
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


lowercase						:      Dict							=							logging.get_logger(__name__)

lowercase						:      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 __snake_case	(   lowerCAmelCase  ):
    _a	:		List[str]=       "layoutlm"
    def __init__(						self	,snake_case=30522	,snake_case=768	,snake_case=12	,snake_case=12	,snake_case=3072	,snake_case="gelu"	,snake_case=0.1	,snake_case=0.1	,snake_case=512	,snake_case=2	,snake_case=0.02	,snake_case=1e-12	,snake_case=0	,snake_case="absolute"	,snake_case=True	,snake_case=1024	,**snake_case	,):



           '''simple docstring'''







           super().__init__(pad_token_id=snake_case	,**snake_case   )
           lowercase							:  Tuple 			=   vocab_size
           lowercase							:  int 			=   hidden_size
           lowercase							:  str 			=   num_hidden_layers
           lowercase							:  Tuple 			=   num_attention_heads
           lowercase							:  Tuple 			=   hidden_act
           lowercase							:  List[str] 			=   intermediate_size
           lowercase							:  Any 			=   hidden_dropout_prob
           lowercase							:  Union[str, Any] 			=   attention_probs_dropout_prob
           lowercase							:  str 			=   max_position_embeddings
           lowercase							:  int 			=   type_vocab_size
           lowercase							:  Tuple 			=   initializer_range
           lowercase							:  str 			=   layer_norm_eps
           lowercase							:  Tuple 			=   position_embedding_type
           lowercase							:  Optional[int] 			=   use_cache
           lowercase							:  Optional[int] 			=   max_ad_position_embeddings






class __snake_case	(   lowerCAmelCase  ):
    def __init__(						self	,snake_case	,snake_case = "default"	,snake_case = None	,):



           '''simple docstring'''







           super().__init__(snake_case	,task=snake_case	,patching_specs=snake_case   )
           lowercase							:  str 			=   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	,snake_case	,snake_case = -1	,snake_case = -1	,snake_case = False	,snake_case = None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   super().generate_dummy_inputs(
               snake_case	,batch_size=snake_case	,seq_length=snake_case	,is_pair=snake_case	,framework=snake_case   )

           # Generate a dummy bbox
           lowercase							:  Optional[Any] 			=   [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							:  Union[str, Any] 			=   input_dict["""input_ids"""].shape
           lowercase							:  int 			=   torch.tensor([*[box] * seq_length]   ).tile(snake_case	,1	,1   )
           return input_dict


from __future__ import annotations







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					bool:
       lowercase							:  int
       lowercase							:  list[str] 			=   list(SCREAMING_SNAKE_CASE__       )
       lowercase			,    lowercase							:  Union[str, Any] 			=   _vector[:-1], int(_vector[-1]       )
       lowercase							:  list[int] 			=   [int(SCREAMING_SNAKE_CASE__       ) for digit in __vector]

       vector.reverse()
       for i, digit in enumerate(SCREAMING_SNAKE_CASE__       ):
              if i & 1 == 0:
                     lowercase							:  int 			=   digit * 2
                     if doubled > 9:
                            doubled -= 9
                     check_digit += doubled
              else:
                     check_digit += digit

       return check_digit % 10 == 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  assert is_luhn("""79927398713""")
  assert not is_luhn("""79927398714""")
