



import math
from collections.abc import Generator





def     __lowerCamelCase							(   __magic_name__						:	int    ):
							a__:							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     __lowerCamelCase							(   __magic_name__						:	int    ):
							a__:							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)
														a__:							str									=int(math.sqrt(__lowerCamelCase    )    ) + 1
														for j in range(2  ,			__lowerCamelCase    ):
																					if (i % j) == 0:
																												break
														else:
																					yield i





def     __lowerCamelCase							(   __magic_name__						:	int    ):
							a__:							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):
														a__:							List[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     __lowerCamelCase							(   ):
							from timeit import timeit

							a__:							Union[str, Any]									="from __main__ import slow_primes, primes, fast_primes"
							print(timeit("slow_primes(1_000_000_000_000)"  ,			setup=__lowerCamelCase  ,			number=1_000_000    )    )
							print(timeit("primes(1_000_000_000_000)"  ,			setup=__lowerCamelCase  ,			number=1_000_000    )    )
							print(timeit("fast_primes(1_000_000_000_000)"  ,			setup=__lowerCamelCase  ,			number=1_000_000    )    )


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










import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  str=False		,						__lowerCamelCase :  Optional[Any]=False		,						__lowerCamelCase :  int=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_processor_tester.min_resolution
   if getattr(__lowerCamelCase		,						"size_divisor"		,						__lowerCamelCase  ):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    __SCREAMING_SNAKE_CASE							:     str							=						max(image_processor_tester.size_divisor		,						__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						np.random.choice(np.arange(__lowerCamelCase		,						image_processor_tester.max_resolution  )		,						2  )
  image_inputs.append(
      np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Dict							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for image in image_inputs]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     Dict							=						[torch.from_numpy(__lowerCamelCase  ) for image in image_inputs]

 return image_inputs






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  List[Any]=10		,						__lowerCamelCase :  Any=10		,						__lowerCamelCase :  int=False		,						__lowerCamelCase :  List[str]=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
 for i in range(image_processor_tester.num_frames  ):
  video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for frame in video]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						[torch.from_numpy(__lowerCamelCase  ) for frame in video]

 return video






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict=False		,						__lowerCamelCase :  Optional[int]=False		,						__lowerCamelCase :  Any=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Dict							=						np.random.choice(
       np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution  )		,						2  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_video(
       image_processor_tester=__lowerCamelCase		,						width=__lowerCamelCase		,						height=__lowerCamelCase		,						numpify=__lowerCamelCase		,						torchify=__lowerCamelCase		,						)
  video_inputs.append(__lowerCamelCase  )

 return video_inputs






class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Dict					)->int:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
      __SCREAMING_SNAKE_CASE							:     str							=						json.loads(image_processor.to_json_string()					)
      for key, value in self.image_processor_dict.items():
       self.assertEqual(obj[key]  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Dict							=						os.path.join(UpperCamelCase  ,	"image_processor.json"					)
       image_processor_first.to_json_file(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_json_file(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				Optional[int]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor_first.save_pretrained(UpperCamelCase					)[0]
       check_json_file_has_correct_format(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class.from_pretrained(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class()
      self.assertIsNotNone(UpperCamelCase					)






     @require_torch
     @require_vision
     def 						__snake_case     (							self			:				Any					)->List[Any]:
      if self.test_cast_dtype is not None:
       # Initialize image_processor
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       # for layoutLM compatiblity
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     int							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.floataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to("cpu"  ,	torch.bfloataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.bfloataa					)

       with self.assertRaises(UpperCamelCase					):
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.bfloataa  ,	"cpu"					)

       # Try with text + image feature
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						encoding.to(torch.floataa					)

       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)
       self.assertEqual(encoding.input_ids.dtype  ,	torch.long					)






