



import math
from collections.abc import Generator


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		(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			,       UpperCAmelCase__       ):
   if (i % j) == 0:
    break
  else:
   yield i


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		(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)
  a							=		int(math.sqrt(UpperCAmelCase__       )       ) + 1
  for j in range(2			,       UpperCAmelCase__       ):
   if (i % j) == 0:
    break
  else:
   yield i


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		(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):
  a							=		int(math.sqrt(UpperCAmelCase__       )       ) + 1
  for j in range(3			,       UpperCAmelCase__			,       2       ):
   # As we removed the even numbers, we don't need them now
   if (i % j) == 0:
    break
  else:
   yield i


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 from timeit import timeit

 a							=		"from __main__ import slow_primes, primes, fast_primes"
 print(timeit("slow_primes(1_000_000_000_000)"			,       setup=UpperCAmelCase__			,       number=1_00_00_00       )       )
 print(timeit("primes(1_000_000_000_000)"			,       setup=UpperCAmelCase__			,       number=1_00_00_00       )       )
 print(timeit("fast_primes(1_000_000_000_000)"			,       setup=UpperCAmelCase__			,       number=1_00_00_00       )       )


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



'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						=      {
    "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
    "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
    "processing_markuplm": ["MarkupLMProcessor"],
    "tokenization_markuplm": ["MarkupLMTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      ["MarkupLMTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      [
	    "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MarkupLMForQuestionAnswering",
	    "MarkupLMForSequenceClassification",
	    "MarkupLMForTokenClassification",
	    "MarkupLMModel",
	    "MarkupLMPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
	from .feature_extraction_markuplm import MarkupLMFeatureExtractor
	from .processing_markuplm import MarkupLMProcessor
	from .tokenization_markuplm import MarkupLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_markuplm_fast import MarkupLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_markuplm import (
		    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		    MarkupLMPreTrainedModel,
		)


else:
	import sys

	__SCREAMING_SNAKE_CASE						=      _LazyModule(__name__, globals()["__file__"], _import_structure)




