






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


UpperCAmelCase__      			=						logging.get_logger(__name__)

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




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """layoutlmv3"""



						def __init__(self:					str ,					__UpperCAmelCase:					Optional[Any]=50265 ,					__UpperCAmelCase:					Optional[int]=768 ,					__UpperCAmelCase:					int=12 ,					__UpperCAmelCase:					Dict=12 ,					__UpperCAmelCase:					Any=3072 ,					__UpperCAmelCase:					str="gelu" ,					__UpperCAmelCase:					List[str]=0.1 ,					__UpperCAmelCase:					Optional[int]=0.1 ,					__UpperCAmelCase:					int=512 ,					__UpperCAmelCase:					List[str]=2 ,					__UpperCAmelCase:					Tuple=0.02 ,					__UpperCAmelCase:					int=1E-5 ,					__UpperCAmelCase:					Optional[int]=1 ,					__UpperCAmelCase:					Union[str, Any]=0 ,					__UpperCAmelCase:					Dict=2 ,					__UpperCAmelCase:					Dict=1024 ,					__UpperCAmelCase:					Optional[int]=128 ,					__UpperCAmelCase:					Tuple=128 ,					__UpperCAmelCase:					Optional[int]=True ,					__UpperCAmelCase:					Dict=32 ,					__UpperCAmelCase:					List[str]=128 ,					__UpperCAmelCase:					Union[str, Any]=64 ,					__UpperCAmelCase:					Optional[int]=256 ,					__UpperCAmelCase:					int=True ,					__UpperCAmelCase:					List[Any]=True ,					__UpperCAmelCase:					Any=True ,					__UpperCAmelCase:					List[str]=224 ,					__UpperCAmelCase:					Dict=3 ,					__UpperCAmelCase:					List[Any]=16 ,					__UpperCAmelCase:					Optional[Any]=None ,					**__UpperCAmelCase:					str ,					)    ->     Dict:




											'''simple docstring'''



											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 ,					)
											__a					:						Tuple      =					max_ad_position_embeddings
											__a					:						str      =					coordinate_size
											__a					:						Union[str, Any]      =					shape_size
											__a					:						Optional[int]      =					has_relative_attention_bias
											__a					:						Optional[Any]      =					rel_pos_bins
											__a					:						Optional[Any]      =					max_rel_pos
											__a					:						Tuple      =					has_spatial_attention_bias
											__a					:						Union[str, Any]      =					rel_ad_pos_bins
											__a					:						Any      =					max_rel_ad_pos
											__a					:						str      =					text_embed
											__a					:						Dict      =					visual_embed
											__a					:						int      =					input_size
											__a					:						Any      =					num_channels
											__a					:						List[str]      =					patch_size
											__a					:						Optional[Any]      =					classifier_dropout




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



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



						@property
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     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:					str   )    ->     float:




											'''simple docstring'''



											return 1E-5



						@property
						def        UpperCAmelCase__					(self:					str   )    ->     int:




											'''simple docstring'''



											return 12



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					"ProcessorMixin" ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					Optional["TensorType"] = None ,					__UpperCAmelCase:					int = 3 ,					__UpperCAmelCase:					int = 40 ,					__UpperCAmelCase:					int = 40 ,					)    ->     Mapping[str, Any]:




											'''simple docstring'''



											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
											__a					:						Dict      =					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
											__a					:						int      =					processor.tokenizer.num_special_tokens_to_add(__UpperCAmelCase   )
											__a					:						Any      =					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
											__a					:						Tuple      =					[[" ".join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

											# Generate dummy bounding boxes
											__a					:						Tuple      =					[[[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)
											__a					:						str      =					self._generate_dummy_images(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Union[str, Any]      =					dict(
											    processor(
											        __UpperCAmelCase ,					text=__UpperCAmelCase ,					boxes=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					)   )

											return inputs













UpperCAmelCase__      			=						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def     a_   ()				->						None:



					"""simple docstring"""
					__a					:						Dict      =					input("Enter message: "      )
					__a					:						Dict      =					input("Enter key [alphanumeric]: "      )
					__a					:						str      =					input("Encrypt/Decrypt [e/d]: "      )

					if mode.lower().startswith("e"      ):
										__a					:						List[str]      =					"encrypt"
										__a					:						Union[str, Any]      =					encrypt_message(__A				,       __A      )
					elif mode.lower().startswith("d"      ):
										__a					:						int      =					"decrypt"
										__a					:						List[str]      =					decrypt_message(__A				,       __A      )

					print(f'\n{mode.title()}ed message:'      )
					print(__A      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "encrypt"      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "decrypt"      )







def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					[]
					__a					:						Tuple      =					0
					__a					:						Dict      =					key.upper()

					for symbol in message:
										__a					:						Dict      =					LETTERS.find(symbol.upper()      )
										if num != -1:
															if mode == "encrypt":
																				num += LETTERS.find(key[key_index]      )
															elif mode == "decrypt":
																				num -= LETTERS.find(key[key_index]      )

															num %= len(__A      )

															if symbol.isupper():
																				translated.append(LETTERS[num]      )
															elif symbol.islower():
																				translated.append(LETTERS[num].lower()      )

															key_index += 1
															if key_index == len(__A      ):
																				__a					:						List[str]      =					0
										else:
															translated.append(__A      )
					return "".join(__A      )


if __name__ == "__main__":
			main()





