
"""simple docstring"""





import math
from collections.abc import Generator







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Generator     	=       (i for i in range(1,						(max_n + 1)    ))
   for i in (n for n in numbers if n > 1):
      for j in range(2,						__lowerCamelCase    ):
         if (i % j) == 0:
            break
      else:
         yield i







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Generator     	=       (i for i in range(1,						(max_n + 1)    ))
   for i in (n for n in numbers if n > 1):
      # only need to check for factors up to sqrt(i)
      UpperCAmelCase_   :   Optional[int]     	=       int(math.sqrt(__lowerCamelCase    )    ) + 1
      for j in range(2,						__lowerCamelCase    ):
         if (i % j) == 0:
            break
      else:
         yield i







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Generator     	=       (i for i in range(1,						(max_n + 1),						2    ))
   # It's useless to test even numbers as they will not be prime
   if max_n > 2:
      yield 2  # Because 2 will not be tested, it's necessary to yield it now
   for i in (n for n in numbers if n > 1):
      UpperCAmelCase_   :   Any     	=       int(math.sqrt(__lowerCamelCase    )    ) + 1
      for j in range(3,						__lowerCamelCase,						2    ):
         # As we removed the even numbers, we don't need them now
         if (i % j) == 0:
            break
      else:
         yield i







def       __a	(						):
   from timeit import timeit

   UpperCAmelCase_   :   List[str]     	=       "from __main__ import slow_primes, primes, fast_primes"
   print(timeit("slow_primes(1_000_000_000_000)",						setup=__lowerCamelCase,						number=100_0000    )    )
   print(timeit("primes(1_000_000_000_000)",						setup=__lowerCamelCase,						number=100_0000    )    )
   print(timeit("fast_primes(1_000_000_000_000)",						setup=__lowerCamelCase,						number=100_0000    )    )


if __name__ == "__main__":
    _a									=				int(input('Calculate primes up to:\n>> ').strip())
    for ret in primes(number):
        print(ret)
    benchmark()







"""simple docstring"""







import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


UpperCAmelCase:  Union[str, Any]			     =				namedtuple("""ExampleNamedTuple""", """a b c""")





class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   torch.randn(5				,2   )
			_lowercase				: List[Any]     =   torch.device("""cuda"""   ) if torch.cuda.is_available() else torch.device("""cpu"""   )

			_lowercase				: int     =   send_to_device(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.cpu()				,UpperCAmelCase_   )   )

			_lowercase				: int     =   send_to_device((tensor, [tensor, tensor], 1)				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta[0].cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta[1]				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta[1][0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta[1][1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta[2]				,1   )

			_lowercase				: Any     =   send_to_device({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta["""a"""].cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta["""b"""]				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta["""b"""][0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta["""b"""][1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta["""c"""]				,1   )

			_lowercase				: Optional[int]     =   send_to_device(ExampleNamedTuple(a=UpperCAmelCase_				,b=[tensor, tensor]				,c=1   )				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.a.cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta.b				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.b[0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta.b[1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta.c				,1   )

			_lowercase				: str     =   send_to_device(UserDict({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}   )				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta["""a"""].cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta["""b"""]				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta["""b"""][0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta["""b"""][1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta["""c"""]				,1   )






	def    lowerCamelCase__					(     self   ):
			with self.assertRaises(UpperCAmelCase_   ) as cm:
					_lowercase				: str     =   recursively_apply(torch.tensor				,(torch.tensor(1   ), 1)				,error_on_other_type=UpperCAmelCase_   )
			self.assertEqual(
			    str(cm.exception   )				,"""Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed."""				,)






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   torch.tensor([1, 2, 3, 4, 5]   )
			self.assertEqual(listify(UpperCAmelCase_   )				,[1, 2, 3, 4, 5]   )

			_lowercase				: Optional[int]     =   torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]   )
			self.assertEqual(listify(UpperCAmelCase_   )				,[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]   )

			_lowercase				: Optional[Any]     =   torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]   )
			self.assertEqual(
			    listify(UpperCAmelCase_   )				,[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]   )






	def    lowerCamelCase__					(     self   ):
			with patch_environment(aa=1				,BB=2   ):
					self.assertEqual(os.environ.get("""AA"""   )				,"""1"""   )
					self.assertEqual(os.environ.get("""BB"""   )				,"""2"""   )

			self.assertNotIn("""AA"""				,os.environ   )
			self.assertNotIn("""BB"""				,os.environ   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   RegressionModel()
			_lowercase				: List[Any]     =   model.forward
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Any     =   extract_model_from_parallel(UpperCAmelCase_				,keep_fpaa_wrapper=UpperCAmelCase_   )
			_lowercase				: Tuple     =   pickle.dumps(UpperCAmelCase_   )






	@require_cuda
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   RegressionModel()
			_lowercase				: Any     =   model.forward
			_lowercase				: Any     =   torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Any     =   extract_model_from_parallel(UpperCAmelCase_				,keep_fpaa_wrapper=UpperCAmelCase_   )
			_lowercase				: Tuple     =   pickle.dumps(UpperCAmelCase_   )






	@require_cuda
	@require_torch_min_version(version="""2.0"""   )
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   RegressionModel()
			_lowercase				: Any     =   model.forward
			_lowercase				: Union[str, Any]     =   torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Union[str, Any]     =   torch.compile(model.forward				,backend="""inductor"""   )
			_lowercase				: Any     =   torch.randn(4				,10   ).cuda()
			_lowercase				: int     =   model(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   RegressionModel()
			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase				: Any     =   torch.nn.parallel.DataParallel(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   extract_model_from_parallel(UpperCAmelCase_   )

			self.assertEqual(UpperCAmelCase_				,UpperCAmelCase_   )






	@require_torch_min_version(version="""2.0"""   )
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   RegressionModel()
			_lowercase				: List[str]     =   torch.compile(UpperCAmelCase_   )

			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase				: Optional[int]     =   torch.nn.parallel.DataParallel(UpperCAmelCase_   )
			_lowercase				: int     =   torch.compile(UpperCAmelCase_   )
			_lowercase				: Dict     =   extract_model_from_parallel(UpperCAmelCase_   )

			self.assertEqual(compiled_model._orig_mod				,compiled_model_unwrapped._orig_mod   )






	def    lowerCamelCase__					(     self   ):
			self.assertEqual(find_device([1, """a""", torch.tensor([1, 2, 3]   )]   )				,torch.device("""cpu"""   )   )
			self.assertEqual(find_device({"""a""": 1, """b""": torch.tensor([1, 2, 3]   )}   )				,torch.device("""cpu"""   )   )
			self.assertIsNone(find_device([1, """a"""]   )   )



