






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


lowerCamelCase			:					str         =	logging.get_logger(__name__)

lowerCamelCase			:					Optional[Any]         =	{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}



class 							_UpperCamelCase			(lowercase_			):

      '''simple docstring'''





      snake_case_  =							'''layoutlmv3'''


      def __init__(	self					,						__UpperCamelCase=5_0_2_6_5					,						__UpperCamelCase=7_6_8					,						__UpperCamelCase=1_2					,						__UpperCamelCase=1_2					,						__UpperCamelCase=3_0_7_2					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=2					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-5					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=1_2_8					,						__UpperCamelCase=1_2_8					,						__UpperCamelCase=True					,						__UpperCamelCase=3_2					,						__UpperCamelCase=1_2_8					,						__UpperCamelCase=6_4					,						__UpperCamelCase=2_5_6					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=2_2_4					,						__UpperCamelCase=3					,						__UpperCamelCase=1_6					,						__UpperCamelCase=None					,						**__UpperCamelCase					,						)->	Optional[int]:
           super().__init__(
               vocab_size=__UpperCamelCase					,						hidden_size=__UpperCamelCase					,						num_hidden_layers=__UpperCamelCase					,						num_attention_heads=__UpperCamelCase					,						intermediate_size=__UpperCamelCase					,						hidden_act=__UpperCamelCase					,						hidden_dropout_prob=__UpperCamelCase					,						attention_probs_dropout_prob=__UpperCamelCase					,						max_position_embeddings=__UpperCamelCase					,						type_vocab_size=__UpperCamelCase					,						initializer_range=__UpperCamelCase					,						layer_norm_eps=__UpperCamelCase					,						pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase					,						)
           __lowerCAmelCase								=					max_ad_position_embeddings
           __lowerCAmelCase								=					coordinate_size
           __lowerCAmelCase								=					shape_size
           __lowerCAmelCase								=					has_relative_attention_bias
           __lowerCAmelCase								=					rel_pos_bins
           __lowerCAmelCase								=					max_rel_pos
           __lowerCAmelCase								=					has_spatial_attention_bias
           __lowerCAmelCase								=					rel_ad_pos_bins
           __lowerCAmelCase								=					max_rel_ad_pos
           __lowerCAmelCase								=					text_embed
           __lowerCAmelCase								=					visual_embed
           __lowerCAmelCase								=					input_size
           __lowerCAmelCase								=					num_channels
           __lowerCAmelCase								=					patch_size
           __lowerCAmelCase								=					classifier_dropout



class 							_UpperCamelCase			(lowercase_			):

      '''simple docstring'''





      snake_case_  =							version.parse("""1.12"""			)


      @property
      def  __UpperCAmelCase		(	self	)->	Tuple:
           # The order of inputs is different for question answering and sequence classification
           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	)->	Optional[Any]:
           return 1e-5


      @property
      def  __UpperCAmelCase		(	self	)->	List[str]:
           return 1_2


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						__UpperCamelCase = 3					,						__UpperCamelCase = 4_0					,						__UpperCamelCase = 4_0					,						)->	List[Any]:
           setattr(processor.image_processor					,						"apply_ocr"					,						__UpperCamelCase	)

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

           # Generate dummy bounding boxes
           __lowerCAmelCase								=					[[[4_8, 8_4, 7_3, 1_2_8]]] * 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)
           __lowerCAmelCase								=					self._generate_dummy_images(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           __lowerCAmelCase								=					dict(
               processor(
                   __UpperCamelCase					,						text=__UpperCamelCase					,						boxes=__UpperCamelCase					,						return_tensors=__UpperCamelCase					,						)	)

           return inputs














import random



class 							_UpperCamelCase			:


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase	)->	tuple[list[int], list[int]]:
           __lowerCAmelCase								=					[ord(__UpperCamelCase	) for i in text]
           __lowerCAmelCase								=					[]
           __lowerCAmelCase								=					[]
           for i in plain:
                __lowerCAmelCase								=					random.randint(1					,						3_0_0	)
                __lowerCAmelCase								=					(i + k) * k
                cipher.append(__UpperCamelCase	)
                key.append(__UpperCamelCase	)
           return cipher, key


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					[]
           for i in range(len(__UpperCamelCase	)	):
                __lowerCAmelCase								=					int((cipher[i] - (key[i]) ** 2) / key[i]	)
                plain.append(chr(__UpperCamelCase	)	)
           return "".join(__UpperCamelCase	)


if __name__ == "__main__":
 lowerCamelCase      ,					lowerCamelCase			:					List[Any]         =	Onepad().encrypt('''Hello''')
 print(c, k)
 print(Onepad().decrypt(c, k))






