
"""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       A_  (				__lowercase						,	__lowercase=()						,	__lowercase=None						,	__lowercase="no"						,	__lowercase="29500"	):
 UpperCamelCase_     :					Dict   	=False
 UpperCamelCase_     :					int   	=False
 if any(key.startswith('KAGGLE'	) for key in os.environ.keys()	):
  UpperCamelCase_     :					Optional[int]   	=True
 elif "IPython" in sys.modules:
  UpperCamelCase_     :					List[str]   	='google.colab' in str(sys.modules['IPython'].get_ipython()	)

 try:
  UpperCamelCase_     :					str   	=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'						,	__lowercase	) 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:
   UpperCamelCase_     :					Any   	=8

  UpperCamelCase_     :					List[str]   	=PrepareForLaunch(__lowercase						,	distributed_type='TPU'	)
  print(F'''Launching a training on {num_processes} TPU cores.'''	)
  xmp.spawn(__lowercase						,	args=__lowercase						,	nprocs=__lowercase						,	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(*__lowercase	)
 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=__lowercase						,	master_addr='127.0.01'						,	master_port=__lowercase						,	mixed_precision=__lowercase	):
    UpperCamelCase_     :					Any   	=PrepareForLaunch(__lowercase						,	distributed_type='MULTI_GPU'	)
    print(F'''Launching training on {num_processes} GPUs.'''	)
    try:
     start_processes(__lowercase						,	args=__lowercase						,	nprocs=__lowercase						,	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():
    UpperCamelCase_     :					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(*__lowercase	)





def       A_  (				__lowercase						,	__lowercase=()						,	__lowercase=2	):
 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=__lowercase						,	master_addr='127.0.01'						,	master_port='29500'						,	accelerate_mixed_precision='no'						,	accelerate_debug_rdv_file=tmp_file.name						,	accelerate_use_cpu='yes'						,	):
   UpperCamelCase_     :					Optional[Any]   	=PrepareForLaunch(__lowercase						,	debug=__lowercase	)
   start_processes(__lowercase						,	args=__lowercase						,	nprocs=__lowercase						,	start_method='fork'	)


"""simple docstring"""






from collections.abc import Callable





class 				a__					:



     def __init__(							self :Tuple      ,  _lowerCamelCase :Callable | None = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					list   	=[]
      # Stores indexes of each item for supporting updates and deletion.
      UpperCamelCase_     :					dict   	={}
      # Stores current size of heap.
      UpperCamelCase_     :					Any   	=0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      UpperCamelCase_     :					List[str]   	=key or (lambda _lowerCamelCase   : x)



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      return int((i - 1) / 2   ) if i > 0 else None



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=int(2 * i + 1   )
      return left if 0 < left < self.size else None



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=int(2 * i + 2   )
      return right if 0 < right < self.size else None



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Optional[int]   	=(
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      UpperCamelCase_	,     UpperCamelCase_     :					Union[str, Any]   	=self.arr[j], self.arr[i]



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      return self.arr[i][1] < self.arr[j][1]



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self._left(_lowerCamelCase   )
      UpperCamelCase_     :					List[Any]   	=self._right(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=i

      if left is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       UpperCamelCase_     :					Optional[int]   	=left
      if right is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       UpperCamelCase_     :					List[Any]   	=right

      return valid_parent



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=self._parent(_lowerCamelCase   )
      while parent is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       self._swap(_lowerCamelCase      ,  _lowerCamelCase   )
       UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=parent, self._parent(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=self._get_valid_parent(_lowerCamelCase   )
      while valid_parent != index:
       self._swap(_lowerCamelCase      ,  _lowerCamelCase   )
       UpperCamelCase_	,     UpperCamelCase_     :					int   	=valid_parent, self._get_valid_parent(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if item not in self.pos_map:
       return
      UpperCamelCase_     :					List[Any]   	=self.pos_map[item]
      UpperCamelCase_     :					int   	=[item, self.key(_lowerCamelCase   )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_lowerCamelCase   )
      self._heapify_down(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if item not in self.pos_map:
       return
      UpperCamelCase_     :					Any   	=self.pos_map[item]
      del self.pos_map[item]
      UpperCamelCase_     :					Dict   	=self.arr[self.size - 1]
      UpperCamelCase_     :					Optional[int]   	=index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
       self._heapify_up(_lowerCamelCase   )
       self._heapify_down(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=len(self.arr   )
      if arr_len == self.size:
       self.arr.append([item, self.key(_lowerCamelCase   )]   )
      else:
       UpperCamelCase_     :					str   	=[item, self.key(_lowerCamelCase   )]
      UpperCamelCase_     :					Optional[int]   	=self.size
      self.size += 1
      self._heapify_up(self.size - 1   )



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      return self.arr[0] if self.size else None




     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self.get_top()
      if top_item_tuple:
       self.delete_item(top_item_tuple[0]   )
      return top_item_tuple




def       A_  (				):
 pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()
