


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__snake_case            =   {
    "configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverOnnxConfig"],
    "tokenization_perceiver": ["PerceiverTokenizer"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case            =   ["PerceiverFeatureExtractor"]
   __snake_case            =   ["PerceiverImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case            =   [
       "PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "PerceiverForImageClassificationConvProcessing",
       "PerceiverForImageClassificationFourier",
       "PerceiverForImageClassificationLearned",
       "PerceiverForMaskedLM",
       "PerceiverForMultimodalAutoencoding",
       "PerceiverForOpticalFlow",
       "PerceiverForSequenceClassification",
       "PerceiverLayer",
       "PerceiverModel",
       "PerceiverPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
   from .tokenization_perceiver import PerceiverTokenizer

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_perceiver import PerceiverFeatureExtractor
      from .image_processing_perceiver import PerceiverImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_perceiver import (
          PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
          PerceiverForImageClassificationConvProcessing,
          PerceiverForImageClassificationFourier,
          PerceiverForImageClassificationLearned,
          PerceiverForMaskedLM,
          PerceiverForMultimodalAutoencoding,
          PerceiverForOpticalFlow,
          PerceiverForSequenceClassification,
          PerceiverLayer,
          PerceiverModel,
          PerceiverPreTrainedModel,
      )

else:
   import sys

   __snake_case            =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








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())




