


import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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 torch import nn

	from transformers import (
	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    MODEL_MAPPING,
	    DeiTForImageClassification,
	    DeiTForImageClassificationWithTeacher,
	    DeiTForMaskedImageModeling,
	    DeiTModel,
	)
	from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import DeiTImageProcessor






class _lowerCamelCase:
							def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=30,   lowerCamelCase=2,   lowerCamelCase=3,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=32,   lowerCamelCase=5,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=10,   lowerCamelCase=0.0_2,   lowerCamelCase=3,   lowerCamelCase=None,   lowerCamelCase=2,   ) ->     Optional[int]:




											"""simple docstring"""

											_lowercase :							Any   	=	parent
											_lowercase :							int   	=	batch_size
											_lowercase :							int   	=	image_size
											_lowercase :							str   	=	patch_size
											_lowercase :							int   	=	num_channels
											_lowercase :							Any   	=	is_training
											_lowercase :							Union[str, Any]   	=	use_labels
											_lowercase :							Dict   	=	hidden_size
											_lowercase :							List[str]   	=	num_hidden_layers
											_lowercase :							Optional[Any]   	=	num_attention_heads
											_lowercase :							Optional[int]   	=	intermediate_size
											_lowercase :							Tuple   	=	hidden_act
											_lowercase :							str   	=	hidden_dropout_prob
											_lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
											_lowercase :							Tuple   	=	type_sequence_label_size
											_lowercase :							List[str]   	=	initializer_range
											_lowercase :							Any   	=	scope
											_lowercase :							Union[str, Any]   	=	encoder_stride

											# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
											_lowercase :							Union[str, Any]   	=	(image_size // patch_size) ** 2
											_lowercase :							Any   	=	num_patches + 2
							def     UpperCamelCase  (      self) ->     str:




											"""simple docstring"""

											_lowercase :							Optional[Any]   	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											_lowercase :							str   	=	None
											if self.use_labels:
															_lowercase :							List[Any]   	=	ids_tensor([self.batch_size],   self.type_sequence_label_size)

											_lowercase :							str   	=	self.get_config()

											return config, pixel_values, labels
							def     UpperCamelCase  (      self) ->     int:




											"""simple docstring"""

											return DeiTConfig(
											    image_size=self.image_size,   patch_size=self.patch_size,   num_channels=self.num_channels,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   is_decoder=lowerCamelCase,   initializer_range=self.initializer_range,   encoder_stride=self.encoder_stride,   )
							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




											"""simple docstring"""

											_lowercase :							Any   	=	DeiTModel(config=lowerCamelCase)
											model.to(lowerCamelCase)
											model.eval()
											_lowercase :							Optional[int]   	=	model(lowerCamelCase)
											self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size))
							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




											"""simple docstring"""

											_lowercase :							Optional[Any]   	=	DeiTForMaskedImageModeling(config=lowerCamelCase)
											model.to(lowerCamelCase)
											model.eval()
											_lowercase :							List[Any]   	=	model(lowerCamelCase)
											self.parent.assertEqual(
											    result.reconstruction.shape,   (self.batch_size, self.num_channels, self.image_size, self.image_size))

											# test greyscale images
											_lowercase :							Any   	=	1
											_lowercase :							Optional[Any]   	=	DeiTForMaskedImageModeling(lowerCamelCase)
											model.to(lowerCamelCase)
											model.eval()

											_lowercase :							List[str]   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
											_lowercase :							Optional[int]   	=	model(lowerCamelCase)
											self.parent.assertEqual(result.reconstruction.shape,   (self.batch_size, 1, self.image_size, self.image_size))
							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Any:




											"""simple docstring"""

											_lowercase :							str   	=	self.type_sequence_label_size
											_lowercase :							Dict   	=	DeiTForImageClassification(lowerCamelCase)
											model.to(lowerCamelCase)
											model.eval()
											_lowercase :							List[Any]   	=	model(lowerCamelCase,   labels=lowerCamelCase)
											self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size))

											# test greyscale images
											_lowercase :							Optional[Any]   	=	1
											_lowercase :							Optional[Any]   	=	DeiTForImageClassification(lowerCamelCase)
											model.to(lowerCamelCase)
											model.eval()

											_lowercase :							List[str]   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
											_lowercase :							List[Any]   	=	model(lowerCamelCase,   labels=lowerCamelCase)
											self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size))




							def     UpperCamelCase  (      self) ->     Union[str, Any]:




											"""simple docstring"""

											_lowercase :							Tuple   	=	self.prepare_config_and_inputs()
											(
											    _lowercase       
											) :							List[str]   	=	config_and_inputs
											_lowercase :							List[str]   	=	{'pixel_values': pixel_values}
											return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
							lowercase_				:			Optional[Any]									=    (
							    (
							        DeiTModel,
							        DeiTForImageClassification,
							        DeiTForImageClassificationWithTeacher,
							        DeiTForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_				:			Optional[Any]									=    (
							    {
							        """feature-extraction""": DeiTModel,
							        """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							lowercase_				:			Dict									=    False
							lowercase_				:			List[str]									=    False
							lowercase_				:			Union[str, Any]									=    False
							def     UpperCamelCase  (      self) ->     Optional[Any]:




