


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  (       lowerCamelCase__							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Tuple        =						["image_processor", "tokenizer"]
							SCREAMING_SNAKE_CASE   :					List[str]        =						"LayoutLMv2ImageProcessor"
							SCREAMING_SNAKE_CASE   :					Tuple        =						("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")





							def __init__(    self				, A_=None				, A_=None				, **A_	)->	int:
											_SCREAMING_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.'				, SCREAMING_SNAKE_CASE_				, )
															_SCREAMING_SNAKE_CASE      = kwargs.pop('feature_extractor'	)

											_SCREAMING_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__(SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_	)





							def __call__(    self				, A_				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = True				, A_ = False				, A_ = False				, A_ = None				, A_ = 0				, A_ = None				, A_ = None				, A_ = None				, A_ = False				, A_ = False				, A_ = False				, A_ = False				, A_ = True				, A_ = None				, **A_				, )->	Tuple:
											# verify input
											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
											_SCREAMING_SNAKE_CASE      = 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_	):
																			_SCREAMING_SNAKE_CASE      = [text]  # add batch dimension (as the image processor always adds a batch dimension)
															_SCREAMING_SNAKE_CASE      = features['words']

											_SCREAMING_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=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
											_SCREAMING_SNAKE_CASE      = features.pop('pixel_values'	)
											if return_overflowing_tokens is True:
															_SCREAMING_SNAKE_CASE      = self.get_overflowing_images(SCREAMING_SNAKE_CASE_				, encoded_inputs['overflow_to_sample_mapping']	)
											_SCREAMING_SNAKE_CASE      = images

											return encoded_inputs





							def 		__magic_name__			(    self				, A_				, A_	)->	Dict:
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											_SCREAMING_SNAKE_CASE      = []
											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 		__magic_name__			(    self				, *A_				, **A_	)->	Tuple:
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_				, **SCREAMING_SNAKE_CASE_	)





							def 		__magic_name__			(    self				, *A_				, **A_	)->	Union[str, Any]:
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_				, **SCREAMING_SNAKE_CASE_	)





							@property
							def 		__magic_name__			(    self	)->	Union[str, Any]:
											return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]





							@property
							def 		__magic_name__			(    self	)->	str:
											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 		__magic_name__			(    self	)->	Union[str, Any]:
											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				, SCREAMING_SNAKE_CASE_				, )
											return self.image_processor








from __future__ import annotations

from collections import Counter


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = [True] * n
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = True

				for i in range(3   ,int(n**0.5 + 1							)   ,2							):
								_SCREAMING_SNAKE_CASE      = i * 2
								while index < n:
												_SCREAMING_SNAKE_CASE      = False
												_SCREAMING_SNAKE_CASE      = index + i

				_SCREAMING_SNAKE_CASE      = [2]

				for i in range(3   ,UpperCAmelCase__   ,2							):
								if is_prime[i]:
												primes.append(UpperCAmelCase__							)

				return primes


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = str(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = []
				_SCREAMING_SNAKE_CASE      = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

				for duplicate in Counter(UpperCAmelCase__							) - Counter(set(UpperCAmelCase__							)							):
								_SCREAMING_SNAKE_CASE      = [int(number_str.replace(UpperCAmelCase__   ,UpperCAmelCase__							)							) for digit in digits]
								replacements.append(UpperCAmelCase__							)

				return replacements


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__ = 8							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()

				# Filter primes with less than 3 replaceable digits
				_SCREAMING_SNAKE_CASE      = {
				    x for x in set(prime_sieve(100_0000							)							) if len(str(UpperCAmelCase__							)							) - len(set(str(UpperCAmelCase__							)							)							) >= 3
				}

				for prime in primes:
								if prime in numbers_checked:
												continue

								_SCREAMING_SNAKE_CASE      = digit_replacements(UpperCAmelCase__							)

								for family in replacements:
												numbers_checked.update(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = primes.intersection(UpperCAmelCase__							)

												if len(UpperCAmelCase__							) != family_length:
																continue

												return min(UpperCAmelCase__							)

				return -1


if __name__ == "__main__":
							print(solution())
