


'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_lowerCAmelCase	:		int		  =						logging.get_logger(__name__)


if is_vision_available():
				import PIL



class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       str         =			["""pixel_values"""]



	def __init__(       self			,       snake_case__ = True			,       snake_case__ = None			,       snake_case__ = PILImageResampling.BILINEAR			,       snake_case__ = True			,       snake_case__ = False			,       snake_case__ = True			,       snake_case__ = True			,       snake_case__ = 1 / 255			,       snake_case__ = True			,       snake_case__ = None			,       snake_case__ = None			,       **snake_case__			,       ):
		super().__init__(**snake_case__     )

		lowerCAmelCase       :     Dict   				=			size if size is not None else {'height': 2560, 'width': 1920}
		if isinstance(snake_case__			,       (tuple, list)     ):
			# The previous feature extractor size parameter was in (width, height) format
			lowerCAmelCase       :     Tuple   				=			size[::-1]
		lowerCAmelCase       :     Any   				=			get_size_dict(snake_case__     )

		lowerCAmelCase       :     str   				=			do_resize
		lowerCAmelCase       :     Optional[int]   				=			size
		lowerCAmelCase       :     List[str]   				=			resample
		lowerCAmelCase       :     Tuple   				=			do_thumbnail
		lowerCAmelCase       :     Dict   				=			do_align_long_axis
		lowerCAmelCase       :     List[Any]   				=			do_pad
		lowerCAmelCase       :     Any   				=			do_rescale
		lowerCAmelCase       :     Tuple   				=			rescale_factor
		lowerCAmelCase       :     str   				=			do_normalize
		lowerCAmelCase       :     Tuple   				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		lowerCAmelCase       :     int   				=			image_std if image_std is not None else IMAGENET_STANDARD_STD



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__ = None     ):
		lowerCAmelCase				, lowerCAmelCase       :     Tuple   				=			get_image_size(snake_case__     )
		lowerCAmelCase				, lowerCAmelCase       :     Optional[int]   				=			size['height'], size['width']

		if (output_width < output_height and input_width > input_height) or (
		    output_width > output_height and input_width < input_height
		):
			lowerCAmelCase       :     int   				=			np.rotaa(snake_case__			,       3     )

		if data_format is not None:
			lowerCAmelCase       :     Dict   				=			to_channel_dimension_format(snake_case__			,       snake_case__     )

		return image



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__ = False			,       snake_case__ = None			,       ):
		lowerCAmelCase				, lowerCAmelCase       :     Optional[int]   				=			size['height'], size['width']
		lowerCAmelCase				, lowerCAmelCase       :     int   				=			get_image_size(snake_case__     )

		lowerCAmelCase       :     str   				=			output_width - input_width
		lowerCAmelCase       :     Dict   				=			output_height - input_height

		if random_padding:
			lowerCAmelCase       :     Dict   				=			np.random.randint(low=0			,       high=delta_height + 1     )
			lowerCAmelCase       :     Optional[Any]   				=			np.random.randint(low=0			,       high=delta_width + 1     )
		else:
			lowerCAmelCase       :     Union[str, Any]   				=			delta_height // 2
			lowerCAmelCase       :     Dict   				=			delta_width // 2

		lowerCAmelCase       :     Optional[Any]   				=			delta_height - pad_top
		lowerCAmelCase       :     List[str]   				=			delta_width - pad_left

		lowerCAmelCase       :     Union[str, Any]   				=			((pad_top, pad_bottom), (pad_left, pad_right))
		return pad(snake_case__			,       snake_case__			,       data_format=snake_case__     )



	def 					lowercase  (       self			,       *snake_case__			,       **snake_case__     ):
		logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'     )
		return self.pad_image(*snake_case__			,       **snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__ = PILImageResampling.BICUBIC			,       snake_case__ = None			,       **snake_case__			,       ):
		lowerCAmelCase				, lowerCAmelCase       :     Any   				=			get_image_size(snake_case__     )
		lowerCAmelCase				, lowerCAmelCase       :     str   				=			size['height'], size['width']

		# We always resize to the smallest of either the input or output size.
		lowerCAmelCase       :     Optional[Any]   				=			min(snake_case__			,       snake_case__     )
		lowerCAmelCase       :     List[Any]   				=			min(snake_case__			,       snake_case__     )

		if height == input_height and width == input_width:
			return image

		if input_height > input_width:
			lowerCAmelCase       :     Any   				=			int(input_width * height / input_height     )
		elif input_width > input_height:
			lowerCAmelCase       :     Tuple   				=			int(input_height * width / input_width     )

		return resize(
		    snake_case__			,       size=(height, width)			,       resample=snake_case__			,       reducing_gap=2.0			,       data_format=snake_case__			,       **snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__ = PILImageResampling.BICUBIC			,       snake_case__ = None			,       **snake_case__			,       ):
		lowerCAmelCase       :     int   				=			get_size_dict(snake_case__     )
		lowerCAmelCase       :     Tuple   				=			min(size['height']			,       size['width']     )
		lowerCAmelCase       :     List[str]   				=			get_resize_output_image_size(snake_case__			,       size=snake_case__			,       default_to_square=snake_case__     )
		lowerCAmelCase       :     str   				=			resize(snake_case__			,       size=snake_case__			,       resample=snake_case__			,       data_format=snake_case__			,       **snake_case__     )
		return resized_image



