





'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}






class 	__lowercase    ( __lowercase  ):







	'''simple docstring'''

	a      :			Dict     =    "layoutlmv3"


	def __init__(self       ,_lowerCamelCase=50265       ,_lowerCamelCase=768       ,_lowerCamelCase=12       ,_lowerCamelCase=12       ,_lowerCamelCase=3072       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.1       ,_lowerCamelCase=512       ,_lowerCamelCase=2       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-5       ,_lowerCamelCase=1       ,_lowerCamelCase=0       ,_lowerCamelCase=2       ,_lowerCamelCase=1024       ,_lowerCamelCase=128       ,_lowerCamelCase=128       ,_lowerCamelCase=True       ,_lowerCamelCase=32       ,_lowerCamelCase=128       ,_lowerCamelCase=64       ,_lowerCamelCase=256       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=224       ,_lowerCamelCase=3       ,_lowerCamelCase=16       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							Dict:







				'''simple docstring'''





				super().__init__(
				    vocab_size=_a       ,hidden_size=_a       ,num_hidden_layers=_a       ,num_attention_heads=_a       ,intermediate_size=_a       ,hidden_act=_a       ,hidden_dropout_prob=_a       ,attention_probs_dropout_prob=_a       ,max_position_embeddings=_a       ,type_vocab_size=_a       ,initializer_range=_a       ,layer_norm_eps=_a       ,pad_token_id=_a       ,bos_token_id=_a       ,eos_token_id=_a       ,**_a       ,)
				__lowercase			  =							max_ad_position_embeddings
				__lowercase			  =							coordinate_size
				__lowercase			  =							shape_size
				__lowercase			  =							has_relative_attention_bias
				__lowercase			  =							rel_pos_bins
				__lowercase			  =							max_rel_pos
				__lowercase			  =							has_spatial_attention_bias
				__lowercase			  =							rel_ad_pos_bins
				__lowercase			  =							max_rel_ad_pos
				__lowercase			  =							text_embed
				__lowercase			  =							visual_embed
				__lowercase			  =							input_size
				__lowercase			  =							num_channels
				__lowercase			  =							patch_size
				__lowercase			  =							classifier_dropout






class 	__lowercase    ( __lowercase  ):







	'''simple docstring'''

	a      :			List[Any]     =    version.parse("1.12"  )


	@property
	def   _UpperCAmelCase       (self	)							->							Mapping[str, Mapping[int, str]]:







				'''simple docstring'''





				if self.task in ["question-answering", "sequence-classification"]:
							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
							        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]	)
				else:
							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
							    ]	)


	@property
	def   _UpperCAmelCase       (self	)							->							float:







				'''simple docstring'''





				return 1E-5


	@property
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				return 12


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = -1       ,_lowerCamelCase = -1       ,_lowerCamelCase = False       ,_lowerCamelCase = None       ,_lowerCamelCase = 3       ,_lowerCamelCase = 40       ,_lowerCamelCase = 40       ,)							->							Mapping[str, Any]:







				'''simple docstring'''





				setattr(processor.image_processor       ,'''apply_ocr'''       ,_a	)

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				__lowercase			  =							compute_effective_axis_dimension(
				    _a       ,fixed_dimension=OnnxConfig.default_fixed_batch       ,num_token_to_add=0	)
				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				__lowercase			  =							processor.tokenizer.num_special_tokens_to_add(_a	)
				__lowercase			  =							compute_effective_axis_dimension(
				    _a       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=_a	)
				# Generate dummy inputs according to compute batch and sequence
				__lowercase			  =							[[''' '''.join([processor.tokenizer.unk_token]	) * seq_length]] * batch_size

				# Generate dummy bounding boxes
				__lowercase			  =							[[[48, 84, 73, 128]]] * batch_size

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
				__lowercase			  =							self._generate_dummy_images(_a       ,_a       ,_a       ,_a	)

				__lowercase			  =							dict(
				    processor(
				        _a       ,text=_a       ,boxes=_a       ,return_tensors=_a       ,)	)

				return inputs












'''simple docstring'''



import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812




def 				_lowerCAmelCase  (			):
			print('''Making key files...'''			)
			make_key_files('''rsa'''						,    1_0_2_4			)
			print('''Key files generation successful.'''			)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					int			):
			print('''Generating prime p...'''			)
			__lowercase			  =							rabinMiller.generate_large_prime(lowerCamelCase_			)
			print('''Generating prime q...'''			)
			__lowercase			  =							rabinMiller.generate_large_prime(lowerCamelCase_			)
			__lowercase			  =							p * q

			print('''Generating e that is relatively prime to (p - 1) * (q - 1)...'''			)
			while True:
						__lowercase			  =							random.randrange(2 ** (key_size - 1)						,    2 ** (key_size)			)
						if cryptoMath.gcd(lowerCamelCase_						,    (p - 1) * (q - 1)			) == 1:
									break

			print('''Calculating d that is mod inverse of e...'''			)
			__lowercase			  =							cryptoMath.find_mod_inverse(lowerCamelCase_						,    (p - 1) * (q - 1)			)

			__lowercase			  =							(n, e)
			__lowercase			  =							(n, d)
			return (public_key, private_key)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					int			):
			if os.path.exists(f"{name}_pubkey.txt"			) or os.path.exists(f"{name}_privkey.txt"			):
						print('''\nWARNING:'''			)
						print(
						    f"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
						    '''Use a different name or delete these files and re-run this program.'''			)
						sys.exit()

			__lowercase  ,						__lowercase			  =							generate_key(lowerCamelCase_			)
			print(f"\nWriting public key to file {name}_pubkey.txt..."			)
			with open(f"{name}_pubkey.txt"						,    '''w'''			) as out_file:
						out_file.write(f"{key_size},{public_key[0]},{public_key[1]}"			)

			print(f"Writing private key to file {name}_privkey.txt..."			)
			with open(f"{name}_privkey.txt"						,    '''w'''			) as out_file:
						out_file.write(f"{key_size},{private_key[0]},{private_key[1]}"			)


if __name__ == "__main__":
	main()





