"""simple docstring"""


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 					UpperCamelCase   (							_lowerCAmelCase  ):
       SCREAMING_SNAKE_CASE_						      = ["image_processor", "tokenizer"]
       SCREAMING_SNAKE_CASE_						      = "LayoutLMv2ImageProcessor"
       SCREAMING_SNAKE_CASE_						      = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")







       def __init__(					self,				lowerCAmelCase__=None,				lowerCAmelCase__=None,				**lowerCAmelCase__) -> List[str]:
            snake_case_   			=   None
            if "feature_extractor" in kwargs:
                 warnings.warn(
                     'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                     ' instead.',				__UpperCAmelCase,				)
                 snake_case_   			=   kwargs.pop('feature_extractor')

            snake_case_   			=   image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                 raise ValueError('You need to specify an `image_processor`.')
            if tokenizer is None:
                 raise ValueError('You need to specify a `tokenizer`.')

            super().__init__(__UpperCAmelCase,				__UpperCAmelCase)







       def __call__(					self,				lowerCAmelCase__,				lowerCAmelCase__ = None,				lowerCAmelCase__ = None,				lowerCAmelCase__ = None,				lowerCAmelCase__ = None,				lowerCAmelCase__ = True,				lowerCAmelCase__ = False,				lowerCAmelCase__ = False,				lowerCAmelCase__ = None,				lowerCAmelCase__ = 0,				lowerCAmelCase__ = None,				lowerCAmelCase__ = None,				lowerCAmelCase__ = None,				lowerCAmelCase__ = False,				lowerCAmelCase__ = False,				lowerCAmelCase__ = False,				lowerCAmelCase__ = False,				lowerCAmelCase__ = True,				lowerCAmelCase__ = None,				**lowerCAmelCase__,				) -> BatchEncoding:



            if self.image_processor.apply_ocr and (boxes is not None):
                 raise ValueError(
                     'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.')

            if self.image_processor.apply_ocr and (word_labels is not None):
                 raise ValueError(
                     'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.')

            if return_overflowing_tokens is True and return_offsets_mapping is False:
                 raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.')

            # first, apply the image processor
            snake_case_   			=   self.image_processor(images=__UpperCAmelCase,				return_tensors=__UpperCAmelCase)

            # second, apply the tokenizer
            if text is not None and self.image_processor.apply_ocr and text_pair is None:
                 if isinstance(__UpperCAmelCase,				__UpperCAmelCase):
                      snake_case_   			=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
                 snake_case_   			=   features['''words''']

            snake_case_   			=   self.tokenizer(
                text=text if text is not None else features['words'],				text_pair=text_pair if text_pair is not None else None,				boxes=boxes if boxes is not None else features['boxes'],				word_labels=__UpperCAmelCase,				add_special_tokens=__UpperCAmelCase,				padding=__UpperCAmelCase,				truncation=__UpperCAmelCase,				max_length=__UpperCAmelCase,				stride=__UpperCAmelCase,				pad_to_multiple_of=__UpperCAmelCase,				return_token_type_ids=__UpperCAmelCase,				return_attention_mask=__UpperCAmelCase,				return_overflowing_tokens=__UpperCAmelCase,				return_special_tokens_mask=__UpperCAmelCase,				return_offsets_mapping=__UpperCAmelCase,				return_length=__UpperCAmelCase,				verbose=__UpperCAmelCase,				return_tensors=__UpperCAmelCase,				**__UpperCAmelCase,				)

            # add pixel values
            snake_case_   			=   features.pop('pixel_values')
            if return_overflowing_tokens is True:
                 snake_case_   			=   self.get_overflowing_images(__UpperCAmelCase,				encoded_inputs['overflow_to_sample_mapping'])
            snake_case_   			=   images

            return encoded_inputs







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> Union[str, Any]:
            snake_case_   			=   []
            for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx])

            if len(__UpperCAmelCase) != len(__UpperCAmelCase):
                 raise ValueError(
                     'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                     f' {len(__UpperCAmelCase)} and {len(__UpperCAmelCase)}')

            return images_with_overflow







       def 						a_		(					self,				*lowerCAmelCase__,				**lowerCAmelCase__) -> Optional[int]:
            return self.tokenizer.batch_decode(*__UpperCAmelCase,				**__UpperCAmelCase)







       def 						a_		(					self,				*lowerCAmelCase__,				**lowerCAmelCase__) -> Union[str, Any]:
            return self.tokenizer.decode(*__UpperCAmelCase,				**__UpperCAmelCase)







       @property
       def 						a_		(					self) -> Optional[int]:
            return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







       @property
       def 						a_		(					self) -> Optional[int]:
            warnings.warn(
                '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',				__UpperCAmelCase,				)
            return self.image_processor_class







       @property
       def 						a_		(					self) -> Optional[int]:
            warnings.warn(
                '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',				__UpperCAmelCase,				)
            return self.image_processor


"""simple docstring"""




import math
from collections.abc import Generator



def 				__a       (			_lowercase						):







					"""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							, _lowercase						):
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			_lowercase						):







					"""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__		:   Tuple  				=					int(math.sqrt(_lowercase						)						) + 1
										for j in range(2							, _lowercase						):
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			_lowercase						):







					"""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__		:   Union[str, Any]  				=					int(math.sqrt(_lowercase						)						) + 1
										for j in range(3							, _lowercase							, 2						):
															# As we removed the even numbers, we don't need them now
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			):







					"""simple docstring"""





					from timeit import timeit

					lowerCamelCase__		:   Any  				=					'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)
					print(timeit('''primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)
					print(timeit('''fast_primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)


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






