id[l] for l in examples['label']]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      SCREAMING_SNAKE_CASE_      		=     datasets.map(
          __UpperCAmelCase     ,     batched=__UpperCAmelCase     ,     remove_columns=['sentence1', 'sentence2', 'label']     ,     )

      def collate_fn(__UpperCAmelCase		:      Dict				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(__UpperCAmelCase     ,     padding='max_length'     ,     max_length=1_28     ,     return_tensors='pt'				)
            return tokenizer.pad(__UpperCAmelCase     ,     padding='longest'     ,     return_tensors='pt'				)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['train']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=2				)
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['validation']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=1				)

      return train_dataloader, eval_dataloader