






"""simple docstring"""





from __future__ import annotations

A__					: Dict=	tuple[int, int, int]
A__					: List[str]=	tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
A__					: Union[str, Any]=	"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""

# -------------------------- default selection --------------------------
# rotors --------------------------
A__					: List[Any]=	"""EGZWVONAHDCLFQMSIPJBYUKXTR"""
A__					: List[Any]=	"""FOBHMDKEXQNRAULPGSJVTYICZW"""
A__					: Union[str, Any]=	"""ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
A__					: Optional[Any]=	{
    """A""": """N""",
    """N""": """A""",
    """B""": """O""",
    """O""": """B""",
    """C""": """P""",
    """P""": """C""",
    """D""": """Q""",
    """Q""": """D""",
    """E""": """R""",
    """R""": """E""",
    """F""": """S""",
    """S""": """F""",
    """G""": """T""",
    """T""": """G""",
    """H""": """U""",
    """U""": """H""",
    """I""": """V""",
    """V""": """I""",
    """J""": """W""",
    """W""": """J""",
    """K""": """X""",
    """X""": """K""",
    """L""": """Y""",
    """Y""": """L""",
    """M""": """Z""",
    """Z""": """M""",
}

# -------------------------- extra rotors --------------------------
A__					: Any=	"""RMDJXFUWGISLHVTCQNKYPBEZOA"""
A__					: Optional[int]=	"""SGLCPQWZHKXAREONTFBVIYJUDM"""
A__					: Tuple=	"""HVSICLTYKQUBXDWAJZOMFGPREN"""
A__					: Dict=	"""RZWQHFMVDBKICJLNTUXAGYPSOE"""
A__					: int=	"""LFKIJODBEGAMQPXVUHYSTCZRWN"""
A__					: str=	"""KOAEGVDHXPQZMLFTYWJNBRCIUS"""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
    """simple docstring"""

    if (unique_rotsel := len(set(SCREAMING_SNAKE_CASE					)					)) < 3:
        UpperCamelCase__         =    F'Please use 3 unique rotors (not {unique_rotsel})'
        raise Exception(SCREAMING_SNAKE_CASE					)

    # Checks if rotor positions are valid
    UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    rotpos
    if not 0 < rotorposa <= len(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    F'First rotor position is not within range of 1..26 ({rotorposa}'
        raise ValueError(SCREAMING_SNAKE_CASE					)
    if not 0 < rotorposa <= len(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    F'Second rotor position is not within range of 1..26 ({rotorposa})'
        raise ValueError(SCREAMING_SNAKE_CASE					)
    if not 0 < rotorposa <= len(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    F'Third rotor position is not within range of 1..26 ({rotorposa})'
        raise ValueError(SCREAMING_SNAKE_CASE					)

    # Validates string and returns dict
    UpperCamelCase__         =    _plugboard(SCREAMING_SNAKE_CASE					)

    return rotpos, rotsel, pbdict







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	dict[str, str]:
    """simple docstring"""

    if not isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    F'Plugboard setting isn\'t type string ({type(SCREAMING_SNAKE_CASE					)})'
        raise TypeError(SCREAMING_SNAKE_CASE					)
    elif len(SCREAMING_SNAKE_CASE					) % 2 != 0:
        UpperCamelCase__         =    F'Odd number of symbols ({len(SCREAMING_SNAKE_CASE					)})'
        raise Exception(SCREAMING_SNAKE_CASE					)
    elif pbstring == "":
        return {}

    pbstring.replace(' ' ,	''					)

    # Checks if all characters are unique
    UpperCamelCase__         =    set()
    for i in pbstring:
        if i not in abc:
            UpperCamelCase__         =    F'\'{i}\' not in list of symbols'
            raise Exception(SCREAMING_SNAKE_CASE					)
        elif i in tmppbl:
            UpperCamelCase__         =    F'Duplicate symbol ({i})'
            raise Exception(SCREAMING_SNAKE_CASE					)
        else:
            tmppbl.add(SCREAMING_SNAKE_CASE					)
    del tmppbl

    # Created the dictionary
    UpperCamelCase__         =    {}
    for j in range(0 ,	len(SCREAMING_SNAKE_CASE					) - 1 ,	2					):
        UpperCamelCase__         =    pbstring[j + 1]
        UpperCamelCase__         =    pbstring[j]

    return pb







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = (rotora, rotora, rotora) ,	SCREAMING_SNAKE_CASE = "" ,	)					->	str:
    """simple docstring"""

    UpperCamelCase__         =    text.upper()
    UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    _validator(
        SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	plugb.upper()					)

    UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    rotor_position
    UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    UpperCamelCase__         =    []

    # encryption/decryption process --------------------------
    for symbol in text:
        if symbol in abc:
            # 1st plugboard --------------------------
            if symbol in plugboard:
                UpperCamelCase__         =    plugboard[symbol]

            # rotor ra --------------------------
            UpperCamelCase__         =    abc.index(SCREAMING_SNAKE_CASE					) + rotorposa
            UpperCamelCase__         =    rotora[index % len(SCREAMING_SNAKE_CASE					)]

