



"""simple docstring"""





import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
  from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
def        a__     (				lowerCAmelCase__				):
      if is_torch_version("<"     ,      "2.0.0"				) or not hasattr(lowerCAmelCase__     ,      "_dynamo"				):
            return False
      return isinstance(lowerCAmelCase__     ,      torch._dynamo.eval_frame.OptimizedModule				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__ = True				):
      UpperCAmelCase_      			=							(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

      UpperCAmelCase_      			=							is_compiled_module(lowerCAmelCase__				)
      if is_compiled:
            UpperCAmelCase_      			=							model
            UpperCAmelCase_      			=							model._orig_mod

      if is_deepspeed_available():
            options += (DeepSpeedEngine,)

      while isinstance(lowerCAmelCase__     ,      lowerCAmelCase__				):
            UpperCAmelCase_      			=							model.module

      if not keep_fpaa_wrapper:
            UpperCAmelCase_      			=							getattr(lowerCAmelCase__     ,      "forward"				)
            UpperCAmelCase_      			=							model.__dict__.pop("_original_forward"     ,      lowerCAmelCase__				)
            if original_forward is not None:
                  while hasattr(lowerCAmelCase__     ,      "__wrapped__"				):
                        UpperCAmelCase_      			=							forward.__wrapped__
                        if forward == original_forward:
                              break
                  UpperCAmelCase_      			=							forward
            if getattr(lowerCAmelCase__     ,      "_converted_to_transformer_engine"     ,      lowerCAmelCase__				):
                  convert_model(lowerCAmelCase__     ,      to_transformer_engine=lowerCAmelCase__				)

      if is_compiled:
            UpperCAmelCase_      			=							model
            UpperCAmelCase_      			=							compiled_model

      return model
def        a__     (				):
      PartialState().wait_for_everyone()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      if PartialState().distributed_type == DistributedType.TPU:
            xm.save(lowerCAmelCase__     ,      lowerCAmelCase__				)
      elif PartialState().local_process_index == 0:
            torch.save(lowerCAmelCase__     ,      lowerCAmelCase__				)
@contextmanager
def        a__     (				**lowerCAmelCase__				):
      for key, value in kwargs.items():
            UpperCAmelCase_      			=							str(lowerCAmelCase__				)

      yield

      for key in kwargs:
            if key.upper() in os.environ:
                  del os.environ[key.upper()]
def        a__     (				lowerCAmelCase__				):
      if not hasattr(lowerCAmelCase__     ,      "__qualname__"				) and not hasattr(lowerCAmelCase__     ,      "__name__"				):
            UpperCAmelCase_      			=							getattr(lowerCAmelCase__     ,      "__class__"     ,      lowerCAmelCase__				)
      if hasattr(lowerCAmelCase__     ,      "__qualname__"				):
            return obj.__qualname__
      if hasattr(lowerCAmelCase__     ,      "__name__"				):
            return obj.__name__
      return str(lowerCAmelCase__				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      for key, value in source.items():
            if isinstance(lowerCAmelCase__     ,      lowerCAmelCase__				):
                  UpperCAmelCase_      			=							destination.setdefault(lowerCAmelCase__     ,      {}				)
                  merge_dicts(lowerCAmelCase__     ,      lowerCAmelCase__				)
            else:
                  UpperCAmelCase_      			=							value

      return destination


def        a__     (				lowerCAmelCase__ = None				):
      if port is None:
            UpperCAmelCase_      			=							29500
      with socket.socket(socket.AF_INET     ,      socket.SOCK_STREAM				) as s:
            return s.connect_ex(("localhost", port)				) == 0









"""simple docstring"""





def        a__     (				lowerCAmelCase__ = 2000000				):
      UpperCAmelCase_      			=							[0 for i in range(n + 1				)]
      UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							1

      for i in range(2     ,      int(n**0.5				) + 1				):
            if primality_list[i] == 0:
                  for j in range(i * i     ,      n + 1     ,      lowerCAmelCase__				):
                        UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							0
      for i in range(lowerCAmelCase__				):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


if __name__ == "__main__":
  print(F"{solution() = }")




