




"""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 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if is_torch_version('''<'''  ,      '''2.0.0'''	) or not hasattr(_UpperCAmelCase  ,      '''_dynamo'''	):
		return False
	return isinstance(_UpperCAmelCase  ,      torch._dynamo.eval_frame.OptimizedModule	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase = True	):




	"""simple docstring"""






	A_  :				Optional[int]	   =		(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

	A_  :				List[Any]	   =		is_compiled_module(_UpperCAmelCase	)
	if is_compiled:
		A_  :				Optional[Any]	   =		model
		A_  :				int	   =		model._orig_mod

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

	while isinstance(_UpperCAmelCase  ,      _UpperCAmelCase	):
		A_  :				int	   =		model.module

	if not keep_fpaa_wrapper:
		A_  :				Any	   =		getattr(_UpperCAmelCase  ,      '''forward'''	)
		A_  :				List[str]	   =		model.__dict__.pop('''_original_forward'''  ,      _UpperCAmelCase	)
		if original_forward is not None:
			while hasattr(_UpperCAmelCase  ,      '''__wrapped__'''	):
				A_  :				List[str]	   =		forward.__wrapped__
				if forward == original_forward:
					break
			A_  :				str	   =		forward
		if getattr(_UpperCAmelCase  ,      '''_converted_to_transformer_engine'''  ,      _UpperCAmelCase	):
			convert_model(_UpperCAmelCase  ,      to_transformer_engine=_UpperCAmelCase	)

	if is_compiled:
		A_  :				List[str]	   =		model
		A_  :				Dict	   =		compiled_model

	return model

def 		lowercase_      (		):




	"""simple docstring"""






	PartialState().wait_for_everyone()

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	if PartialState().distributed_type == DistributedType.TPU:
		xm.save(_UpperCAmelCase  ,      _UpperCAmelCase	)
	elif PartialState().local_process_index == 0:
		torch.save(_UpperCAmelCase  ,      _UpperCAmelCase	)

@contextmanager
def 		lowercase_      (		**_UpperCAmelCase	):




	"""simple docstring"""






	for key, value in kwargs.items():
		A_  :				Optional[Any]	   =		str(_UpperCAmelCase	)

	yield

	for key in kwargs:
		if key.upper() in os.environ:
			del os.environ[key.upper()]

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if not hasattr(_UpperCAmelCase  ,      '''__qualname__'''	) and not hasattr(_UpperCAmelCase  ,      '''__name__'''	):
		A_  :				Tuple	   =		getattr(_UpperCAmelCase  ,      '''__class__'''  ,      _UpperCAmelCase	)
	if hasattr(_UpperCAmelCase  ,      '''__qualname__'''	):
		return obj.__qualname__
	if hasattr(_UpperCAmelCase  ,      '''__name__'''	):
		return obj.__name__
	return str(_UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	for key, value in source.items():
		if isinstance(_UpperCAmelCase  ,      _UpperCAmelCase	):
			A_  :				Optional[Any]	   =		destination.setdefault(_UpperCAmelCase  ,      {}	)
			merge_dicts(_UpperCAmelCase  ,      _UpperCAmelCase	)
		else:
			A_  :				Dict	   =		value

	return destination

def 		lowercase_      (		_UpperCAmelCase = None	):




	"""simple docstring"""






	if port is None:
		A_  :				Optional[int]	   =		2_9500
	with socket.socket(socket.AF_INET  ,      socket.SOCK_STREAM	) as s:
		return s.connect_ex(('''localhost''', port)	) == 0











"""simple docstring"""

import random

from .binary_exp_mod import bin_exp_mod

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1000	):




	"""simple docstring"""






	if n < 2:
		return False

	if n % 2 == 0:
		return n == 2

	# this means n is odd
	A_  :				List[Any]	   =		n - 1
	A_  :				Dict	   =		0
	while d % 2 == 0:
		d /= 2
		exp += 1

	# n - 1=d*(2**exp)
	A_  :				Optional[Any]	   =		0
	while count < prec:
		A_  :				Optional[int]	   =		random.randint(2  ,      n - 1	)
		A_  :				Any	   =		bin_exp_mod(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
		if b != 1:
			A_  :				Dict	   =		True
			for _ in range(_UpperCAmelCase	):
				if b == n - 1:
					A_  :				Optional[int]	   =		False
					break
				A_  :				Any	   =		b * b
				b %= n
			if flag:
				return False
			count += 1
	return True


if __name__ == "__main__":
		_lowerCamelCase  :			List[str]        =       abs(int(input('Enter bound : ').strip()))
		print('Here\'s the list of primes:')
		print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))





