






"""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 			a	(  _a      ):
    UpperCAmelCase_					:	Tuple      	=["image_processor", "tokenizer"]
    UpperCAmelCase_					:	Optional[int]      	="LayoutLMv2ImageProcessor"
    UpperCAmelCase_					:	Optional[Any]      	=("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")







    def __init__(     self				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      lowercase				       =			None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'				,						_SCREAMING_SNAKE_CASE				,						)
        lowercase				       =			kwargs.pop('feature_extractor'			)

      lowercase				       =			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__(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)







    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				,						):






      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
      lowercase				       =			self.image_processor(images=_SCREAMING_SNAKE_CASE				,						return_tensors=_SCREAMING_SNAKE_CASE			)

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

      lowercase				       =			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=_SCREAMING_SNAKE_CASE				,						add_special_tokens=_SCREAMING_SNAKE_CASE				,						padding=_SCREAMING_SNAKE_CASE				,						truncation=_SCREAMING_SNAKE_CASE				,						max_length=_SCREAMING_SNAKE_CASE				,						stride=_SCREAMING_SNAKE_CASE				,						pad_to_multiple_of=_SCREAMING_SNAKE_CASE				,						return_token_type_ids=_SCREAMING_SNAKE_CASE				,						return_attention_mask=_SCREAMING_SNAKE_CASE				,						return_overflowing_tokens=_SCREAMING_SNAKE_CASE				,						return_special_tokens_mask=_SCREAMING_SNAKE_CASE				,						return_offsets_mapping=_SCREAMING_SNAKE_CASE				,						return_length=_SCREAMING_SNAKE_CASE				,						verbose=_SCREAMING_SNAKE_CASE				,						return_tensors=_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE				,						)

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

      return encoded_inputs







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			[]
      for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]			)

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

      return images_with_overflow







    def      UpperCamelCase_ (     self				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			)







    def      UpperCamelCase_ (     self				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			)







    @property
    def      UpperCamelCase_ (     self			):
      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







    @property
    def      UpperCamelCase_ (     self			):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,						_SCREAMING_SNAKE_CASE				,						)
      return self.image_processor_class







    @property
    def      UpperCamelCase_ (     self			):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,						_SCREAMING_SNAKE_CASE				,						)
      return self.image_processor




'''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()