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__ = None			,       **snake_case__			,       ):
		return rescale(snake_case__			,       scale=snake_case__			,       data_format=snake_case__			,       **snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__			,       snake_case__ = None			,       **snake_case__			,       ):
		return normalize(snake_case__			,       mean=snake_case__			,       std=snake_case__			,       data_format=snake_case__			,       **snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = False			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = ChannelDimension.FIRST			,       **snake_case__			,       ):
		lowerCAmelCase       :     Dict   				=			do_resize if do_resize is not None else self.do_resize
		lowerCAmelCase       :     Optional[int]   				=			size if size is not None else self.size
		if isinstance(snake_case__			,       (tuple, list)     ):
			# Previous feature extractor had size in (width, height) format
			lowerCAmelCase       :     Dict   				=			size[::-1]
		lowerCAmelCase       :     Union[str, Any]   				=			get_size_dict(snake_case__     )
		lowerCAmelCase       :     int   				=			resample if resample is not None else self.resample
		lowerCAmelCase       :     List[str]   				=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
		lowerCAmelCase       :     List[Any]   				=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
		lowerCAmelCase       :     Optional[Any]   				=			do_pad if do_pad is not None else self.do_pad
		lowerCAmelCase       :     Any   				=			do_rescale if do_rescale is not None else self.do_rescale
		lowerCAmelCase       :     List[str]   				=			rescale_factor if rescale_factor is not None else self.rescale_factor
		lowerCAmelCase       :     Union[str, Any]   				=			do_normalize if do_normalize is not None else self.do_normalize
		lowerCAmelCase       :     Optional[Any]   				=			image_mean if image_mean is not None else self.image_mean
		lowerCAmelCase       :     List[str]   				=			image_std if image_std is not None else self.image_std

		lowerCAmelCase       :     List[str]   				=			make_list_of_images(snake_case__     )

		if not valid_images(snake_case__     ):
			raise ValueError(
			    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
			    'torch.Tensor, tf.Tensor or jax.ndarray.'     )

		if do_resize and size is None:
			raise ValueError('Size must be specified if do_resize is True.'     )

		if do_rescale and rescale_factor is None:
			raise ValueError('Rescale factor must be specified if do_rescale is True.'     )

		if do_pad and size is None:
			raise ValueError('Size must be specified if do_pad is True.'     )

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError('Image mean and std must be specified if do_normalize is True.'     )

		# All transformations expect numpy arrays.
		lowerCAmelCase       :     List[Any]   				=			[to_numpy_array(snake_case__     ) for image in images]

		if do_align_long_axis:
			lowerCAmelCase       :     List[Any]   				=			[self.align_long_axis(snake_case__			,       size=snake_case__     ) for image in images]

		if do_resize:
			lowerCAmelCase       :     int   				=			[self.resize(image=snake_case__			,       size=snake_case__			,       resample=snake_case__     ) for image in images]

		if do_thumbnail:
			lowerCAmelCase       :     Dict   				=			[self.thumbnail(image=snake_case__			,       size=snake_case__     ) for image in images]

		if do_pad:
			lowerCAmelCase       :     Optional[int]   				=			[self.pad_image(image=snake_case__			,       size=snake_case__			,       random_padding=snake_case__     ) for image in images]

		if do_rescale:
			lowerCAmelCase       :     List[str]   				=			[self.rescale(image=snake_case__			,       scale=snake_case__     ) for image in images]

		if do_normalize:
			lowerCAmelCase       :     int   				=			[self.normalize(image=snake_case__			,       mean=snake_case__			,       std=snake_case__     ) for image in images]

		lowerCAmelCase       :     Optional[int]   				=			[to_channel_dimension_format(snake_case__			,       snake_case__     ) for image in images]

		lowerCAmelCase       :     Union[str, Any]   				=			{'pixel_values': images}
		return BatchFeature(data=snake_case__			,       tensor_type=snake_case__     )




'''simple docstring'''




import math
from collections.abc import Generator

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





	lowerCAmelCase       :     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       , _A					):
			if (i % j) == 0:
				break
		else:
			yield i

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





	lowerCAmelCase       :     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)
		lowerCAmelCase       :     List[str]   				=			int(math.sqrt(_A					)					) + 1
		for j in range(2       , _A					):
			if (i % j) == 0:
				break
		else:
			yield i

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





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

def       __UpperCamelCase     (     )     ->   Dict:




	"""simple docstring"""





	from timeit import timeit

	lowerCAmelCase       :     Tuple   				=			'from __main__ import slow_primes, primes, fast_primes'
	print(timeit('slow_primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)
	print(timeit('primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)
	print(timeit('fast_primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)


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