


"""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__							):
   __UpperCAmelCase       					=				"""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-1_2			,      SCREAMING_SNAKE_CASE=0			,      SCREAMING_SNAKE_CASE="absolute"			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=1024			,      **SCREAMING_SNAKE_CASE			,      )     ->     Optional[int]:
      super().__init__(pad_token_id=_A			,      **_A)
      _lowerCamelCase  :				str      =		vocab_size
      _lowerCamelCase  :				Optional[int]      =		hidden_size
      _lowerCamelCase  :				Optional[Any]      =		num_hidden_layers
      _lowerCamelCase  :				List[Any]      =		num_attention_heads
      _lowerCamelCase  :				Any      =		hidden_act
      _lowerCamelCase  :				List[str]      =		intermediate_size
      _lowerCamelCase  :				Optional[int]      =		hidden_dropout_prob
      _lowerCamelCase  :				Optional[int]      =		attention_probs_dropout_prob
      _lowerCamelCase  :				Union[str, Any]      =		max_position_embeddings
      _lowerCamelCase  :				Union[str, Any]      =		type_vocab_size
      _lowerCamelCase  :				Dict      =		initializer_range
      _lowerCamelCase  :				List[Any]      =		layer_norm_eps
      _lowerCamelCase  :				Tuple      =		position_embedding_type
      _lowerCamelCase  :				Union[str, Any]      =		use_cache
      _lowerCamelCase  :				List[str]      =		max_ad_position_embeddings







class   lowercase__							(				lowercase__							):





   def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = "default"			,      SCREAMING_SNAKE_CASE = None			,      )     ->     List[Any]:
      super().__init__(_A			,      task=_A			,      patching_specs=_A)
      _lowerCamelCase  :				int      =		config.max_ad_position_embeddings - 1





   @property
   def 		UpperCamelCase_     (       self)     ->     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 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = -1			,      SCREAMING_SNAKE_CASE = -1			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = None			,      )     ->     Mapping[str, Any]:
      _lowerCamelCase  :				List[str]      =		super().generate_dummy_inputs(
          _A			,      batch_size=_A			,      seq_length=_A			,      is_pair=_A			,      framework=_A)

      # Generate a dummy bbox
      _lowerCamelCase  :				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

      _lowerCamelCase  :				Dict      =		input_dict['input_ids'].shape
      _lowerCamelCase  :				List[Any]      =		torch.tensor([*[box] * seq_length]).tile(_A			,      1			,      1)
      return input_dict







"""simple docstring"""
from itertools import permutations

def 							_snake_case			(      __snake_case       :			int = 5      ):




   """simple docstring"""







   if gon_side < 3 or gon_side > 5:
      raise ValueError("""gon_side must be in the range [3, 5]"""      )

   # Since it's 16, we know 10 is on the outer ring
   # Put the big numbers at the end so that they are never the first number
   _lowerCamelCase  :				Dict      =		list(range(gon_side + 1	,   0	,   -1      )      )
   _lowerCamelCase  :				Optional[int]      =		list(range(gon_side + 2	,   gon_side * 2 + 1      )      )

   for perm in permutations(small_numbers + big_numbers      ):
      _lowerCamelCase  :				List[str]      =		generate_gon_ring(__snake_case	,   list(__snake_case      )      )
      if is_magic_gon(__snake_case      ):
         return int("""""".join(str(__snake_case      ) for n in numbers      )      )

   _lowerCamelCase  :				Union[str, Any]      =		F'Magic {gon_side}-gon ring is impossible'
   raise ValueError(__snake_case      )

def 							_snake_case			(      __snake_case       :			int	,   __snake_case       :			list[int]      ):




   """simple docstring"""







   _lowerCamelCase  :				Union[str, Any]      =		[0] * (gon_side * 3)
   _lowerCamelCase  :				Dict      =		perm[0:3]
   perm.append(perm[1]      )

   _lowerCamelCase  :				Tuple      =		1 if gon_side < 5 else 2

   for i in range(1	,   len(__snake_case      ) // 3 + magic_number      ):
      _lowerCamelCase  :				Dict      =		perm[2 * i + 1]
      _lowerCamelCase  :				str      =		result[3 * i - 1]
      _lowerCamelCase  :				Dict      =		perm[2 * i + 2]

   return result

def 							_snake_case			(      __snake_case       :			list[int]      ):




   """simple docstring"""







   if len(__snake_case      ) % 3 != 0:
      raise ValueError("""a gon ring should have a length that is a multiple of 3"""      )

   if min(numbers[::3]      ) != numbers[0]:
      return False

   _lowerCamelCase  :				str      =		sum(numbers[:3]      )

   return all(sum(numbers[i : i + 3]      ) == total for i in range(3	,   len(__snake_case      )	,   3      )      )


if __name__ == "__main__":
 print(solution())

