'''simple docstring'''






from __future__ import annotations

SCREAMING_SNAKE_CASE_	   =			tuple[int, int, int]
SCREAMING_SNAKE_CASE_	   =			tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
SCREAMING_SNAKE_CASE_	   =			"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

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






def  UpperCamelCase__					(					_lowercase    :				RotorPositionT   ,    _lowercase    :				RotorSelectionT   ,    _lowercase    :				str     )							->	str:



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

  # Checks if rotor positions are valid
  __UpperCAmelCase:					Union[str, Any]		= rotpos
  if not 0 < rotorposa <= len(_lowercase     ):
    __UpperCAmelCase:					Tuple		= F'''First rotor position is not within range of 1..26 ({rotorposa}'''
    raise ValueError(_lowercase     )
  if not 0 < rotorposa <= len(_lowercase     ):
    __UpperCAmelCase:					str		= F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
    raise ValueError(_lowercase     )
  if not 0 < rotorposa <= len(_lowercase     ):
    __UpperCAmelCase:					Tuple		= F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
    raise ValueError(_lowercase     )

  # Validates string and returns dict
  __UpperCAmelCase:					Any		= _plugboard(_lowercase     )

  return rotpos, rotsel, pbdict






def  UpperCamelCase__					(					_lowercase    :				str     )							->	Optional[int]:



  if not isinstance(_lowercase   ,    _lowercase     ):
    __UpperCAmelCase:					Optional[Any]		= F'''Plugboard setting isn\'t type string ({type(_lowercase     )})'''
    raise TypeError(_lowercase     )
  elif len(_lowercase     ) % 2 != 0:
    __UpperCAmelCase:					Union[str, Any]		= F'''Odd number of symbols ({len(_lowercase     )})'''
    raise Exception(_lowercase     )
  elif pbstring == "":
    return {}

  pbstring.replace(""" """   ,    """"""     )

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

  # Created the dictionary
  __UpperCAmelCase:					List[Any]		= {}
  for j in range(0   ,    len(_lowercase     ) - 1   ,    2     ):
    __UpperCAmelCase:					str		= pbstring[j + 1]
    __UpperCAmelCase:					str		= pbstring[j]

  return pb






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				RotorPositionT   ,    _lowercase    :				RotorSelectionT = (rotora, rotora, rotora)   ,    _lowercase    :				str = ""   ,    )							->	List[Any]:
  __UpperCAmelCase:					List[Any]		= text.upper()
  __UpperCAmelCase:					Any		= _validator(
      _lowercase   ,    _lowercase   ,    plugb.upper()     )

  __UpperCAmelCase:					Optional[Any]		= rotor_position
  __UpperCAmelCase:					str		= rotor_selection
  rotorposa -= 1
  rotorposa -= 1
  rotorposa -= 1

  __UpperCAmelCase:					int		= []

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

      # rotor ra --------------------------
      __UpperCAmelCase:					Optional[Any]		= abc.index(_lowercase     ) + rotorposa
      __UpperCAmelCase:					int		= rotora[index % len(_lowercase     )]

      # rotor rb --------------------------
      __UpperCAmelCase:					List[str]		= abc.index(_lowercase     ) + rotorposa
      __UpperCAmelCase:					List[Any]		= rotora[index % len(_lowercase     )]

      # rotor rc --------------------------
      __UpperCAmelCase:					List[Any]		= abc.index(_lowercase     ) + rotorposa
      __UpperCAmelCase:					Tuple		= rotora[index % len(_lowercase     )]

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

      __UpperCAmelCase:					List[str]		= reflector[symbol]

      # 2nd rotors
      __UpperCAmelCase:					int		= abc[rotora.index(_lowercase     ) - rotorposa]
      __UpperCAmelCase:					str		= abc[rotora.index(_lowercase     ) - rotorposa]
      __UpperCAmelCase:					int		= abc[rotora.index(_lowercase     ) - rotorposa]

      # 2nd plugboard
      if symbol in plugboard:
        __UpperCAmelCase:					Tuple		= plugboard[symbol]

      # moves/resets rotor positions
      rotorposa += 1
      if rotorposa >= len(_lowercase     ):
        __UpperCAmelCase:					str		= 0
        rotorposa += 1
      if rotorposa >= len(_lowercase     ):
        __UpperCAmelCase:					int		= 0
        rotorposa += 1
      if rotorposa >= len(_lowercase     ):
        __UpperCAmelCase:					Union[str, Any]		= 0

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

  return "".join(_lowercase     )


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

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






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=18   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= size if size is not None else {"""height""": 18, """width""": 18}
    __UpperCAmelCase:					Tuple		= parent
    __UpperCAmelCase:					Any		= batch_size
    __UpperCAmelCase:					str		= num_channels
    __UpperCAmelCase:					Any		= image_size
    __UpperCAmelCase:					Optional[int]		= min_resolution
    __UpperCAmelCase:					Tuple		= max_resolution
    __UpperCAmelCase:					Any		= do_resize
    __UpperCAmelCase:					Any		= size
    __UpperCAmelCase:					Optional[Any]		= do_normalize







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
            ]					),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }




@require_torch
@require_vision
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					ImageGPTImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= ImageGPTImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """clusters"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""height""": 18, """width""": 18}					)

    __UpperCAmelCase:					int		= self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42					)
    self.assertEqual(image_processor.size   ,    {"""height""": 42, """width""": 42}					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.image_processing_class(**self.image_processor_dict					)
    __UpperCAmelCase:					str		= json.loads(image_processor.to_json_string()					)
    for key, value in self.image_processor_dict.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    obj[key]					)					)
      else:
        self.assertEqual(obj[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase:					List[Any]		= os.path.join(snake_case_   ,    """image_processor.json"""					)
      image_processor_first.to_json_file(snake_case_					)
      __UpperCAmelCase:					List[str]		= self.image_processing_class.from_json_file(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(snake_case_					)
      __UpperCAmelCase:					Any		= self.image_processing_class.from_pretrained(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  @unittest.skip("""ImageGPT requires clusters at initialization"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass






def  UpperCamelCase__					(					)							->	List[str]:
  __UpperCAmelCase:					Any		= load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,    split="""test"""     )

  __UpperCAmelCase:					List[Any]		= Image.open(dataset[4]["""file"""]     )
  __UpperCAmelCase:					Dict		= Image.open(dataset[5]["""file"""]     )

  __UpperCAmelCase:					Dict		= [imagea, imagea]

  return images







@require_vision
@require_torch
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""					)

    __UpperCAmelCase:					Dict		= prepare_images()

    # test non-batched
    __UpperCAmelCase:					Tuple		= image_processing(images[0]   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (1, 1024)					)

    __UpperCAmelCase:					int		= [306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist()   ,    snake_case_					)

    # test batched
    __UpperCAmelCase:					Union[str, Any]		= image_processing(snake_case_   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (2, 1024)					)

    __UpperCAmelCase:					Tuple		= [303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,    snake_case_					)