


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


_snake_case   = logging.get_logger(__name__)

_snake_case   = {
    "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    A_   ( a_     ):







 """simple docstring"""





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




class    A_   ( a_     ):







 """simple docstring"""





 def __init__(			self					: Tuple       ,    _a					: PretrainedConfig       ,    _a					: str = "default"       ,    _a					: List[PatchingSpec] = None       ,    )    ->			List[str]:
      super().__init__(_a       ,    task=_a       ,    patching_specs=_a				)
      UpperCAmelCase						:				Optional[Any]		  =			config.max_ad_position_embeddings - 1
 @property
 def   _UpperCamelCase    (			self					: 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   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: PreTrainedTokenizer       ,    _a					: int = -1       ,    _a					: int = -1       ,    _a					: bool = False       ,    _a					: Optional[TensorType] = None       ,    )    ->			Mapping[str, Any]:
      UpperCAmelCase						:				List[Any]		  =			super().generate_dummy_inputs(
          _a       ,    batch_size=_a       ,    seq_length=_a       ,    is_pair=_a       ,    framework=_a				)

      # Generate a dummy bbox
      UpperCAmelCase						:				Optional[Any]		  =			[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

      UpperCAmelCase  , UpperCAmelCase						:				Union[str, Any]		  =			input_dict["input_ids"].shape
      UpperCAmelCase						:				Any		  =			torch.tensor([*[box] * seq_length]				).tile(_a       ,    1       ,    1				)
      return input_dict







from collections.abc import Callable

_snake_case   = Callable[[float], float]  # type alias for a real -> real function







def   A   (		A__		,		A__		,		A__		,		):
     UpperCAmelCase						:				Any		  =			float(A__ )
     while True:
          try:
               UpperCAmelCase						:				Optional[Any]		  =			prev_guess - function(A__ ) / derivative(A__ )
          except ZeroDivisionError:
               raise ZeroDivisionError("Could not find root" ) from None
          if abs(prev_guess - next_guess ) < 1_0**-5:
               return next_guess
          UpperCAmelCase						:				List[str]		  =			next_guess







def   A   (		A__ ):
     return (x**3) - (2 * x) - 5







def   A   (		A__ ):
     return 3 * (x**2) - 2


if __name__ == "__main__":
      print(newton(f, fa, 3))



