
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 							a__					(							snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  # Checks if there are 3 unique rotors

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

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

  # Validates string and returns dict
  __SCREAMING_SNAKE_CASE      :	int					=		_plugboard(snake_case							)

  return rotpos, rotsel, pbdict


def 							a__					(							snake_case							):





  """simple docstring"""

  # tests the input string if it
  # a) is type string
  # b) has even length (so pairs can be made)
  if not isinstance(snake_case       ,			snake_case							):
    __SCREAMING_SNAKE_CASE      :	str					=		F'''Plugboard setting isn\'t type string ({type(snake_case							)})'''
    raise TypeError(snake_case							)
  elif len(snake_case							) % 2 != 0:
    __SCREAMING_SNAKE_CASE      :	int					=		F'''Odd number of symbols ({len(snake_case							)})'''
    raise Exception(snake_case							)
  elif pbstring == "":
    return {}

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

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

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

  return pb


def 							a__					(							snake_case       ,			snake_case       ,			snake_case = (rotora, rotora, rotora)       ,			snake_case = ""       ,			):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	str					=		text.upper()
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[int]					=		_validator(
      snake_case       ,			snake_case       ,			plugb.upper()							)

  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[int]					=		rotor_position
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	str					=		rotor_selection
  rotorposa -= 1
  rotorposa -= 1
  rotorposa -= 1

  __SCREAMING_SNAKE_CASE      :	List[Any]					=		[]

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

      # rotor ra --------------------------
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		abc.index(snake_case							) + rotorposa
      __SCREAMING_SNAKE_CASE      :	Dict					=		rotora[index % len(snake_case							)]

      # rotor rb --------------------------
      __SCREAMING_SNAKE_CASE      :	List[Any]					=		abc.index(snake_case							) + rotorposa
      __SCREAMING_SNAKE_CASE      :	int					=		rotora[index % len(snake_case							)]

      # rotor rc --------------------------
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		abc.index(snake_case							) + rotorposa
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		rotora[index % len(snake_case							)]

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

      __SCREAMING_SNAKE_CASE      :	int					=		reflector[symbol]

      # 2nd rotors
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		abc[rotora.index(snake_case							) - rotorposa]
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		abc[rotora.index(snake_case							) - rotorposa]
      __SCREAMING_SNAKE_CASE      :	Any					=		abc[rotora.index(snake_case							) - rotorposa]

      # 2nd plugboard
      if symbol in plugboard:
        __SCREAMING_SNAKE_CASE      :	int					=		plugboard[symbol]

      # moves/resets rotor positions
      rotorposa += 1
      if rotorposa >= len(snake_case							):
        __SCREAMING_SNAKE_CASE      :	str					=		0
        rotorposa += 1
      if rotorposa >= len(snake_case							):
        __SCREAMING_SNAKE_CASE      :	Tuple					=		0
        rotorposa += 1
      if rotorposa >= len(snake_case							):
        __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		0

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

  return "".join(snake_case							)


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 inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
 from transformers.models.mobilevitva.modeling_mobilevitva import (
     MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
     make_divisible,
 )


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     self.config_class(**self.inputs_dict   )
         self.parent.assertTrue(hasattr(lowerCamelCase			, 'width_multiplier'   )   )







class 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase			, lowerCamelCase=13			, lowerCamelCase=64			, lowerCamelCase=2			, lowerCamelCase=3			, lowerCamelCase="swish"			, lowerCamelCase=3			, lowerCamelCase=32			, lowerCamelCase=0.1			, lowerCamelCase=0.02			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase=10			, lowerCamelCase=None			, lowerCamelCase=0.25			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, )				->List[str]:



         '''simple docstring'''
         __a					  =     parent
         __a					  =     batch_size
         __a					  =     image_size
         __a					  =     patch_size
         __a					  =     num_channels
         __a					  =     make_divisible(512 * width_multiplier			, divisor=8   )
         __a					  =     hidden_act
         __a					  =     conv_kernel_size
         __a					  =     output_stride
         __a					  =     classifier_dropout_prob
         __a					  =     use_labels
         __a					  =     is_training
         __a					  =     num_labels
         __a					  =     initializer_range
         __a					  =     scope
         __a					  =     width_multiplier
         __a					  =     ffn_dropout
         __a					  =     attn_dropout




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

         __a					  =     None
         __a					  =     None
         if self.use_labels:
            __a					  =     ids_tensor([self.batch_size]			, self.num_labels   )
            __a					  =     ids_tensor([self.batch_size, self.image_size, self.image_size]			, self.num_labels   )

         __a					  =     self.get_config()

         return config, pixel_values, labels, pixel_labels




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         return MobileViTVaConfig(
             image_size=self.image_size			, patch_size=self.patch_size			, num_channels=self.num_channels			, hidden_act=self.hidden_act			, conv_kernel_size=self.conv_kernel_size			, output_stride=self.output_stride			, classifier_dropout_prob=self.classifier_dropout_prob			, initializer_range=self.initializer_range			, width_multiplier=self.width_multiplier			, ffn_dropout=self.ffn_dropout_prob			, attn_dropout=self.attn_dropout_prob			, )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->str:



