

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def      _lowerCAmelCase(	__A   ,       __A=()   ,       __A=None   ,       __A="no"   ,       __A="29500"			):
  UpperCAmelCase					      =  False
  UpperCAmelCase					      =  False
  if any(key.startswith("KAGGLE"			) for key in os.environ.keys()			):
    UpperCAmelCase					      =  True
  elif "IPython" in sys.modules:
    UpperCAmelCase					      =  "google.colab" in str(sys.modules["IPython"].get_ipython()			)

  try:
    UpperCAmelCase					      =  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"   ,       __A			) 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					      =  8

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





def      _lowerCAmelCase(	__A   ,       __A=()   ,       __A=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=__A   ,       master_addr="127.0.01"   ,       master_port="29500"   ,       accelerate_mixed_precision="no"   ,       accelerate_debug_rdv_file=tmp_file.name   ,       accelerate_use_cpu="yes"   ,       ):
      UpperCAmelCase					      =  PrepareForLaunch(__A   ,       debug=__A			)
      start_processes(__A   ,       args=__A   ,       nprocs=__A   ,       start_method="fork"			)







def      _lowerCAmelCase(	__A   ,       __A			):
  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def      _lowerCAmelCase(	__A   ,       __A=0			):
  return sorted(__A   ,       key=lambda __A			: x[column]			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(points_counts - 1			):
    for j in range(i + 1   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(min(6   ,       points_counts - 1			)   ,       __A			):
    for j in range(max(0   ,       i - 6			)   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):

  # base case
  if points_counts <= 3:
    return dis_between_closest_pair(__A   ,       __A			)

  # recursion
  UpperCAmelCase					      =  points_counts // 2
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[:mid]   ,       __A			)
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[mid:]   ,       points_counts - mid			)
  UpperCAmelCase					      =  min(__A   ,       __A			)
  UpperCAmelCase					      =  []
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]			) < closest_pair_dis:
      cross_strip.append(__A			)

  UpperCAmelCase					      =  dis_between_closest_in_strip(
      __A   ,       len(__A			)   ,       __A			)
  return min(__A   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=0			)
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=1			)
  return (
      closest_pair_of_points_sqr(
          __A   ,       __A   ,       __A			)
  ) ** 0.5


if __name__ == "__main__":
      lowerCAmelCase__       = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
      print("Distance:", closest_pair_of_points(points, len(points)))



