






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


A__     :      Any             =			logging.get_logger(__name__)

A__     :      str             =			{
    '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 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				lowercase__    							=	"""layoutlm"""




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

								'''simple docstring'''
								super().__init__(pad_token_id=lowerCamelCase,  **lowerCamelCase  )
								lowercase__       			=      vocab_size
								lowercase__       			=      hidden_size
								lowercase__       			=      num_hidden_layers
								lowercase__       			=      num_attention_heads
								lowercase__       			=      hidden_act
								lowercase__       			=      intermediate_size
								lowercase__       			=      hidden_dropout_prob
								lowercase__       			=      attention_probs_dropout_prob
								lowercase__       			=      max_position_embeddings
								lowercase__       			=      type_vocab_size
								lowercase__       			=      initializer_range
								lowercase__       			=      layer_norm_eps
								lowercase__       			=      position_embedding_type
								lowercase__       			=      use_cache
								lowercase__       			=      max_ad_position_embeddings



class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				def __init__(					self						:   Tuple,  lowerCamelCase						:   PretrainedConfig,  lowerCamelCase						:   str = "default",  lowerCamelCase						:   List[PatchingSpec] = None,  ):

								'''simple docstring'''
								super().__init__(lowerCamelCase,  task=lowerCamelCase,  patching_specs=lowerCamelCase  )
								lowercase__       			=      config.max_ad_position_embeddings - 1




				@property
				def     lowercase__       (					self						:   List[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     lowercase__       (					self						:   str,  lowerCamelCase						:   PreTrainedTokenizer,  lowerCamelCase						:   int = -1,  lowerCamelCase						:   int = -1,  lowerCamelCase						:   bool = False,  lowerCamelCase						:   Optional[TensorType] = None,  ):

								'''simple docstring'''
								lowercase__       			=      super().generate_dummy_inputs(
								    lowerCamelCase,  batch_size=lowerCamelCase,  seq_length=lowerCamelCase,  is_pair=lowerCamelCase,  framework=lowerCamelCase  )

								# Generate a dummy bbox
								lowercase__       			=      [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

								lowercase__			,						lowercase__       			=      input_dict['''input_ids'''].shape
								lowercase__       			=      torch.tensor([*[box] * seq_length]  ).tile(lowerCamelCase,  1,  1  )
								return input_dict












from collections import defaultdict






def   a      (						lowerCamelCase_ = 5    ):






				'''simple docstring'''
				lowercase__       			=      defaultdict(lowerCamelCase_    )
				lowercase__       			=      0

				while True:
								lowercase__       			=      get_digits(lowerCamelCase_    )
								freqs[digits].append(lowerCamelCase_    )

								if len(freqs[digits]    ) == max_base:
												lowercase__       			=      freqs[digits][0] ** 3
												return base

								num += 1






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return "".join(sorted(str(num**3    )    )    )


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




