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	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Dict					     =			'layoutlm'

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		Any=3_0_5_2_2							,      lowerCamelCase							:		List[Any]=7_6_8							,      lowerCamelCase							:		Optional[int]=1_2							,      lowerCamelCase							:		int=1_2							,      lowerCamelCase							:		Union[str, Any]=3_0_7_2							,      lowerCamelCase							:		str="gelu"							,      lowerCamelCase							:		Any=0.1							,      lowerCamelCase							:		Tuple=0.1							,      lowerCamelCase							:		Any=5_1_2							,      lowerCamelCase							:		Union[str, Any]=2							,      lowerCamelCase							:		str=0.0_2							,      lowerCamelCase							:		Optional[int]=1e-12							,      lowerCamelCase							:		str=0							,      lowerCamelCase							:		Tuple="absolute"							,      lowerCamelCase							:		Tuple=True							,      lowerCamelCase							:		Any=1_0_2_4							,      **lowerCamelCase							:		Tuple							,      )			->				Optional[int]:
        super().__init__(pad_token_id=lowerCamelCase							,      **lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	vocab_size
        _UpperCamelCase  :							Optional[int]	    =	hidden_size
        _UpperCamelCase  :							int	    =	num_hidden_layers
        _UpperCamelCase  :							Optional[Any]	    =	num_attention_heads
        _UpperCamelCase  :							Optional[Any]	    =	hidden_act
        _UpperCamelCase  :							Union[str, Any]	    =	intermediate_size
        _UpperCamelCase  :							Union[str, Any]	    =	hidden_dropout_prob
        _UpperCamelCase  :							Optional[int]	    =	attention_probs_dropout_prob
        _UpperCamelCase  :							Optional[int]	    =	max_position_embeddings
        _UpperCamelCase  :							Any	    =	type_vocab_size
        _UpperCamelCase  :							Optional[int]	    =	initializer_range
        _UpperCamelCase  :							List[Any]	    =	layer_norm_eps
        _UpperCamelCase  :							str	    =	position_embedding_type
        _UpperCamelCase  :							Any	    =	use_cache
        _UpperCamelCase  :							Union[str, Any]	    =	max_ad_position_embeddings







class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[Any]							,      lowerCamelCase							:		PretrainedConfig							,      lowerCamelCase							:		str = "default"							,      lowerCamelCase							:		List[PatchingSpec] = None							,      )			->				Dict:
        super().__init__(lowerCamelCase							,      task=lowerCamelCase							,      patching_specs=lowerCamelCase	)
        _UpperCamelCase  :							int	    =	config.max_ad_position_embeddings - 1

 @property
 def        __snake_case       (      self							:		Optional[int]	)			->				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        __snake_case       (      self							:		str							,      lowerCamelCase							:		PreTrainedTokenizer							,      lowerCamelCase							:		int = -1							,      lowerCamelCase							:		int = -1							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		Optional[TensorType] = None							,      )			->				Mapping[str, Any]:
        _UpperCamelCase  :							Optional[Any]	    =	super().generate_dummy_inputs(
            lowerCamelCase							,      batch_size=lowerCamelCase							,      seq_length=lowerCamelCase							,      is_pair=lowerCamelCase							,      framework=lowerCamelCase	)

        # Generate a dummy bbox
        _UpperCamelCase  :							List[Any]	    =	[4_8, 8_4, 7_3, 1_2_8]

        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  :							Tuple	    =	input_dict['input_ids'].shape
        _UpperCamelCase  :							List[str]	    =	torch.tensor([*[box] * seq_length]	).tile(lowerCamelCase							,      1							,      1	)
        return input_dict




def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):
       return n * (2 * n - 1)


def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):
       _UpperCamelCase  :							str	    =	(1 + 24 * n) ** 0.5
       return ((1 + root) / 6) % 1 == 0


def SCREAMING_SNAKE_CASE					(  lowercase_:						int = 144					):
       _UpperCamelCase  :							Optional[Any]	    =	start
       _UpperCamelCase  :							List[str]	    =	hexagonal_num(lowercase_					)
       while not is_pentagonal(lowercase_					):
              n += 1
              _UpperCamelCase  :							Optional[int]	    =	hexagonal_num(lowercase_					)
       return num


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