            # rotor rb --------------------------
            UpperCamelCase__         =    abc.index(SCREAMING_SNAKE_CASE					) + rotorposa
            UpperCamelCase__         =    rotora[index % len(SCREAMING_SNAKE_CASE					)]

            # rotor rc --------------------------
            UpperCamelCase__         =    abc.index(SCREAMING_SNAKE_CASE					) + rotorposa
            UpperCamelCase__         =    rotora[index % len(SCREAMING_SNAKE_CASE					)]

            # reflector --------------------------
            # this is the reason you don't need another machine to decipher

            UpperCamelCase__         =    reflector[symbol]

            # 2nd rotors
            UpperCamelCase__         =    abc[rotora.index(SCREAMING_SNAKE_CASE					) - rotorposa]
            UpperCamelCase__         =    abc[rotora.index(SCREAMING_SNAKE_CASE					) - rotorposa]
            UpperCamelCase__         =    abc[rotora.index(SCREAMING_SNAKE_CASE					) - rotorposa]

            # 2nd plugboard
            if symbol in plugboard:
                UpperCamelCase__         =    plugboard[symbol]

            # moves/resets rotor positions
            rotorposa += 1
            if rotorposa >= len(SCREAMING_SNAKE_CASE					):
                UpperCamelCase__         =    0
                rotorposa += 1
            if rotorposa >= len(SCREAMING_SNAKE_CASE					):
                UpperCamelCase__         =    0
                rotorposa += 1
            if rotorposa >= len(SCREAMING_SNAKE_CASE					):
                UpperCamelCase__         =    0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
        result.append(SCREAMING_SNAKE_CASE					)

    return "".join(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: Tuple=	"""This is my Python script that emulates the Enigma machine from WWII."""
  A__					: Optional[int]=	(1, 1, 1)
  A__					: List[str]=	"""pictures"""
  A__					: Optional[int]=	(rotora, rotora, rotora)
  A__					: Optional[Any]=	enigma(message, rotor_pos, rotor_sel, pb)

  print("""Encrypted message:""", en)
  print("""Decrypted message:""", enigma(en, rotor_pos, rotor_sel, pb))












"""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


A__					: Any=	logging.get_logger(__name__)

A__					: str=	{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class    __lowerCamelCase    (  _a  ):
   a      :				List[str] 						="""layoutlmv3"""

   def __init__(					self ,				snake_case_=5_0265 ,				snake_case_=768 ,				snake_case_=12 ,				snake_case_=12 ,				snake_case_=3072 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=1E-5 ,				snake_case_=1 ,				snake_case_=0 ,				snake_case_=2 ,				snake_case_=1024 ,				snake_case_=128 ,				snake_case_=128 ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=128 ,				snake_case_=64 ,				snake_case_=256 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=224 ,				snake_case_=3 ,				snake_case_=16 ,				snake_case_=None ,				**snake_case_ ,				)		->       Union[str, Any]:
       super().__init__(
           vocab_size=snake_case_ ,				hidden_size=snake_case_ ,				num_hidden_layers=snake_case_ ,				num_attention_heads=snake_case_ ,				intermediate_size=snake_case_ ,				hidden_act=snake_case_ ,				hidden_dropout_prob=snake_case_ ,				attention_probs_dropout_prob=snake_case_ ,				max_position_embeddings=snake_case_ ,				type_vocab_size=snake_case_ ,				initializer_range=snake_case_ ,				layer_norm_eps=snake_case_ ,				pad_token_id=snake_case_ ,				bos_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				**snake_case_ ,				)
       UpperCamelCase__         =    max_ad_position_embeddings
       UpperCamelCase__         =    coordinate_size
       UpperCamelCase__         =    shape_size
       UpperCamelCase__         =    has_relative_attention_bias
       UpperCamelCase__         =    rel_pos_bins
       UpperCamelCase__         =    max_rel_pos
       UpperCamelCase__         =    has_spatial_attention_bias
       UpperCamelCase__         =    rel_ad_pos_bins
       UpperCamelCase__         =    max_rel_ad_pos
       UpperCamelCase__         =    text_embed
       UpperCamelCase__         =    visual_embed
       UpperCamelCase__         =    input_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    classifier_dropout







class    __lowerCamelCase    (  _a  ):
   a      :				Tuple 						=version.parse("""1.12"""  )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Mapping[str, Mapping[int, str]]:
       # 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 						SCREAMING_SNAKE_CASE__ (					self      )		->       float:
       return 1E-5

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return 12







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				snake_case_ = 3 ,				snake_case_ = 40 ,				snake_case_ = 40 ,				)		->       Mapping[str, Any]:
       setattr(processor.image_processor ,				'apply_ocr' ,				snake_case_      )

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

       # Generate dummy bounding boxes
       UpperCamelCase__         =    [[[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)
       UpperCamelCase__         =    self._generate_dummy_images(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    dict(
           processor(
               snake_case_ ,				text=snake_case_ ,				boxes=snake_case_ ,				return_tensors=snake_case_ ,				)      )

       return inputs




