






"""simple docstring"""




import copy
import os
from typing import Union

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


UpperCamelCase							       =    logging.get_logger(__name__)

UpperCamelCase							       =    {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		List[str]       			=				'''pix2struct_text_model'''
      A__	:		int       			=				['''past_key_values''']
      A__	:		Optional[Any]       			=				{
          '''hidden_size''': '''hidden_size''',
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }






      def __init__(    self     ,     a_=5_0_2_4_4     ,     a_=7_6_8     ,     a_=6_4     ,     a_=2_0_4_8     ,     a_=1_2     ,     a_=1_2     ,     a_=3_2     ,     a_=1_2_8     ,     a_=0.1     ,     a_=1E-6     ,     a_=1.0     ,     a_="gelu_new"     ,     a_=0     ,     a_=False     ,     a_=0     ,     a_=1     ,     a_=False     ,     a_=True     ,     **a_     ,     )	->Tuple:







        '''simple docstring'''


        _a							=	vocab_size
        _a							=	hidden_size
        _a							=	d_kv
        _a							=	d_ff
        _a							=	num_layers
        _a							=	num_heads
        _a							=	relative_attention_num_buckets
        _a							=	relative_attention_max_distance
        _a							=	dropout_rate
        _a							=	layer_norm_epsilon
        _a							=	initializer_factor
        _a							=	use_cache

        _a							=	eos_token_id
        _a							=	decoder_start_token_id

        # for backwards compatibility
        _a							=	dense_act_fn

        super().__init__(
            pad_token_id=a_     ,     eos_token_id=a_     ,     decoder_start_token_id=a_     ,     tie_word_embeddings=a_     ,     is_decoder=a_     ,     **a_     ,     )






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     a_     ,     **a_	)	->"PretrainedConfig":







        '''simple docstring'''


        cls._set_token_in_kwargs(a_	)

        _a			,	_a							=	cls.get_config_dict(a_     ,     **a_	)

        # get the text config dict if we are loading from Pix2StructConfig
        if config_dict.get("model_type"	) == "pix2struct":
          _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(a_     ,     **a_	)







class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Any       			=				'''pix2struct_vision_model'''






      def __init__(    self     ,     a_=7_6_8     ,     a_=7_6_8     ,     a_=2_0_4_8     ,     a_=6_4     ,     a_=1_2     ,     a_=1_2     ,     a_="gelu_new"     ,     a_=1E-6     ,     a_=0.0     ,     a_=0.0     ,     a_=1E-10     ,     a_=1.0     ,     a_=4_0_9_6     ,     a_=3_2     ,     a_=1_2_8     ,     **a_     ,     )	->str:







        '''simple docstring'''


        super().__init__(**a_	)

        _a							=	hidden_size
        _a							=	patch_embed_hidden_size
        _a							=	d_ff
        _a							=	dropout_rate
        _a							=	num_hidden_layers
        _a							=	num_attention_heads
        _a							=	initializer_range
        _a							=	initializer_factor
        _a							=	attention_dropout
        _a							=	layer_norm_eps
        _a							=	dense_act_fn
        _a							=	seq_len
        _a							=	relative_attention_num_buckets
        _a							=	relative_attention_max_distance
        _a							=	d_kv






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     a_     ,     **a_	)	->"PretrainedConfig":







        '''simple docstring'''


        cls._set_token_in_kwargs(a_	)

        _a			,	_a							=	cls.get_config_dict(a_     ,     **a_	)

        # get the vision config dict if we are loading from Pix2StructConfig
        if config_dict.get("model_type"	) == "pix2struct":
          _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(a_     ,     **a_	)







class    lowercase_    (_UpperCAmelCase     ):
      A__	:		List[Any]       			=				'''pix2struct'''
      A__	:		int       			=				True






      def __init__(    self     ,     a_=None     ,     a_=None     ,     a_=1.0     ,     a_=0.02     ,     a_=False     ,     a_=False     ,     a_=True     ,     **a_     ,     )	->Optional[Any]:







        '''simple docstring'''


        super().__init__(tie_word_embeddings=a_     ,     is_encoder_decoder=a_     ,     **a_	)

        if text_config is None:
          _a							=	{}
          logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."	)

        if vision_config is None:
          _a							=	{}
          logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."	)

        _a							=	PixaStructTextConfig(**a_	)
        _a							=	PixaStructVisionConfig(**a_	)

        _a							=	self.text_config.decoder_start_token_id
        _a							=	self.text_config.pad_token_id
        _a							=	self.text_config.eos_token_id

        _a							=	initializer_factor
        _a							=	initializer_range

        _a							=	self.initializer_range
        _a							=	self.initializer_range

        _a							=	is_vqa






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     a_     ,     a_     ,     **a_	)	->Dict:







        '''simple docstring'''


        return cls(text_config=text_config.to_dict()     ,     vision_config=vision_config.to_dict()     ,     **a_	)






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	copy.deepcopy(self.__dict__	)
        _a							=	self.text_config.to_dict()
        _a							=	self.vision_config.to_dict()
        _a							=	self.__class__.model_type
        return output










"""simple docstring"""




import math






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							list:







  '''simple docstring'''







  _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        lowerCAmelCase (      UpperCamelCase_:							int = 999966663333					)       ->							int:







  '''simple docstring'''







  _a							=	math.floor(math.sqrt(UpperCamelCase_					)					) + 100
  _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())


