



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


_lowerCamelCase         =	logging.get_logger(__name__)

_lowerCamelCase         =	{
    """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    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      """layoutlm"""





     def __init__(							self			:				int  ,	UpperCamelCase			:				Optional[Any]=3_0_5_2_2  ,	UpperCamelCase			:				str=7_6_8  ,	UpperCamelCase			:				str=1_2  ,	UpperCamelCase			:				Optional[Any]=1_2  ,	UpperCamelCase			:				Dict=3_0_7_2  ,	UpperCamelCase			:				Tuple="gelu"  ,	UpperCamelCase			:				int=0.1  ,	UpperCamelCase			:				Union[str, Any]=0.1  ,	UpperCamelCase			:				List[Any]=5_1_2  ,	UpperCamelCase			:				List[str]=2  ,	UpperCamelCase			:				Optional[Any]=0.0_2  ,	UpperCamelCase			:				int=1E-12  ,	UpperCamelCase			:				str=0  ,	UpperCamelCase			:				Optional[int]="absolute"  ,	UpperCamelCase			:				int=True  ,	UpperCamelCase			:				List[Any]=1_0_2_4  ,	**UpperCamelCase			:				Dict  ,	)->int:
      super().__init__(pad_token_id=UpperCamelCase  ,	**UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						vocab_size
      __SCREAMING_SNAKE_CASE							:     Any							=						hidden_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						num_hidden_layers
      __SCREAMING_SNAKE_CASE							:     str							=						num_attention_heads
      __SCREAMING_SNAKE_CASE							:     int							=						hidden_act
      __SCREAMING_SNAKE_CASE							:     Any							=						intermediate_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Tuple							=						max_position_embeddings
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						type_vocab_size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     List[str]							=						layer_norm_eps
      __SCREAMING_SNAKE_CASE							:     int							=						position_embedding_type
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						use_cache
      __SCREAMING_SNAKE_CASE							:     Dict							=						max_ad_position_embeddings






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				PretrainedConfig  ,	UpperCamelCase			:				str = "default"  ,	UpperCamelCase			:				List[PatchingSpec] = None  ,	)->Optional[int]:
      super().__init__(UpperCamelCase  ,	task=UpperCamelCase  ,	patching_specs=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						config.max_ad_position_embeddings - 1





     @property
     def 						__snake_case     (							self			:				Union[str, Any]					)->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 						__snake_case     (							self			:				Tuple  ,	UpperCamelCase			:				PreTrainedTokenizer  ,	UpperCamelCase			:				int = -1  ,	UpperCamelCase			:				int = -1  ,	UpperCamelCase			:				bool = False  ,	UpperCamelCase			:				Optional[TensorType] = None  ,	)->Mapping[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						super().generate_dummy_inputs(
          UpperCamelCase  ,	batch_size=UpperCamelCase  ,	seq_length=UpperCamelCase  ,	is_pair=UpperCamelCase  ,	framework=UpperCamelCase					)

      # Generate a dummy bbox
      __SCREAMING_SNAKE_CASE							:     List[str]							=						[4_8, 8_4, 7_3, 1_2_8]

      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

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     str							=						input_dict["input_ids"].shape
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.tensor([*[box] * seq_length]					).tile(UpperCamelCase  ,	1  ,	1					)
      return input_dict













"""simple docstring"""
from functools import lru_cache






@lru_cache
def    A__							(     UpperCamelCase			):
							if not isinstance(UpperCamelCase   ,    UpperCamelCase			):
														raise TypeError("k must be an integer."			)
							if k < 0:
														raise ValueError("k integer must be greater or equal to zero."			)
							if k == 0:
														return 0
							elif k == 1:
														return 1
							else:
														return fibonacci(k - 1			) + fibonacci(k - 2			)






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    len(UpperCamelCase			)
							# Find m such that F_m >= n where F_i is the i_th fibonacci number.
							A						=    0
							while True:
														if fibonacci(UpperCamelCase			) >= len_list:
																					A						=    i
																					break
														i += 1
							A						=    0
							while fibb_k > 0:
														A						=    min(
														    offset + fibonacci(fibb_k - 1			)   ,    len_list - 1			)  # Prevent out of range
														A						=    arr[index_k]
														if item_k_1 == val:
																					return index_k
														elif val < item_k_1:
																					fibb_k -= 1
														elif val > item_k_1:
																					offset += fibonacci(fibb_k - 1			)
																					fibb_k -= 2
							else:
														return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()