											"""simple docstring"""

											_lowercase :							int   	=	DeiTModelTester(self)
											_lowercase :							Optional[Any]   	=	ConfigTester(self,   config_class=lowerCamelCase,   has_text_modality=lowerCamelCase,   hidden_size=37)
							def     UpperCamelCase  (      self) ->     Union[str, Any]:




											"""simple docstring"""

											self.config_tester.run_common_tests()
							@unittest.skip(reason='DeiT does not use inputs_embeds')
							def     UpperCamelCase  (      self) ->     str:




											"""simple docstring"""

											pass
							def     UpperCamelCase  (      self) ->     Union[str, Any]:




											"""simple docstring"""

											_lowercase :							int   	=	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_lowercase :							int   	=	model_class(lowerCamelCase)
															self.assertIsInstance(model.get_input_embeddings(),   (nn.Module))
															_lowercase :							Union[str, Any]   	=	model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(lowerCamelCase,   nn.Linear))
							def     UpperCamelCase  (      self) ->     int:




											"""simple docstring"""

											_lowercase :							Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

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

															_lowercase :							str   	=	['pixel_values']
															self.assertListEqual(arg_names[:1],   lowerCamelCase)
							def     UpperCamelCase  (      self) ->     List[str]:




											"""simple docstring"""

											_lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowerCamelCase)
							def     UpperCamelCase  (      self) ->     str:




											"""simple docstring"""

											_lowercase :							Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase)
							def     UpperCamelCase  (      self) ->     List[str]:




											"""simple docstring"""

											_lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)
							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=False) ->     Any:




											"""simple docstring"""

											_lowercase :							Dict   	=	super()._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)

											if return_labels:
															if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																			del inputs_dict["labels"]

											return inputs_dict
							def     UpperCamelCase  (      self) ->     Optional[int]:




											"""simple docstring"""

											if not self.model_tester.is_training:
															return

											_lowercase :							str   	=	self.model_tester.prepare_config_and_inputs_for_common()
											_lowercase :							Tuple   	=	True

											for model_class in self.all_model_classes:
															# DeiTForImageClassificationWithTeacher supports inference-only
															if (
															    model_class in get_values(lowerCamelCase)
															    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
															):
																			continue
															_lowercase :							Optional[int]   	=	model_class(lowerCamelCase)
															model.to(lowerCamelCase)
															model.train()
															_lowercase :							Optional[Any]   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)
															_lowercase :							List[str]   	=	model(**lowerCamelCase).loss
															loss.backward()
							def     UpperCamelCase  (      self) ->     Optional[Any]:




											"""simple docstring"""

											_lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
															return

											_lowercase :							Dict   	=	False
											_lowercase :							Optional[int]   	=	True

											for model_class in self.all_model_classes:
															if model_class in get_values(lowerCamelCase) or not model_class.supports_gradient_checkpointing:
																			continue
															# DeiTForImageClassificationWithTeacher supports inference-only
															if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																			continue
															_lowercase :							str   	=	model_class(lowerCamelCase)
															model.gradient_checkpointing_enable()
															model.to(lowerCamelCase)
															model.train()
															_lowercase :							Union[str, Any]   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)
															_lowercase :							List[Any]   	=	model(**lowerCamelCase).loss
															loss.backward()
							def     UpperCamelCase  (      self) ->     Union[str, Any]:




											"""simple docstring"""

											_lowercase :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()

											_lowercase :							int   	=	[
											    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
											    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
											    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
											]

											for model_class in self.all_model_classes:
															if (
															    model_class
															    not in [
															        *get_values(lowerCamelCase),
															        *get_values(lowerCamelCase),
															    ]
															    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
															):
																			continue

															for problem_type in problem_types:
																			with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''):
																							_lowercase :							List[Any]   	=	problem_type['title']
																							_lowercase :							str   	=	problem_type['num_labels']

																							_lowercase :							Optional[int]   	=	model_class(lowerCamelCase)
																							model.to(lowerCamelCase)
																							model.train()

																							_lowercase :							Tuple   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)

																							if problem_type["num_labels"] > 1:
																											_lowercase :							Dict   	=	inputs['labels'].unsqueeze(1).repeat(1,   problem_type['num_labels'])

																							_lowercase :							Optional[int]   	=	inputs['labels'].to(problem_type['dtype'])

																							# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																							# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																							# they have the same size." which is a symptom something in wrong for the regression problem.
																							# See https://github.com/huggingface/transformers/issues/11780
																							with warnings.catch_warnings(record=lowerCamelCase) as warning_list:
																											_lowercase :							Dict   	=	model(**lowerCamelCase).loss
																							for w in warning_list:
																											if "Using a target size that is different to the input size" in str(w.message):
																															raise ValueError(
																															    F'''Something is going wrong in the regression problem: intercepted {w.message}''')

																							loss.backward()




