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


SCREAMING_SNAKE_CASE__		:       Tuple       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       Union[str, Any]       						=       {
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'markuplm'




  def __init__(   self  :						List[Any]  ,			a_  :						List[Any]=3_0522  ,			a_  :						int=768  ,			a_  :						Union[str, Any]=12  ,			a_  :						str=12  ,			a_  :						Any=3072  ,			a_  :						Tuple="gelu"  ,			a_  :						Any=0.1  ,			a_  :						Optional[Any]=0.1  ,			a_  :						Optional[Any]=512  ,			a_  :						int=2  ,			a_  :						List[Any]=0.02  ,			a_  :						str=1e-1_2  ,			a_  :						Optional[int]=0  ,			a_  :						List[str]=0  ,			a_  :						Optional[Any]=2  ,			a_  :						str=256  ,			a_  :						List[str]=1024  ,			a_  :						Optional[int]=216  ,			a_  :						Tuple=1001  ,			a_  :						Any=32  ,			a_  :						Optional[int]=50  ,			a_  :						Optional[int]="absolute"  ,			a_  :						Optional[int]=True  ,			a_  :						str=None  ,			**a_  :						Tuple  ,			)->							Optional[Any]:




       """simple docstring"""
       super().__init__(
           pad_token_id=a_  ,			bos_token_id=a_  ,			eos_token_id=a_  ,			**a_  ,			)
       SCREAMING_SNAKE_CASE__					:					int      =		vocab_size
       SCREAMING_SNAKE_CASE__					:					List[str]      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					Any      =		num_hidden_layers
       SCREAMING_SNAKE_CASE__					:					Tuple      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		hidden_act
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		intermediate_size
       SCREAMING_SNAKE_CASE__					:					Any      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					str      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		max_position_embeddings
       SCREAMING_SNAKE_CASE__					:					Dict      =		type_vocab_size
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		initializer_range
       SCREAMING_SNAKE_CASE__					:					Any      =		layer_norm_eps
       SCREAMING_SNAKE_CASE__					:					str      =		position_embedding_type
       SCREAMING_SNAKE_CASE__					:					Any      =		use_cache
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		classifier_dropout
       # additional properties
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		max_depth
       SCREAMING_SNAKE_CASE__					:					Tuple      =		max_xpath_tag_unit_embeddings
       SCREAMING_SNAKE_CASE__					:					int      =		max_xpath_subs_unit_embeddings
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		tag_pad_id
       SCREAMING_SNAKE_CASE__					:					str      =		subs_pad_id
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		xpath_unit_hidden_size









from __future__ import annotations

from collections.abc import Generator

def    UpperCAmelCase_				( )   ->					Generator[int, None, None]:
      SCREAMING_SNAKE_CASE_      		=     {}
      SCREAMING_SNAKE_CASE_      		=     2
      while True:
            SCREAMING_SNAKE_CASE_      		=     factor_map.pop(__UpperCAmelCase     ,     __UpperCAmelCase				)
            if factor:
                  SCREAMING_SNAKE_CASE_      		=     factor + prime
                  while x in factor_map:
                        x += factor
                  SCREAMING_SNAKE_CASE_      		=     factor
            else:
                  SCREAMING_SNAKE_CASE_      		=     prime
                  yield prime
            prime += 1

def    UpperCAmelCase_				( __UpperCAmelCase		:      float = 1E10				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     sieve()

      SCREAMING_SNAKE_CASE_      		=     1
      while True:
            SCREAMING_SNAKE_CASE_      		=     next(__UpperCAmelCase				)
            if (2 * prime * n) > limit:
                  return n
            # Ignore the next prime as the reminder will be 2.
            next(__UpperCAmelCase				)
            n += 2


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