
"""simple docstring"""
import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any]=(),						lowerCamelCase__							:							Union[str, Any]=None,						lowerCamelCase__							:							Any="no",						lowerCamelCase__							:							int="29500"				)  ->    Optional[Any]:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = False
  _SCREAMING_SNAKE_CASE   :				Any							    = False
  if any(key.startswith("KAGGLE"				) for key in os.environ.keys()				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = True
  elif "IPython" in sys.modules:
    _SCREAMING_SNAKE_CASE   :				Any							    = "google.colab" in str(sys.modules["IPython"].get_ipython()				)

  try:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = PrecisionType(mixed_precision.lower()				)
  except ValueError:
    raise ValueError(
        f'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''				)

  if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME",						lowerCamelCase__				) is not None):
    # TPU launch
    import torch_xla.distributed.xla_multiprocessing as xmp

    if len(AcceleratorState._shared_state				) > 0:
      raise ValueError(
          "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
          "your training function. Restart your notebook and make sure no cells initializes an "
          "`Accelerator`."				)
    if num_processes is None:
      _SCREAMING_SNAKE_CASE   :				Dict							    = 8

    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = PrepareForLaunch(lowerCamelCase__,						distributed_type="TPU"				)
    print(f'''Launching a training on {num_processes} TPU cores.'''				)
    xmp.spawn(lowerCamelCase__,						args=lowerCamelCase__,						nprocs=lowerCamelCase__,						start_method="fork"				)
  elif in_colab:
    # No need for a distributed launch otherwise as it's either CPU or one GPU.
    if torch.cuda.is_available():
      print("Launching training on one GPU."				)
    else:
      print("Launching training on one CPU."				)
    function(*lowerCamelCase__				)
  else:
    if num_processes is None:
      raise ValueError(
          "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."				)

    if num_processes > 1:
      # Multi-GPU launch
      from torch.multiprocessing import start_processes
      from torch.multiprocessing.spawn import ProcessRaisedException

      if len(AcceleratorState._shared_state				) > 0:
        raise ValueError(
            "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
            "inside your training function. Restart your notebook and make sure no cells initializes an "
            "`Accelerator`."				)

      if torch.cuda.is_initialized():
        raise ValueError(
            "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
            "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
            "function."				)

      # torch.distributed will expect a few environment variable to be here. We set the ones common to each
      # process here (the other ones will be set be the launcher).
      with patch_environment(
          world_size=lowerCamelCase__,						master_addr="127.0.01",						master_port=lowerCamelCase__,						mixed_precision=lowerCamelCase__				):
        _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = PrepareForLaunch(lowerCamelCase__,						distributed_type="MULTI_GPU"				)
        print(f'''Launching training on {num_processes} GPUs.'''				)
        try:
          start_processes(lowerCamelCase__,						args=lowerCamelCase__,						nprocs=lowerCamelCase__,						start_method="fork"				)
        except ProcessRaisedException as e:
          if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
            raise RuntimeError(
                "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                "Please review your imports and test them when running the `notebook_launcher()` to identify "
                "which one is problematic."				) from e

    else:
      # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
      if is_mps_available():
        _SCREAMING_SNAKE_CASE   :				str							    = "1"
        print("Launching training on MPS."				)
      elif torch.cuda.is_available():
        print("Launching training on one GPU."				)
      else:
        print("Launching training on CPU."				)
      function(*lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Any=(),						lowerCamelCase__							:							Union[str, Any]=2				)  ->    List[Any]:
  from torch.multiprocessing import start_processes

  with tempfile.NamedTemporaryFile() as tmp_file:
    # torch.distributed will expect a few environment variable to be here. We set the ones common to each
    # process here (the other ones will be set be the launcher).
    with patch_environment(
        world_size=lowerCamelCase__,						master_addr="127.0.01",						master_port="29500",						accelerate_mixed_precision="no",						accelerate_debug_rdv_file=tmp_file.name,						accelerate_use_cpu="yes",						):
      _SCREAMING_SNAKE_CASE   :				Dict							    = PrepareForLaunch(lowerCamelCase__,						debug=lowerCamelCase__				)
      start_processes(lowerCamelCase__,						args=lowerCamelCase__,						nprocs=lowerCamelCase__,						start_method="fork"				)





"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = value
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None  # Added in order to delete a node easier
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None




     def __repr__(     self				):




       """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 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = root
     def __str__(     self				):




       """simple docstring"""


       return str(self.root				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if new_children is not None:  # reset its kids
         _SCREAMING_SNAKE_CASE   :				Dict							    = node.parent
       if node.parent is not None:  # reset its parent
         if self.is_right(snake_case__				):  # If it is the right children
           _SCREAMING_SNAKE_CASE   :				Any							    = new_children
         else:
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_children
       else:
         _SCREAMING_SNAKE_CASE   :				Any							    = new_children
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if node.parent and node.parent.right:
         return node == node.parent.right
       return False
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.root is None
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Node(snake_case__				)  # create a new Node
       if self.empty():  # if Tree is empty
         _SCREAMING_SNAKE_CASE   :				str							    = new_node  # set its root
       else:  # Tree is not empty
         _SCREAMING_SNAKE_CASE   :				Dict							    = 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:
               _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_node  # We insert the new node in a leaf
               break
             else:
               _SCREAMING_SNAKE_CASE   :				int							    = parent_node.left
           else:
             if parent_node.right is None:
               _SCREAMING_SNAKE_CASE   :				str							    = new_node
               break
             else:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = parent_node.right
         _SCREAMING_SNAKE_CASE   :				Any							    = parent_node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__				):




       """simple docstring"""


       for value in values:
         self.__insert(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.empty():
         raise IndexError("Warning: Tree is empty! please use another."				)
       else:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.root
         # use lazy evaluation here to avoid NoneType Attribute error
         while node is not None and node.value is not value:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = node.left if value < node.value else node.right
         return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None				):




       """simple docstring"""


       if node is None:
         if self.root is None:
           return None
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.root

       if not self.empty():
         while node.right is not None:
           _SCREAMING_SNAKE_CASE   :				Dict							    = node.right
       return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None				):




       """simple docstring"""


       if node is None:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.root
       if self.root is None:
         return None
       if not self.empty():
         _SCREAMING_SNAKE_CASE   :				Any							    = self.root
         while node.left is not None:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = node.left
       return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = 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:
           _SCREAMING_SNAKE_CASE   :				Dict							    = self.get_max(
               node.left				)  # Gets the max value of the left branch
           self.remove(tmp_node.value				)  # type: ignore
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = (
               tmp_node.value  # type: ignore
           )  # Assigns the value to the node to delete and keep tree structure
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=None				):




       """simple docstring"""


       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__				):




       """simple docstring"""


       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__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				list[int]							    = []
       self.inorder(snake_case__  ,     snake_case__				)  # append all values to list using inorder traversal
       return arr[k - 1]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Node | None				)  ->    list[Node]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  if curr_node is not None:
    _SCREAMING_SNAKE_CASE   :				int							    = postorder(curr_node.left				) + postorder(curr_node.right				) + [curr_node]
  return node_list





def    _lowerCAmelCase    (     )  ->    None:
  _SCREAMING_SNAKE_CASE   :				Any							    = (8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = BinarySearchTree()
  for i in testlist:
    t.insert(lowerCamelCase__				)

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

  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(lowerCamelCase__				)
    print(lowerCamelCase__				)


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



