


import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import YolosForObjectDetection, YolosModel
	from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor






class _lowerCamelCase:
							def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=[30, 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=8,   lowerCamelCase=10,   ) ->     Union[str, Any]:




											"""simple docstring"""

											_lowercase :							Optional[Any]   	=	parent
											_lowercase :							int   	=	batch_size
											_lowercase :							str   	=	image_size
											_lowercase :							Any   	=	patch_size
											_lowercase :							Optional[Any]   	=	num_channels
											_lowercase :							Union[str, Any]   	=	is_training
											_lowercase :							Dict   	=	use_labels
											_lowercase :							Optional[Any]   	=	hidden_size
											_lowercase :							Optional[int]   	=	num_hidden_layers
											_lowercase :							List[str]   	=	num_attention_heads
											_lowercase :							Optional[Any]   	=	intermediate_size
											_lowercase :							Tuple   	=	hidden_act
											_lowercase :							Union[str, Any]   	=	hidden_dropout_prob
											_lowercase :							str   	=	attention_probs_dropout_prob
											_lowercase :							int   	=	type_sequence_label_size
											_lowercase :							str   	=	initializer_range
											_lowercase :							Tuple   	=	num_labels
											_lowercase :							Any   	=	scope
											_lowercase :							Optional[Any]   	=	n_targets
											_lowercase :							List[Any]   	=	num_detection_tokens
											# we set the expected sequence length (which is used in several tests)
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
											_lowercase :							Tuple   	=	(image_size[1] // patch_size) * (image_size[0] // patch_size)
											_lowercase :							str   	=	num_patches + 1 + self.num_detection_tokens
							def     UpperCamelCase  (      self) ->     Union[str, Any]:




											"""simple docstring"""

											_lowercase :							List[str]   	=	floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

											_lowercase :							str   	=	None
											if self.use_labels:
															# labels is a list of Dict (each Dict being the labels for a given example in the batch)
															_lowercase :							Optional[Any]   	=	[]
															for i in range(self.batch_size):
																			_lowercase :							Tuple   	=	{}
																			_lowercase :							Dict   	=	torch.randint(
																			    high=self.num_labels,   size=(self.n_targets,),   device=lowerCamelCase)
																			_lowercase :							str   	=	torch.rand(self.n_targets,   4,   device=lowerCamelCase)
																			labels.append(lowerCamelCase)

											_lowercase :							Tuple   	=	self.get_config()

											return config, pixel_values, labels
							def     UpperCamelCase  (      self) ->     List[Any]:




											"""simple docstring"""

											return YolosConfig(
											    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,   num_detection_tokens=self.num_detection_tokens,   num_labels=self.num_labels,   )
							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Tuple:




											"""simple docstring"""

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




											"""simple docstring"""

											_lowercase :							Optional[int]   	=	YolosForObjectDetection(lowerCamelCase)
											model.to(lowerCamelCase)
											model.eval()

											_lowercase :							List[Any]   	=	model(pixel_values=lowerCamelCase)
											_lowercase :							Union[str, Any]   	=	model(lowerCamelCase)

											self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
											self.parent.assertEqual(result.pred_boxes.shape,   (self.batch_size, self.num_detection_tokens, 4))

											_lowercase :							Tuple   	=	model(pixel_values=lowerCamelCase,   labels=lowerCamelCase)

											self.parent.assertEqual(result.loss.shape,   ())
											self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
											self.parent.assertEqual(result.pred_boxes.shape,   (self.batch_size, self.num_detection_tokens, 4))




							def     UpperCamelCase  (      self) ->     Any:




											"""simple docstring"""

											_lowercase :							int   	=	self.prepare_config_and_inputs()
											_lowercase :							Dict   	=	config_and_inputs
											_lowercase :							Dict   	=	{'pixel_values': pixel_values}
											return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
							lowercase_				:			int									=    (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							lowercase_				:			Optional[Any]									=    (
							    {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
							)

							lowercase_				:			Tuple									=    False
							lowercase_				:			Optional[Any]									=    False
							lowercase_				:			Tuple									=    False
							lowercase_				:			Optional[Any]									=    False
							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=False) ->     str:




											"""simple docstring"""

											_lowercase :							List[Any]   	=	super()._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)

											if return_labels:
															if model_class.__name__ == "YolosForObjectDetection":
																			_lowercase :							Dict   	=	[]
																			for i in range(self.model_tester.batch_size):
																							_lowercase :							List[Any]   	=	{}
																							_lowercase :							str   	=	torch.ones(
																							    size=(self.model_tester.n_targets,),   device=lowerCamelCase,   dtype=torch.long)
																							_lowercase :							List[str]   	=	torch.ones(
																							    self.model_tester.n_targets,   4,   device=lowerCamelCase,   dtype=torch.float)
																							labels.append(lowerCamelCase)
																			_lowercase :							Optional[int]   	=	labels

											return inputs_dict
							def     UpperCamelCase  (      self) ->     Tuple:




											"""simple docstring"""

											_lowercase :							str   	=	YolosModelTester(self)
											_lowercase :							int   	=	ConfigTester(self,   config_class=lowerCamelCase,   has_text_modality=lowerCamelCase,   hidden_size=37)
							def     UpperCamelCase  (      self) ->     int:




