
from __future__ import annotations

lowerCamelCase_						     =					tuple[int, int, int]
lowerCamelCase_						     =					tuple[str, str, str]


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

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






def 						__magic_name__			(   __a      :	RotorPositionT	,		__a      :	RotorSelectionT	,		__a      :	str						):



     '''simple docstring'''




     if (unique_rotsel := len(set(__a						)						)) < 3:
          UpperCamelCase__				=    f"Please use 3 unique rotors (not {unique_rotsel})"
          raise Exception(__a						)

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

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

     return rotpos, rotsel, pbdict






def 						__magic_name__			(   __a      :	str						):



     '''simple docstring'''




     if not isinstance(__a	,		__a						):
          UpperCamelCase__				=    f"Plugboard setting isn't type string ({type(__a						)})"
          raise TypeError(__a						)
     elif len(__a						) % 2 != 0:
          UpperCamelCase__				=    f"Odd number of symbols ({len(__a						)})"
          raise Exception(__a						)
     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(__a						)
          elif i in tmppbl:
               UpperCamelCase__				=    f"Duplicate symbol ({i})"
               raise Exception(__a						)
          else:
               tmppbl.add(__a						)
     del tmppbl

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

     return pb






def 						__magic_name__			(   __a      :	str	,		__a      :	RotorPositionT	,		__a      :	RotorSelectionT = (rotora, rotora, rotora)	,		__a      :	str = ""	,		):



     '''simple docstring'''




     UpperCamelCase__				=    text.upper()
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    _validator(
         __a	,		__a	,		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(__a						) + rotorposa
               UpperCamelCase__				=    rotora[index % len(__a						)]

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

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

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

               UpperCamelCase__				=    reflector[symbol]

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

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

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

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

     return "".join(__a						)


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

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







import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    lowerCamelCase_						     =					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    size if size is not None else {"""height""": 20, """width""": 20}
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    size
       UpperCamelCase__				=    do_normalize
       UpperCamelCase__				=    do_convert_rgb
       UpperCamelCase__				=    [5_12, 10_24, 20_48, 40_96]
       UpperCamelCase__				=    patch_size if patch_size is not None else {"""height""": 16, """width""": 16}







  def 	UpperCAmelCase_ (self						):
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
       UpperCamelCase__				=    Image.open(requests.get(SCREAMING_SNAKE_CASE_	,      stream=SCREAMING_SNAKE_CASE_						).raw						).convert("""RGB"""						)
       return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,				reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,				)
@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processor_tester.prepare_dummy_image()

       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       UpperCamelCase__				=    20_48

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,      torch.tensor(0.0606						)	,      atol=1E-3	,      rtol=1E-3						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       UpperCamelCase__				=    True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(SCREAMING_SNAKE_CASE_						):
                 UpperCamelCase__				=    image_processor(
                     image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches

            UpperCamelCase__				=    """Hello"""

            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      torchify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						)

       # Test not batched input
       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,				reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,				)
@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self	,      num_channels=4						)
       UpperCamelCase__				=    3







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )





