






"""simple docstring"""


from __future__ import annotations

lowercase_				     =     tuple[int, int, int]
lowercase_				     =     tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowercase_				     =     'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
lowercase_				     =     'EGZWVONAHDCLFQMSIPJBYUKXTR'
lowercase_				     =     'FOBHMDKEXQNRAULPGSJVTYICZW'
lowercase_				     =     'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
lowercase_				     =     {
    '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 --------------------------
lowercase_				     =     'RMDJXFUWGISLHVTCQNKYPBEZOA'
lowercase_				     =     'SGLCPQWZHKXAREONTFBVIYJUDM'
lowercase_				     =     'HVSICLTYKQUBXDWAJZOMFGPREN'
lowercase_				     =     'RZWQHFMVDBKICJLNTUXAGYPSOE'
lowercase_				     =     'LFKIJODBEGAMQPXVUHYSTCZRWN'
lowercase_				     =     'KOAEGVDHXPQZMLFTYWJNBRCIUS'



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  if (unique_rotsel := len(set(__UpperCamelCase ) )) < 3:
    __A									= f'Please use 3 unique rotors (not {unique_rotsel})'
    raise Exception(__UpperCamelCase )

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

  # Validates string and returns dict
  __A									= _plugboard(__UpperCamelCase )

  return rotpos, rotsel, pbdict



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







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

  pbstring.replace(''' '''		,       '''''' )

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

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

  return pb



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase = (rotora, rotora, rotora)		,       __UpperCamelCase = ""		,       ):




  """simple docstring"""







  __A									= text.upper()
  __A			,    __A			,    __A									= _validator(
      __UpperCamelCase		,       __UpperCamelCase		,       plugb.upper() )

  __A			,    __A			,    __A									= rotor_position
  __A			,    __A			,    __A									= rotor_selection
  rotorposa -= 1
  rotorposa -= 1
  rotorposa -= 1

  __A									= []

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

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

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

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

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

      __A									= reflector[symbol]

      # 2nd rotors
      __A									= abc[rotora.index(__UpperCamelCase ) - rotorposa]
      __A									= abc[rotora.index(__UpperCamelCase ) - rotorposa]
      __A									= abc[rotora.index(__UpperCamelCase ) - rotorposa]

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

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

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

  return "".join(__UpperCamelCase )


if __name__ == "__main__":
       lowercase_				     =     'This is my Python script that emulates the Enigma machine from WWII.'
       lowercase_				     =     (1, 1, 1)
       lowercase_				     =     'pictures'
       lowercase_				     =     (rotora, rotora, rotora)
       lowercase_				     =     enigma(message, rotor_pos, rotor_sel, pb)

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












"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }





@require_torch
@require_vision
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''size'''  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       np.ndarray  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       torch.Tensor  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )




