





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__: List[str]							   =						logging.get_logger(__name__)

a__: List[Any]							   =						{
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''blip_text_model'''



 def __init__(				self,__lowerCamelCase=3_0524,__lowerCamelCase=768,__lowerCamelCase=768,__lowerCamelCase=3072,__lowerCamelCase=768,__lowerCamelCase=12,__lowerCamelCase=8,__lowerCamelCase=512,__lowerCamelCase="gelu",__lowerCamelCase=1E-12,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.02,__lowerCamelCase=3_0522,__lowerCamelCase=2,__lowerCamelCase=0,__lowerCamelCase=102,__lowerCamelCase=True,__lowerCamelCase=True,**__lowerCamelCase,):
        super().__init__(
            pad_token_id=__lowerCamelCase,bos_token_id=__lowerCamelCase,eos_token_id=__lowerCamelCase,sep_token_id=__lowerCamelCase,**__lowerCamelCase,)

        A__		   =		vocab_size
        A__		   =		hidden_size
        A__		   =		encoder_hidden_size
        A__		   =		intermediate_size
        A__		   =		projection_dim
        A__		   =		hidden_dropout_prob
        A__		   =		num_hidden_layers
        A__		   =		num_attention_heads
        A__		   =		max_position_embeddings
        A__		   =		layer_norm_eps
        A__		   =		hidden_act
        A__		   =		initializer_range
        A__		   =		attention_probs_dropout_prob
        A__		   =		is_decoder
        A__		   =		use_cache




 @classmethod
 def 							UpperCamelCase     (				cls,__lowerCamelCase,**__lowerCamelCase					):
        cls._set_token_in_kwargs(__lowerCamelCase					)

        A__   ,      A__		   =		cls.get_config_dict(__lowerCamelCase,**__lowerCamelCase					)

        # get the text config dict if we are loading from BlipConfig
        if config_dict.get('''model_type'''					) == "blip":
               A__		   =		config_dict['''text_config''']

        if "model_type" in config_dict and hasattr(cls,'''model_type'''					) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                   f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."					)

        return cls.from_dict(__lowerCamelCase,**__lowerCamelCase					)




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''blip_vision_model'''



 def __init__(				self,__lowerCamelCase=768,__lowerCamelCase=3072,__lowerCamelCase=512,__lowerCamelCase=12,__lowerCamelCase=12,__lowerCamelCase=384,__lowerCamelCase=16,__lowerCamelCase="gelu",__lowerCamelCase=1E-5,__lowerCamelCase=0.0,__lowerCamelCase=1E-10,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase					)

        A__		   =		hidden_size
        A__		   =		intermediate_size
        A__		   =		projection_dim
        A__		   =		num_hidden_layers
        A__		   =		num_attention_heads
        A__		   =		patch_size
        A__		   =		image_size
        A__		   =		initializer_range
        A__		   =		attention_dropout
        A__		   =		layer_norm_eps
        A__		   =		hidden_act




 @classmethod
 def 							UpperCamelCase     (				cls,__lowerCamelCase,**__lowerCamelCase					):
        cls._set_token_in_kwargs(__lowerCamelCase					)

        A__   ,      A__		   =		cls.get_config_dict(__lowerCamelCase,**__lowerCamelCase					)

        # get the vision config dict if we are loading from BlipConfig
        if config_dict.get('''model_type'''					) == "blip":
               A__		   =		config_dict['''vision_config''']

        if "model_type" in config_dict and hasattr(cls,'''model_type'''					) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                   f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."					)

        return cls.from_dict(__lowerCamelCase,**__lowerCamelCase					)




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''blip'''
 __SCREAMING_SNAKE_CASE							=      True



 def __init__(				self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=512,__lowerCamelCase=2.6592,__lowerCamelCase=256,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase					)

        if text_config is None:
               A__		   =		{}
               logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''					)

        if vision_config is None:
               A__		   =		{}
               logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''					)

        A__		   =		BlipTextConfig(**__lowerCamelCase					)
        A__		   =		BlipVisionConfig(**__lowerCamelCase					)

        A__		   =		self.vision_config.hidden_size

        A__		   =		projection_dim
        A__		   =		logit_scale_init_value
        A__		   =		1.0
        A__		   =		0.02
        A__		   =		image_text_hidden_size



 @classmethod
 def 							UpperCamelCase     (				cls,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase					):
        return cls(text_config=text_config.to_dict(),vision_config=vision_config.to_dict(),**__lowerCamelCase					)




 def 							UpperCamelCase     (				self					):
        A__		   =		copy.deepcopy(self.__dict__					)
        A__		   =		self.text_config.to_dict()
        A__		   =		self.vision_config.to_dict()
        A__		   =		self.__class__.model_type
        return output












import math


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->list:
       A__		   =		[True] * n
       A__		   =		False
       A__		   =		False
       A__		   =		True

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

       A__		   =		[2]

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

       return primes


def       UpperCamelCase__(			UpperCamelCase__     :			int = 99_99_66_66_33_33    )->int:
       A__		   =		math.floor(math.sqrt(UpperCamelCase__    )    ) + 1_00
       A__		   =		prime_sieve(UpperCamelCase__    )

       A__		   =		0
       A__		   =		0
       A__		   =		primes[prime_index]

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

              A__		   =		last_prime**2
              A__		   =		next_prime**2

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

              # Remove the numbers divisible by both ups and lps
              A__		   =		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
              A__		   =		next_prime
              prime_index += 1

       return matches_sum


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





