


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 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			"layoutlm"


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







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase = "default"			,						__lowerCamelCase = None			,						)      ->    Tuple:
            super().__init__(__lowerCamelCase			,						task=__lowerCamelCase			,						patching_specs=__lowerCamelCase)
            _A				:	int       =			config.max_ad_position_embeddings - 1


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

            # Generate a dummy bbox
            _A				:	List[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

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




def 							_UpperCAmelCase    (UpperCamelCase__					:  str						):
       _A				:	Tuple       =			str(UpperCamelCase__						).strip()
       if not bin_string:
              raise ValueError("Empty string was passed to the function"						)
       _A				:	Dict       =			bin_string[0] == "-"
       if is_negative:
              _A				:	Any       =			bin_string[1:]
       if not all(char in "01" for char in bin_string						):
              raise ValueError("Non-binary value was passed to the function"						)
       _A				:	Union[str, Any]       =			0
       for char in bin_string:
              _A				:	Union[str, Any]       =			2 * decimal_number + int(UpperCamelCase__						)
       return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()
