




"""simple docstring"""






import math







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Any    =    [True] * n
				_lowerCamelCase     :			List[Any]    =    False
				_lowerCamelCase     :			Optional[int]    =    False
				_lowerCamelCase     :			Optional[int]    =    True

				for i in range(3							,    int(n**0.5 + 1		)							,    2		):
								_lowerCamelCase     :			Union[str, Any]    =    i * 2
								while index < n:
												_lowerCamelCase     :			List[Any]    =    False
												_lowerCamelCase     :			str    =    index + i

				_lowerCamelCase     :			Any    =    [2]

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

				return primes







def      _snake_case			(				lowercase__ = 999966663333		):
				_lowerCamelCase     :			Tuple    =    math.floor(math.sqrt(lowercase__		)		) + 100
				_lowerCamelCase     :			Optional[int]    =    prime_sieve(lowercase__		)

				_lowerCamelCase     :			List[Any]    =    0
				_lowerCamelCase     :			Optional[int]    =    0
				_lowerCamelCase     :			Tuple    =    primes[prime_index]

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

								_lowerCamelCase     :			Dict    =    last_prime**2
								_lowerCamelCase     :			int    =    next_prime**2

								# Get numbers divisible by lps(current)
								_lowerCamelCase     :			Any    =    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)
								_lowerCamelCase     :			str    =    upper_bound - next_prime
								while current > lower_bound:
												matches_sum += current
												current -= next_prime

								# Remove the numbers divisible by both ups and lps
								_lowerCamelCase     :			int    =    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
								_lowerCamelCase     :			str    =    next_prime
								prime_index += 1

				return matches_sum


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





"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states