


"""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			,		_lowerCamelCase=()			,		_lowerCamelCase=None			,		_lowerCamelCase="no"			,		_lowerCamelCase="29500"       )    ->     str:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[Any]							=     False
   _lowerCamelCase   :				Dict							=     False
   if any(key.startswith("KAGGLE"       ) for key in os.environ.keys()       ):
      _lowerCamelCase   :				Optional[int]							=     True
   elif "IPython" in sys.modules:
      _lowerCamelCase   :				Tuple							=     "google.colab" in str(sys.modules["IPython"].get_ipython()       )

   try:
      _lowerCamelCase   :				Optional[int]							=     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:
         _lowerCamelCase   :				List[str]							=     8

      _lowerCamelCase   :				List[str]							=     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       ):
            _lowerCamelCase   :				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():
            _lowerCamelCase   :				Union[str, Any]							=     "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			,		_lowerCamelCase=()			,		_lowerCamelCase=2       )    ->     List[Any]:
   '''simple docstring'''





   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"			,		):
         _lowerCamelCase   :				Tuple							=     PrepareForLaunch(_lowerCamelCase			,		debug=_lowerCamelCase       )
         start_processes(_lowerCamelCase			,		args=_lowerCamelCase			,		nprocs=_lowerCamelCase			,		start_method="fork"       )
"""simple docstring"""


from __future__ import annotations

import typing
from collections import Counter






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   Counter()
  for base in range(1 ,max_perimeter + 1		):
    for perpendicular in range(lowercase ,max_perimeter + 1		):
      _UpperCAmelCase    			=   (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(lowercase		):
        _UpperCAmelCase    			=   int(base + perpendicular + hypotenuse		)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets






def      __UpperCAmelCase       (      lowercase = 10_00		):







  """simple docstring"""
  _UpperCAmelCase    			=   pythagorean_triple(lowercase		)
  return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
    print(F'''Perimeter {solution()} has maximum solutions''')
