



'''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


_lowercase       :						Optional[Any]						=logging.get_logger(__name__)

_lowercase       :						Dict						={
    "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				(lowercase__      ):
			A__								=  'layoutlm'



			def __init__(     self		:						Any    ,			__UpperCamelCase		:						List[str]=30522    ,			__UpperCamelCase		:						Union[str, Any]=768    ,			__UpperCamelCase		:						Dict=12    ,			__UpperCamelCase		:						Any=12    ,			__UpperCamelCase		:						List[str]=3072    ,			__UpperCamelCase		:						Tuple="gelu"    ,			__UpperCamelCase		:						Optional[int]=0.1    ,			__UpperCamelCase		:						Any=0.1    ,			__UpperCamelCase		:						List[Any]=512    ,			__UpperCamelCase		:						Any=2    ,			__UpperCamelCase		:						str=0.02    ,			__UpperCamelCase		:						Union[str, Any]=1e-12    ,			__UpperCamelCase		:						Optional[int]=0    ,			__UpperCamelCase		:						List[str]="absolute"    ,			__UpperCamelCase		:						int=True    ,			__UpperCamelCase		:						List[Any]=1024    ,			**__UpperCamelCase		:						Any    ,			)      ->      Dict:
							"""simple docstring"""





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






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						List[Any]    ,			__UpperCamelCase		:						PretrainedConfig    ,			__UpperCamelCase		:						str = "default"    ,			__UpperCamelCase		:						List[PatchingSpec] = None    ,			)      ->      Optional[int]:
							"""simple docstring"""





							super().__init__(__UpperCamelCase    ,			task=__UpperCamelCase    ,			patching_specs=__UpperCamelCase			)
							snake_case__      :   Optional[Any]      =    config.max_ad_position_embeddings - 1



			@property
			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Mapping[str, Mapping[int, str]]:
							"""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		:						Tuple    ,			__UpperCamelCase		:						PreTrainedTokenizer    ,			__UpperCamelCase		:						int = -1    ,			__UpperCamelCase		:						int = -1    ,			__UpperCamelCase		:						bool = False    ,			__UpperCamelCase		:						Optional[TensorType] = None    ,			)      ->      Mapping[str, Any]:
							"""simple docstring"""





							snake_case__      :   List[str]      =    super().generate_dummy_inputs(
							    __UpperCamelCase    ,			batch_size=__UpperCamelCase    ,			seq_length=__UpperCamelCase    ,			is_pair=__UpperCamelCase    ,			framework=__UpperCamelCase			)

							# Generate a dummy bbox
							snake_case__      :   Dict      =    [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__      :   int      =    input_dict['''input_ids'''].shape
							snake_case__      :   Tuple      =    torch.tensor([*[box] * seq_length]			).tile(__UpperCamelCase    ,			1    ,			1			)
							return input_dict








from __future__ import annotations

import math
from itertools import permutations

def 		snake_case_   (				lowercase__     :    int     ):






				'''simple docstring'''






				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5       ,     int(math.sqrt(lowercase__     ) + 1     )       ,     6     ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def 		snake_case_   (				lowercase__     :    int = 7     ):






				'''simple docstring'''






				_lowerCAmelCase  						="""""".join(str(lowercase__     ) for i in range(1       ,     n + 1     )     )
				_lowerCAmelCase  						=[int("""""".join(lowercase__     )     ) for i in permutations(lowercase__       ,     lowercase__     )]
				_lowerCAmelCase  						=[num for num in perm_list if is_prime(lowercase__     )]
				return max(lowercase__     ) if pandigitals else 0


if __name__ == "__main__":
		print(F'{solution() = }')