							@slow
							def     UpperCamelCase  (      self) ->     str:




											"""simple docstring"""

											for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase :							Tuple   	=	DeiTModel.from_pretrained(lowerCamelCase)
															self.assertIsNotNone(lowerCamelCase)



def        UpperCamelCase_(   )					->		List[str]:
				_lowercase :							Optional[Any]   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
				return image






@require_torch
@require_vision
class _lowerCamelCase(		unittest.TestCase     ):
							@cached_property
							def     UpperCamelCase  (      self) ->     Dict:




											"""simple docstring"""

											return (
											    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
											    if is_vision_available()
											    else None
											)
							@slow
							def     UpperCamelCase  (      self) ->     Dict:




											"""simple docstring"""

											_lowercase :							Optional[int]   	=	DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224').to(
											    lowerCamelCase)

											_lowercase :							List[str]   	=	self.default_image_processor
											_lowercase :							List[str]   	=	prepare_img()
											_lowercase :							Tuple   	=	image_processor(images=lowerCamelCase,   return_tensors='pt').to(lowerCamelCase)

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

											# verify the logits
											_lowercase :							Any   	=	torch.Size((1, 10_00))
											self.assertEqual(outputs.logits.shape,   lowerCamelCase)

											_lowercase :							Union[str, Any]   	=	torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]).to(lowerCamelCase)

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




							@slow
							@require_accelerate
							@require_torch_gpu
							def     UpperCamelCase  (      self) ->     List[Any]:




											"""simple docstring"""

											_lowercase :							Tuple   	=	DeiTModel.from_pretrained(
											    'facebook/deit-base-distilled-patch16-224',   torch_dtype=torch.floataa,   device_map='auto')
											_lowercase :							Union[str, Any]   	=	self.default_image_processor

											_lowercase :							Union[str, Any]   	=	prepare_img()
											_lowercase :							int   	=	image_processor(images=lowerCamelCase,   return_tensors='pt')
											_lowercase :							Union[str, Any]   	=	inputs.pixel_values.to(lowerCamelCase)

											# forward pass to make sure inference works in fp16
											with torch.no_grad():
															_lowercase :							Optional[int]   	=	model(lowerCamelCase)






from __future__ import annotations

SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				tuple[int, int, int]
SCREAMING_SNAKE_CASE :   Any							      =				tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# -------------------------- default selection --------------------------
# rotors --------------------------
SCREAMING_SNAKE_CASE :   Tuple							      =				"EGZWVONAHDCLFQMSIPJBYUKXTR"
SCREAMING_SNAKE_CASE :   List[str]							      =				"FOBHMDKEXQNRAULPGSJVTYICZW"
SCREAMING_SNAKE_CASE :   Dict							      =				"ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
SCREAMING_SNAKE_CASE :   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 --------------------------
SCREAMING_SNAKE_CASE :   List[Any]							      =				"RMDJXFUWGISLHVTCQNKYPBEZOA"
SCREAMING_SNAKE_CASE :   Dict							      =				"SGLCPQWZHKXAREONTFBVIYJUDM"
SCREAMING_SNAKE_CASE :   Optional[int]							      =				"HVSICLTYKQUBXDWAJZOMFGPREN"
SCREAMING_SNAKE_CASE :   List[Any]							      =				"RZWQHFMVDBKICJLNTUXAGYPSOE"
SCREAMING_SNAKE_CASE :   Optional[int]							      =				"LFKIJODBEGAMQPXVUHYSTCZRWN"
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"KOAEGVDHXPQZMLFTYWJNBRCIUS"



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
    # Checks if there are 3 unique rotors

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

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

    # Validates string and returns dict
    _lowercase :							Optional[int]   	=	_plugboard(lowerCamelCase_				)

    return rotpos, rotsel, pbdict



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict[str, str]:

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

    pbstring.replace(' '							,				''				)

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

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

    return pb



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_ = (rotora, rotora, rotora)							,				lowerCamelCase_ = ""							,				)					->		str:
    _lowercase :							int   	=	text.upper()
    _lowercase	,       _lowercase	,       _lowercase :							Optional[int]   	=	_validator(
        lowerCamelCase_							,				lowerCamelCase_							,				plugb.upper()				)

    _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	rotor_position
    _lowercase	,       _lowercase	,       _lowercase :							Optional[Any]   	=	rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    _lowercase :							Any   	=	[]

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

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

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

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

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

            _lowercase :							List[Any]   	=	reflector[symbol]

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

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

            # moves/resets rotor positions
            rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Optional[Any]   	=	0
                rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Any   	=	0
                rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Any   	=	0

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

    return "".join(lowerCamelCase_				)


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

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


