'''simple docstring'''

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


_lowerCamelCase :				Dict														= logging.get_logger(__name__)

_lowerCamelCase :				Any														= {
    "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__  (   _SCREAMING_SNAKE_CASE     ):
   __UpperCAmelCase           =						"""layoutlm"""



   def __init__(    self					,      lowerCAmelCase__=30_522					,      lowerCAmelCase__=768					,      lowerCAmelCase__=12					,      lowerCAmelCase__=12					,      lowerCAmelCase__=3_072					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=512					,      lowerCAmelCase__=2					,      lowerCAmelCase__=0.02					,      lowerCAmelCase__=1e-12					,      lowerCAmelCase__=0					,      lowerCAmelCase__="absolute"					,      lowerCAmelCase__=True					,      lowerCAmelCase__=1_024					,      **lowerCAmelCase__					,      )				->     str:
        """simple docstring"""





        super().__init__(pad_token_id=lowerCAmelCase__					,      **lowerCAmelCase__     )
        _UpperCamelCase  :str       =vocab_size
        _UpperCamelCase  :Optional[int]       =hidden_size
        _UpperCamelCase  :str       =num_hidden_layers
        _UpperCamelCase  :str       =num_attention_heads
        _UpperCamelCase  :Optional[Any]       =hidden_act
        _UpperCamelCase  :List[Any]       =intermediate_size
        _UpperCamelCase  :Union[str, Any]       =hidden_dropout_prob
        _UpperCamelCase  :List[Any]       =attention_probs_dropout_prob
        _UpperCamelCase  :List[str]       =max_position_embeddings
        _UpperCamelCase  :Any       =type_vocab_size
        _UpperCamelCase  :int       =initializer_range
        _UpperCamelCase  :List[str]       =layer_norm_eps
        _UpperCamelCase  :Dict       =position_embedding_type
        _UpperCamelCase  :Optional[int]       =use_cache
        _UpperCamelCase  :Tuple       =max_ad_position_embeddings




class    lowerCamelCase__  (   _SCREAMING_SNAKE_CASE     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = "default"					,      lowerCAmelCase__ = None					,      )				->     int:
        """simple docstring"""





        super().__init__(lowerCAmelCase__					,      task=lowerCAmelCase__					,      patching_specs=lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =config.max_ad_position_embeddings - 1



   @property
   def  _UpperCamelCase		(    self     )				->     Mapping[str, Mapping[int, str]]:
        """simple docstring"""





        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  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = -1					,      lowerCAmelCase__ = -1					,      lowerCAmelCase__ = False					,      lowerCAmelCase__ = None					,      )				->     Mapping[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =super().generate_dummy_inputs(
            lowerCAmelCase__					,      batch_size=lowerCAmelCase__					,      seq_length=lowerCAmelCase__					,      is_pair=lowerCAmelCase__					,      framework=lowerCAmelCase__     )

        # Generate a dummy bbox
        _UpperCamelCase  :Tuple       =[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

        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =input_dict["""input_ids"""].shape
        _UpperCamelCase  :List[Any]       =torch.tensor([*[box] * seq_length]     ).tile(lowerCAmelCase__					,      1					,      1     )
        return input_dict



'''simple docstring'''






import math
from decimal import Decimal, getcontext






def 						_A					(lowerCAmelCase__	:int = 4_00_00_00		) ->	int:




     '''simple docstring'''



     try:
          _a         =				int(lowerCAmelCase__		)
     except (TypeError, ValueError):
          raise TypeError('Parameter n must be int or castable to int.'		)
     if n <= 0:
          raise ValueError('Parameter n must be greater than or equal to one.'		)
     _a         =				1_00
     _a         =				(Decimal(5		) ** Decimal(0.5		) + 1) / Decimal(2		)

     _a         =				(math.floor(math.log(n * (phi + 2)					,    lowerCAmelCase__		) - 1		) // 3) * 3 + 2
     _a         =				Decimal(round(phi ** Decimal(index + 1		)		)		) / (phi + 2)
     _a         =				num // 2
     return int(lowerCAmelCase__		)


if __name__ == "__main__":
 print(f'''{solution() = }''')

