





import os
import sys
import tempfile

import torch

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






def lowerCamelCase__       (				snake_case_      :    Dict			, snake_case_      :    Dict=()			, snake_case_      :    Union[str, Any]=None			, snake_case_      :    List[Any]="no"			, snake_case_      :    str="29500"			) ->							List[Any]:
   __snake_case							=  False
   __snake_case							=  False
   if any(key.startswith('''KAGGLE'''			) for key in os.environ.keys()			):
      __snake_case							=  True
   elif "IPython" in sys.modules:
      __snake_case							=  '''google.colab''' in str(sys.modules['''IPython'''].get_ipython()			)

   try:
      __snake_case							=  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'''			, snake_case_			) 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:
         __snake_case							=  8

      __snake_case							=  PrepareForLaunch(snake_case_			, distributed_type='''TPU'''			)
      print(f"""Launching a training on {num_processes} TPU cores."""			)
      xmp.spawn(snake_case_			, args=snake_case_			, nprocs=snake_case_			, 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(*snake_case_			)
   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=snake_case_			, master_addr='''127.0.01'''			, master_port=snake_case_			, mixed_precision=snake_case_			):
            __snake_case							=  PrepareForLaunch(snake_case_			, distributed_type='''MULTI_GPU'''			)
            print(f"""Launching training on {num_processes} GPUs."""			)
            try:
               start_processes(snake_case_			, args=snake_case_			, nprocs=snake_case_			, 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():
            __snake_case							=  '''1'''
            print('''Launching training on MPS.'''			)
         elif torch.cuda.is_available():
            print('''Launching training on one GPU.'''			)
         else:
            print('''Launching training on CPU.'''			)
         function(*snake_case_			)







def lowerCamelCase__       (				snake_case_      :    Union[str, Any]			, snake_case_      :    int=()			, snake_case_      :    int=2			) ->							int:
   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=snake_case_			, master_addr='''127.0.01'''			, master_port='''29500'''			, accelerate_mixed_precision='''no'''			, accelerate_debug_rdv_file=tmp_file.name			, accelerate_use_cpu='''yes'''			, ):
         __snake_case							=  PrepareForLaunch(snake_case_			, debug=snake_case_			)
         start_processes(snake_case_			, args=snake_case_			, nprocs=snake_case_			, start_method='''fork'''			)








'''simple docstring'''

from __future__ import annotations

import queue
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			data
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None







def    a__							(							)						->						TreeNode:





     """simple docstring"""

     print('''\n********Press N to stop entering at any point of time********\n''' )
     _UpperCamelCase							       =			input('''Enter the value of the root node: ''' ).strip().lower()
     _UpperCamelCase							       =			queue.Queue()
     _UpperCamelCase							       =			TreeNode(int(lowercase ) )
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          _UpperCamelCase							       =			F"""Enter the left node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			left_node
          q.put(lowercase )
          _UpperCamelCase							       =			F"""Enter the right node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			right_node
          q.put(lowercase )
     raise







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     print(node.data,   end=''',''' )
     pre_order(node.left )
     pre_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     in_order(node.left )
     print(node.data,   end=''',''' )
     in_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     post_order(node.left )
     post_order(node.right )
     print(node.data,   end=''',''' )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          print(node_dequeued.data,   end=''',''' )
          if node_dequeued.left:
               q.put(node_dequeued.left )
          if node_dequeued.right:
               q.put(node_dequeued.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			[]
          while not q.empty():
               _UpperCamelCase							       =			q.get()
               print(node_dequeued.data,   end=''',''' )
               if node_dequeued.left:
                    list_.append(node_dequeued.left )
               if node_dequeued.right:
                    list_.append(node_dequeued.right )
          print()
          for node in list_:
               q.put(lowercase )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data,   end=''',''' )
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          # end of while means current node doesn't have left child
          _UpperCamelCase							       =			stack.pop()
          # start to traverse its right child
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          _UpperCamelCase							       =			stack.pop()
          print(n.data,   end=''',''' )
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase       ,							_UpperCamelCase							       =			[], []
     _UpperCamelCase							       =			node
     stacka.append(lowercase )
     while stacka:  # to find the reversed order of post order, store it in stack2
          _UpperCamelCase							       =			stacka.pop()
          if n.left:
               stacka.append(n.left )
          if n.right:
               stacka.append(n.right )
          stacka.append(lowercase )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data,   end=''',''' )







def    a__							(							lowercase   :	str = "",   lowercase   :	List[str]=50,   lowercase   :	List[str]="*" )						->						str:





     """simple docstring"""

     if not s:
          return "\n" + width * char
     _UpperCamelCase       ,							_UpperCamelCase							       =			divmod(width - len(lowercase ) - 2,   2 )
     return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(prompt('Binary Tree Traversals'))

   lowercase__		:     TreeNode			=							build_tree()
   print(prompt('Pre Order Traversal'))
   pre_order(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal'))
   in_order(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal'))
   post_order(node)
   print(prompt() + '\n')

   print(prompt('Level Order Traversal'))
   level_order(node)
   print(prompt() + '\n')

   print(prompt('Actual Level Order Traversal'))
   level_order_actual(node)
   print('*' * 50 + '\n')

   print(prompt('Pre Order Traversal - Iteration Version'))
   pre_order_iter(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal - Iteration Version'))
   in_order_iter(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal - Iteration Version'))
   post_order_iter(node)
   print(prompt())



