




"""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


__A							=     logging.get_logger(__name__)

__A							=     {
    "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__      (					lowerCamelCase_     ):
			a__	: Any        =       """layoutlm"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=30_522			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	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-12			,	SCREAMING_SNAKE_CASE=0			,	SCREAMING_SNAKE_CASE="absolute"			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=1_024			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







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







class      lowerCamelCase__      (					lowerCamelCase_     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = "default"			,	SCREAMING_SNAKE_CASE = None			,	):



					"""simple docstring"""







					super().__init__(SCREAMING_SNAKE_CASE			,	task=SCREAMING_SNAKE_CASE			,	patching_specs=SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[int]        =			config.max_ad_position_embeddings - 1


			@property
			def 							lowerCamelCase_			(       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 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = -1			,	SCREAMING_SNAKE_CASE = -1			,	SCREAMING_SNAKE_CASE = False			,	SCREAMING_SNAKE_CASE = None			,	):



					"""simple docstring"""







					snake_case						:  str        =			super().generate_dummy_inputs(
					    SCREAMING_SNAKE_CASE			,	batch_size=SCREAMING_SNAKE_CASE			,	seq_length=SCREAMING_SNAKE_CASE			,	is_pair=SCREAMING_SNAKE_CASE			,	framework=SCREAMING_SNAKE_CASE							)

					# Generate a dummy bbox
					snake_case						:  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

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







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       set    ,       lowercase__					:       set				):
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_a={set_a}] is not a set'''
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_b={set_b}] is not a set'''

		return {a + b for a in set_a for b in set_b}


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

							testmod()

