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 SCREAMING_SNAKE_CASE					(  lowercase_:						int		,    lowercase_:						Optional[int]=False		,    lowercase_:						List[str]=False		,    lowercase_:						Optional[Any]=False					):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase  :							Dict	    =	[]
       for i in range(image_processor_tester.batch_size					):
              if equal_resolution:
                     _UpperCamelCase  :							Union[str, Any]	    =	image_processor_tester.max_resolution
              else:
                     # To avoid getting image width/height 0
                     _UpperCamelCase  :							Optional[int]	    =	image_processor_tester.min_resolution
                     if getattr(lowercase_		,    'size_divisor'		,    lowercase_					):
                            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                            _UpperCamelCase  :							Dict	    =	max(image_processor_tester.size_divisor		,    lowercase_					)
                     _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	np.random.choice(np.arange(lowercase_		,    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
              _UpperCamelCase  :							Optional[int]	    =	[Image.fromarray(np.moveaxis(lowercase_		,    0		,    -1					)					) for image in image_inputs]

       if torchify:
              _UpperCamelCase  :							Union[str, Any]	    =	[torch.from_numpy(lowercase_					) for image in image_inputs]

       return image_inputs


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Optional[Any]=10		,    lowercase_:						int=10		,    lowercase_:						Optional[int]=False		,    lowercase_:						str=False					):
       _UpperCamelCase  :							Dict	    =	[]
       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
              _UpperCamelCase  :							Any	    =	[Image.fromarray(np.moveaxis(lowercase_		,    0		,    -1					)					) for frame in video]

       if torchify:
              _UpperCamelCase  :							Any	    =	[torch.from_numpy(lowercase_					) for frame in video]

       return video


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						int=False		,    lowercase_:						List[Any]=False		,    lowercase_:						Union[str, Any]=False					):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

       return video_inputs





class  __lowerCAmelCase	:



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			None

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

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

        with tempfile.TemporaryDirectory() as tmpdirname:
               _UpperCamelCase  :							Tuple	    =	os.path.join(lowerCamelCase							,      'image_processor.json'	)
               image_processor_first.to_json_file(lowerCamelCase	)
               _UpperCamelCase  :							Dict	    =	self.image_processing_class.from_json_file(lowerCamelCase	)

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

 def        __snake_case       (      self							:		str	)			->				Optional[int]:
        _UpperCamelCase  :							Dict	    =	self.image_processing_class(**self.image_processor_dict	)

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

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

 def        __snake_case       (      self							:		List[str]	)			->				Optional[Any]:
        _UpperCamelCase  :							Tuple	    =	self.image_processing_class()
        self.assertIsNotNone(lowerCamelCase	)

 @require_torch
 @require_vision
 def        __snake_case       (      self							:		Dict	)			->				Tuple:
        if self.test_cast_dtype is not None:
               # Initialize image_processor
               _UpperCamelCase  :							Optional[int]	    =	self.image_processing_class(**self.image_processor_dict	)

               # create random PyTorch tensors
               _UpperCamelCase  :							Any	    =	prepare_image_inputs(self.image_processor_tester							,      equal_resolution=lowerCamelCase							,      torchify=lowerCamelCase	)

               _UpperCamelCase  :							Any	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	)
               # for layoutLM compatiblity
               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.floataa	)

               _UpperCamelCase  :							str	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	).to(torch.floataa	)
               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.floataa	)

               _UpperCamelCase  :							Tuple	    =	image_processor(lowerCamelCase							,      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(lowerCamelCase	):
                      _UpperCamelCase  :							int	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	).to(torch.bfloataa							,      'cpu'	)

               # Try with text + image feature
               _UpperCamelCase  :							Union[str, Any]	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	)
               encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]	)}	)
               _UpperCamelCase  :							Any	    =	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	)





'''simple docstring'''


import math
from collections.abc import Generator
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						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							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						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_     :	List[Any]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(2							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						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_     :	Optional[int]					     =     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 					__magic_name__(       ):
						from timeit import timeit

						UpperCamelCase_     :	int					     =     """from __main__ import slow_primes, primes, fast_primes"""
						print(timeit("""slow_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""fast_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)


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




