


'''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        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    ['image_processor', 'tokenizer']
		_a   			=    'LayoutLMv2ImageProcessor'
		_a   			=    ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')




		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''   ,       UpperCamelCase_   ,       )
												UpperCamelCase__       :Tuple							 =			kwargs.pop('''feature_extractor'''							)

							UpperCamelCase__       :Union[str, Any]							 =			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   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = False   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = 0   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = False   ,       UpperCamelCase_ = False   ,       UpperCamelCase_ = False   ,       UpperCamelCase_ = False   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = None   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							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
							UpperCamelCase__       :Any							 =			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_							):
																	UpperCamelCase__       :List[str]							 =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
												UpperCamelCase__       :int							 =			features['''words''']

							UpperCamelCase__       :List[str]							 =			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
							UpperCamelCase__       :Union[str, Any]							 =			features.pop('''pixel_values'''							)
							if return_overflowing_tokens is True:
												UpperCamelCase__       :Any							 =			self.get_overflowing_images(UpperCamelCase_   ,       encoded_inputs['''overflow_to_sample_mapping''']							)
							UpperCamelCase__       :Tuple							 =			images

							return encoded_inputs




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			[]
							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    lowerCAmelCase__    (		self   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_   ,       **UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_   ,       **UpperCamelCase_							)




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return ["input_ids", "bbox", "attention_mask", "image"]




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							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    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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



'''simple docstring'''
from __future__ import annotations

__snake_case											=					[True] * 1000001
__snake_case											=					2
while i * i <= 1000000:
		if seive[i]:
				for j in range(i * i, 1000001, i):
						__snake_case											=					False
		i += 1


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					return seive[n]


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					return any(digit in '''02468''' for digit in str(__a       )       )


def       a				(      __a = 1000000       )  ->     list[int]:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			[2]  # result already includes the number 2.
					for num in range(3			, limit + 1			, 2       ):
										if is_prime(__a       ) and not contains_an_even_digit(__a       ):
															UpperCamelCase__       :str							 =			str(__a       )
															UpperCamelCase__       :List[str]							 =			[int(str_num[j:] + str_num[:j]       ) for j in range(len(__a       )       )]
															if all(is_prime(__a       ) for i in list_nums       ):
																				result.append(__a       )
					return result


def       a				(      )  ->     int:



					'''simple docstring'''






					return len(find_circular_primes()       )


if __name__ == "__main__":
		print(F"""{len(find_circular_primes()) = }""")