


import warnings
from typing import List, Optional, Union

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




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= ["""image_processor""", """tokenizer"""]
			lowercase						= """FlavaImageProcessor"""
			lowercase						= ("""BertTokenizer""", """BertTokenizerFast""")




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		str=None       ,       **__magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""       ,       __magic_name__       ,       )
									UpperCamelCase         =					kwargs.pop("""feature_extractor"""						)

						UpperCamelCase         =					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__(__magic_name__       ,       __magic_name__						)
						UpperCamelCase         =					self.image_processor




			def __call__(						self							:		int       ,       __magic_name__							:		Optional[ImageInput] = None       ,       __magic_name__							:		Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Union[bool, str, PaddingStrategy] = False       ,       __magic_name__							:		Union[bool, str, TruncationStrategy] = False       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		int = 0       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Union[str, TensorType]] = None       ,       **__magic_name__							:		str       ,       ):







						"""simple docstring"""







						if text is None and images is None:
									raise ValueError("""You have to specify either text or images. Both cannot be none."""						)

						if text is not None:
									UpperCamelCase         =					self.tokenizer(
									    text=__magic_name__       ,       add_special_tokens=__magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       stride=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_token_type_ids=__magic_name__       ,       return_attention_mask=__magic_name__       ,       return_overflowing_tokens=__magic_name__       ,       return_special_tokens_mask=__magic_name__       ,       return_offsets_mapping=__magic_name__       ,       return_length=__magic_name__       ,       verbose=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )
						if images is not None:
									UpperCamelCase         =					self.image_processor(
									    __magic_name__       ,       return_image_mask=__magic_name__       ,       return_codebook_pixels=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )

						if text is not None and images is not None:
									encoding.update(__magic_name__						)
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__magic_name__						)       ,       tensor_type=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple       ,       *__magic_name__							:		Dict       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		int       ,       *__magic_name__							:		Tuple       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







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




			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.tokenizer.model_input_names
						UpperCamelCase         =					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,       __magic_name__       ,       )
						return self.image_processor_class





			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







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








import math






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					[True] * n
			UpperCamelCase         =					False
			UpperCamelCase         =					False
			UpperCamelCase         =					True

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

			UpperCamelCase         =					[2]

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

			return primes






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 999_966_663_333	):





			"""simple docstring"""




			UpperCamelCase         =					math.floor(math.sqrt(SCREAMING_SNAKE_CASE_	)	) + 100
			UpperCamelCase         =					prime_sieve(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					0
			UpperCamelCase         =					0
			UpperCamelCase         =					primes[prime_index]

			while (last_prime**2) <= limit:
						UpperCamelCase         =					primes[prime_index + 1]

						UpperCamelCase         =					last_prime**2
						UpperCamelCase         =					next_prime**2

						# Get numbers divisible by lps(current)
						UpperCamelCase         =					lower_bound + last_prime
						while upper_bound > current <= limit:
									matches_sum += current
									current += last_prime

						# Reset the upper_bound
						while (upper_bound - next_prime) > limit:
									upper_bound -= next_prime

						# Add the numbers divisible by ups(current)
						UpperCamelCase         =					upper_bound - next_prime
						while current > lower_bound:
									matches_sum += current
									current -= next_prime

						# Remove the numbers divisible by both ups and lps
						UpperCamelCase         =					0
						while upper_bound > current <= limit:
									if current <= lower_bound:
												# Increment the current number
												current += last_prime * next_prime
												continue

									if current > limit:
												break

									# Remove twice since it was added by both ups and lps
									matches_sum -= current * 2

									# Increment the current number
									current += last_prime * next_prime

						# Setup for next pair
						UpperCamelCase         =					next_prime
						prime_index += 1

			return matches_sum


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




