





"""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


a__					:     List[Any]							=				logging.get_logger(__name__)

a__					:     Dict							=				{
    """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_				(     __lowerCAmelCase							):
       snake_case__				:							Union[str, Any]											=     """layoutlm"""

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3_0522   ,					__SCREAMING_SNAKE_CASE=768   ,					__SCREAMING_SNAKE_CASE=12   ,					__SCREAMING_SNAKE_CASE=12   ,					__SCREAMING_SNAKE_CASE=3072   ,					__SCREAMING_SNAKE_CASE="gelu"   ,					__SCREAMING_SNAKE_CASE=0.1   ,					__SCREAMING_SNAKE_CASE=0.1   ,					__SCREAMING_SNAKE_CASE=512   ,					__SCREAMING_SNAKE_CASE=2   ,					__SCREAMING_SNAKE_CASE=0.02   ,					__SCREAMING_SNAKE_CASE=1e-12   ,					__SCREAMING_SNAKE_CASE=0   ,					__SCREAMING_SNAKE_CASE="absolute"   ,					__SCREAMING_SNAKE_CASE=True   ,					__SCREAMING_SNAKE_CASE=1024   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				vocab_size
        snake_case__    							=				hidden_size
        snake_case__    							=				num_hidden_layers
        snake_case__    							=				num_attention_heads
        snake_case__    							=				hidden_act
        snake_case__    							=				intermediate_size
        snake_case__    							=				hidden_dropout_prob
        snake_case__    							=				attention_probs_dropout_prob
        snake_case__    							=				max_position_embeddings
        snake_case__    							=				type_vocab_size
        snake_case__    							=				initializer_range
        snake_case__    							=				layer_norm_eps
        snake_case__    							=				position_embedding_type
        snake_case__    							=				use_cache
        snake_case__    							=				max_ad_position_embeddings



class      lowercase_				(     __lowerCAmelCase							):

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = "default"   ,					__SCREAMING_SNAKE_CASE = None   ,					):






        '''simple docstring'''

        super().__init__(__SCREAMING_SNAKE_CASE   ,					task=__SCREAMING_SNAKE_CASE   ,					patching_specs=__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				config.max_ad_position_embeddings - 1

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''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 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = False   ,					__SCREAMING_SNAKE_CASE = None   ,					):






        '''simple docstring'''

        snake_case__    							=				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
        snake_case__    							=				[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

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








"""simple docstring"""


def 							a__			(  _UpperCAmelCase = 1_0_0_0	)    ->  int:

 '''simple docstring'''






 snake_case__    							=				[]
 snake_case__    							=				[]
 snake_case__    							=				3
 snake_case__    							=				5
 snake_case__    							=				1
 while True:
  snake_case__    							=				z * temp
  if result < n:
   zmulti.append(_UpperCAmelCase	)
   temp += 1
  else:
   snake_case__    							=				1
   break
 while True:
  snake_case__    							=				x * temp
  if result < n:
   xmulti.append(_UpperCAmelCase	)
   temp += 1
  else:
   break
 snake_case__    							=				list(set(xmulti + zmulti	)	)
 return sum(_UpperCAmelCase	)


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

