




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase								=     logging.get_logger(__name__)

UpperCamelCase								=     {
    'microsoft/layoutlm-base-uncased': (
        'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'
    ),
    'microsoft/layoutlm-large-uncased': (
        'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'
    ),
}





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    snake_case__					=      "layoutlm"


    def __init__(       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Dict=30_522   ,	SCREAMING_SNAKE_CASE__     :     str=768   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=12   ,	SCREAMING_SNAKE_CASE__     :     List[str]=12   ,	SCREAMING_SNAKE_CASE__     :     int=3_072   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]="gelu"   ,	SCREAMING_SNAKE_CASE__     :     Dict=0.1   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]=0.1   ,	SCREAMING_SNAKE_CASE__     :     Dict=512   ,	SCREAMING_SNAKE_CASE__     :     Any=2   ,	SCREAMING_SNAKE_CASE__     :     int=0.02   ,	SCREAMING_SNAKE_CASE__     :     str=1e-1_2   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=0   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]="absolute"   ,	SCREAMING_SNAKE_CASE__     :     Any=True   ,	SCREAMING_SNAKE_CASE__     :     Dict=1_024   ,	**SCREAMING_SNAKE_CASE__     :     Tuple   ,	)      ->       Optional[Any]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__   ,	**SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= vocab_size
         lowerCAmelCase__		= hidden_size
         lowerCAmelCase__		= num_hidden_layers
         lowerCAmelCase__		= num_attention_heads
         lowerCAmelCase__		= hidden_act
         lowerCAmelCase__		= intermediate_size
         lowerCAmelCase__		= hidden_dropout_prob
         lowerCAmelCase__		= attention_probs_dropout_prob
         lowerCAmelCase__		= max_position_embeddings
         lowerCAmelCase__		= type_vocab_size
         lowerCAmelCase__		= initializer_range
         lowerCAmelCase__		= layer_norm_eps
         lowerCAmelCase__		= position_embedding_type
         lowerCAmelCase__		= use_cache
         lowerCAmelCase__		= max_ad_position_embeddings






class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     PretrainedConfig   ,	SCREAMING_SNAKE_CASE__     :     str = "default"   ,	SCREAMING_SNAKE_CASE__     :     List[PatchingSpec] = None   ,	)      ->       Union[str, Any]:
         super().__init__(SCREAMING_SNAKE_CASE__   ,	task=SCREAMING_SNAKE_CASE__   ,	patching_specs=SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= config.max_ad_position_embeddings - 1


    @property
    def    a      (       self     :     Optional[Any]   )      ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "sequence"}),
                 ("bbox", {0: "batch", 1: "sequence"}),
                 ("attention_mask", {0: "batch", 1: "sequence"}),
                 ("token_type_ids", {0: "batch", 1: "sequence"}),
             ]   )


    def    a      (       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     PreTrainedTokenizer   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     bool = False   ,	SCREAMING_SNAKE_CASE__     :     Optional[TensorType] = None   ,	)      ->       Mapping[str, Any]:
         lowerCAmelCase__		= super().generate_dummy_inputs(
             SCREAMING_SNAKE_CASE__   ,	batch_size=SCREAMING_SNAKE_CASE__   ,	seq_length=SCREAMING_SNAKE_CASE__   ,	is_pair=SCREAMING_SNAKE_CASE__   ,	framework=SCREAMING_SNAKE_CASE__   )

         # Generate a dummy bbox
         lowerCAmelCase__		= [48, 84, 73, 128]

         if not framework == TensorType.PYTORCH:
              raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch."   )

         if not is_torch_available():
              raise ValueError("Cannot generate dummy inputs without PyTorch installed."   )
         import torch

         lowerCAmelCase__	,		lowerCAmelCase__		= input_dict["input_ids"].shape
         lowerCAmelCase__		= torch.tensor([*[box] * seq_length]   ).tile(SCREAMING_SNAKE_CASE__   ,	1   ,	1   )
         return input_dict






from math import floor, sqrt



def 							_A   (						lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= 0.0
     lowerCAmelCase__		= 1.0
     lowerCAmelCase__		= int(sqrt(lowerCAmelCase_			)			)
     lowerCAmelCase__		= root
     lowerCAmelCase__		= 0
     while integer_part != 2 * root:
          lowerCAmelCase__		= denominator * integer_part - numerator
          lowerCAmelCase__		= (n - numerator**2) / denominator
          lowerCAmelCase__		= int((root + numerator) / denominator			)
          period += 1
     return period



def 							_A   (						lowerCAmelCase_     :					int = 1_0000			):
     """simple docstring"""

     lowerCAmelCase__		= 0
     for i in range(2   ,				n + 1			):
          lowerCAmelCase__		= sqrt(lowerCAmelCase_			)
          if sr - floor(lowerCAmelCase_			) != 0 and continuous_fraction_period(lowerCAmelCase_			) % 2 == 1:
               count_odd_periods += 1
     return count_odd_periods


if __name__ == "__main__":
     print(F"""{solution(int(input().strip()))}""")
