
"""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


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  lowercase				(   lowercase__						):
			lowercase			     =    """layoutlm"""






			def __init__(self     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  List[Any]=30_522			,SCREAMING_SNAKE_CASE_     :  Dict=768			,SCREAMING_SNAKE_CASE_     :  Optional[Any]=12			,SCREAMING_SNAKE_CASE_     :  Any=12			,SCREAMING_SNAKE_CASE_     :  Optional[Any]=3_072			,SCREAMING_SNAKE_CASE_     :  int="gelu"			,SCREAMING_SNAKE_CASE_     :  List[str]=0.1			,SCREAMING_SNAKE_CASE_     :  Dict=0.1			,SCREAMING_SNAKE_CASE_     :  str=512			,SCREAMING_SNAKE_CASE_     :  str=2			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]=0.02			,SCREAMING_SNAKE_CASE_     :  Dict=1e-12			,SCREAMING_SNAKE_CASE_     :  Optional[Any]=0			,SCREAMING_SNAKE_CASE_     :  Optional[Any]="absolute"			,SCREAMING_SNAKE_CASE_     :  int=True			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]=1_024			,**SCREAMING_SNAKE_CASE_     :  Optional[Any]			,)					->  Optional[int]:





				"""simple docstring"""


				super().__init__(pad_token_id=_A			,**_A )
				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  lowercase				(   lowercase__						):






			def __init__(self     :  int			,SCREAMING_SNAKE_CASE_     :  PretrainedConfig			,SCREAMING_SNAKE_CASE_     :  str = "default"			,SCREAMING_SNAKE_CASE_     :  List[PatchingSpec] = None			,)					->  List[Any]:





				"""simple docstring"""


				super().__init__(_A			,task=_A			,patching_specs=_A )
				lowerCAmelCase				       =     config.max_ad_position_embeddings - 1






			@property
			def 				UpperCAmelCase       (self     :  Any )					->  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     :  Tuple			,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]:





				"""simple docstring"""


				lowerCAmelCase				       =     super().generate_dummy_inputs(
				    _A			,batch_size=_A			,seq_length=_A			,is_pair=_A			,framework=_A )

				# 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				       =     input_dict['input_ids'].shape
				lowerCAmelCase				       =     torch.tensor([*[box] * seq_length] ).tile(_A			,1			,1 )
				return input_dict




"""simple docstring"""






def __magic_name__  (							_lowerCamelCase:		int,				_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	lowerCAmelCase				       =     1.0
	for i in range(1,				r + 1 ):
		ret *= (n + 1 - i) / i
	return round(_lowerCamelCase )




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return choose(8 + n,				_lowerCamelCase ) + choose(9 + n,				_lowerCamelCase ) - 10




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return sum(non_bouncy_exact(_lowerCamelCase ) for i in range(1,				n + 1 ) )




def __magic_name__  (							_lowerCamelCase:		int = 100 )				->	int:

	'''simple docstring'''




	return non_bouncy_upto(_lowerCamelCase )


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