         '''simple docstring'''
         __a					  =     MobileViTVaModel(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase   )
         self.parent.assertEqual(
             result.last_hidden_state.shape			, (
                 self.batch_size,
                 self.last_hidden_size,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )			, )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Any:



         '''simple docstring'''
         __a					  =     self.num_labels
         __a					  =     MobileViTVaForImageClassification(lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase			, labels=lowerCamelCase   )
         self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.num_labels
         __a					  =     MobileViTVaForSemanticSegmentation(lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase   )
         self.parent.assertEqual(
             result.logits.shape			, (
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )			, )
         __a					  =     model(lowerCamelCase			, labels=lowerCamelCase   )
         self.parent.assertEqual(
             result.logits.shape			, (
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )			, )






      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.prepare_config_and_inputs()
         __a  ,      __a  ,      __a  ,      __a					  =     config_and_inputs
         __a					  =     {'pixel_values': pixel_values}
         return config, inputs_dict







@require_torch
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   _lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =(
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

      __a	       =(
          {
              "feature-extraction": MobileViTVaModel,
              "image-classification": MobileViTVaForImageClassification,
              "image-segmentation": MobileViTVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __a	       =False
      __a	       =False
      __a	       =False
      __a	       =False




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     MobileViTVaModelTester(self   )
         __a					  =     MobileViTVaConfigTester(self			, config_class=lowerCamelCase			, has_text_modality=lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         self.config_tester.run_common_tests()




      @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'   )
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         pass




      @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'   )
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         pass




      @unittest.skip(reason='MobileViTV2 does not output attentions'   )
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         pass




      @require_torch_multi_gpu
      @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'   )
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         pass




      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         pass




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a  ,      __a					  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __a					  =     model_class(lowerCamelCase   )
            __a					  =     inspect.signature(model.forward   )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __a					  =     [*signature.parameters.keys()]

            __a					  =     ['pixel_values']
            self.assertListEqual(arg_names[:1]			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         def check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   ):
            __a					  =     model_class(lowerCamelCase   )
            model.to(lowerCamelCase   )
            model.eval()

            with torch.no_grad():
               __a					  =     model(**self._prepare_for_class(lowerCamelCase			, lowerCamelCase   )   )

            __a					  =     outputs.hidden_states

            __a					  =     5
            self.assertEqual(len(lowerCamelCase   )			, lowerCamelCase   )

            # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
            # with the width and height being successively divided by 2.
            __a					  =     2
            for i in range(len(lowerCamelCase   )   ):
               self.assertListEqual(
                   list(hidden_states[i].shape[-2:]   )			, [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			, )
               divisor *= 2

            self.assertEqual(self.model_tester.output_stride			, divisor // 2   )

         __a  ,      __a					  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __a					  =     True
            check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __a					  =     True

            check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase   )






      @slow
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a					  =     MobileViTVaModel.from_pretrained(lowerCamelCase   )
            self.assertIsNotNone(lowerCamelCase   )







def  __UpperCAmelCase						(					)	->      List[Any]:

   """simple docstring"""
   __a					  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
   return image



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




      @cached_property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return (
             MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   )
             if is_vision_available()
             else None
         )




      @slow
      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   ).to(
             lowerCamelCase   )

         __a					  =     self.default_image_processor
         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )

         # verify the logits
         __a					  =     torch.Size((1, 1000)   )
         self.assertEqual(outputs.logits.shape			, lowerCamelCase   )

         __a					  =     torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]   ).to(lowerCamelCase   )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]			, lowerCamelCase			, atol=1e-4   )   )




      @slow
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
         __a					  =     model.to(lowerCamelCase   )

         __a					  =     MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )
         __a					  =     outputs.logits

         # verify the logits
         __a					  =     torch.Size((1, 21, 32, 32)   )
         self.assertEqual(logits.shape			, lowerCamelCase   )

         __a					  =     torch.tensor(
             [
                 [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                 [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                 [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
             ]			, device=lowerCamelCase			, )

         self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			, lowerCamelCase			, atol=1e-4   )   )






      @slow
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
         __a					  =     model.to(lowerCamelCase   )

         __a					  =     MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )

         __a					  =     outputs.logits.detach().cpu()

         __a					  =     image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase			, target_sizes=[(50, 60)]   )
         __a					  =     torch.Size((50, 60)   )
         self.assertEqual(segmentation[0].shape			, lowerCamelCase   )

         __a					  =     image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase   )
         __a					  =     torch.Size((32, 32)   )
         self.assertEqual(segmentation[0].shape			, lowerCamelCase   )