											"""simple docstring"""

											self.config_tester.run_common_tests()
							def     UpperCamelCase  (      self) ->     int:




											"""simple docstring"""

											pass
							def     UpperCamelCase  (      self) ->     List[str]:




											"""simple docstring"""

											_lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()

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




											"""simple docstring"""

											_lowercase :							str   	=	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_lowercase :							Optional[int]   	=	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 :							Any   	=	['pixel_values']
															self.assertListEqual(arg_names[:1],   lowerCamelCase)
							def     UpperCamelCase  (      self) ->     Any:




											"""simple docstring"""

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




											"""simple docstring"""

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

											# in YOLOS, the seq_len is different
											_lowercase :							Dict   	=	self.model_tester.expected_seq_len
											for model_class in self.all_model_classes:
															_lowercase :							Optional[Any]   	=	True
															_lowercase :							str   	=	False
															_lowercase :							Tuple   	=	True
															_lowercase :							Tuple   	=	model_class(lowerCamelCase)
															model.to(lowerCamelCase)
															model.eval()
															with torch.no_grad():
																			_lowercase :							int   	=	model(**self._prepare_for_class(lowerCamelCase,   lowerCamelCase))
															_lowercase :							Optional[int]   	=	outputs.attentions
															self.assertEqual(len(lowerCamelCase),   self.model_tester.num_hidden_layers)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															_lowercase :							int   	=	True
															_lowercase :							Tuple   	=	model_class(lowerCamelCase)
															model.to(lowerCamelCase)
															model.eval()
															with torch.no_grad():
																			_lowercase :							Any   	=	model(**self._prepare_for_class(lowerCamelCase,   lowerCamelCase))
															_lowercase :							str   	=	outputs.attentions
															self.assertEqual(len(lowerCamelCase),   self.model_tester.num_hidden_layers)

															self.assertListEqual(
															    list(attentions[0].shape[-3:]),   [self.model_tester.num_attention_heads, seq_len, seq_len],   )
															_lowercase :							Optional[Any]   	=	len(lowerCamelCase)

															# Check attention is always last and order is fine
															_lowercase :							List[str]   	=	True
															_lowercase :							Union[str, Any]   	=	True
															_lowercase :							Any   	=	model_class(lowerCamelCase)
															model.to(lowerCamelCase)
															model.eval()
															with torch.no_grad():
																			_lowercase :							Dict   	=	model(**self._prepare_for_class(lowerCamelCase,   lowerCamelCase))

															_lowercase :							Dict   	=	1
															self.assertEqual(out_len + added_hidden_states,   len(lowerCamelCase))

															_lowercase :							Any   	=	outputs.attentions

															self.assertEqual(len(lowerCamelCase),   self.model_tester.num_hidden_layers)
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]),   [self.model_tester.num_attention_heads, seq_len, seq_len],   )
							def     UpperCamelCase  (      self) ->     Optional[int]:




											"""simple docstring"""

											def check_hidden_states_output(lowerCamelCase,   lowerCamelCase,   lowerCamelCase):
															_lowercase :							Tuple   	=	model_class(lowerCamelCase)
															model.to(lowerCamelCase)
															model.eval()

															with torch.no_grad():
																			_lowercase :							Optional[Any]   	=	model(**self._prepare_for_class(lowerCamelCase,   lowerCamelCase))

															_lowercase :							int   	=	outputs.hidden_states

															_lowercase :							Dict   	=	getattr(
															    self.model_tester,   'expected_num_hidden_layers',   self.model_tester.num_hidden_layers + 1)
															self.assertEqual(len(lowerCamelCase),   lowerCamelCase)

															# YOLOS has a different seq_length
															_lowercase :							List[str]   	=	self.model_tester.expected_seq_len

															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]),   [seq_length, self.model_tester.hidden_size],   )

											_lowercase :							Dict   	=	self.model_tester.prepare_config_and_inputs_for_common()

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

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_lowercase :							Union[str, Any]   	=	True

															check_hidden_states_output(lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
							def     UpperCamelCase  (      self) ->     Dict:




											"""simple docstring"""

											_lowercase :							Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_object_detection(*lowerCamelCase)




							@slow
							def     UpperCamelCase  (      self) ->     Dict:




											"""simple docstring"""

											for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase :							Optional[Any]   	=	YolosModel.from_pretrained(lowerCamelCase)
															self.assertIsNotNone(lowerCamelCase)



def        UpperCamelCase_(   )					->		List[str]:
				_lowercase :							List[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 AutoImageProcessor.from_pretrained('hustvl/yolos-small') if is_vision_available() else None




							@slow
							def     UpperCamelCase  (      self) ->     int:




											"""simple docstring"""

											_lowercase :							List[str]   	=	YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(lowerCamelCase)

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

											# forward pass
											with torch.no_grad():
															_lowercase :							str   	=	model(inputs.pixel_values)

											# verify outputs
											_lowercase :							Optional[int]   	=	torch.Size((1, 1_00, 92))
											self.assertEqual(outputs.logits.shape,   lowerCamelCase)

											_lowercase :							Tuple   	=	torch.tensor(
											    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]],   device=lowerCamelCase,   )
											_lowercase :							Dict   	=	torch.tensor(
											    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]],   device=lowerCamelCase)
											self.assertTrue(torch.allclose(outputs.logits[0, :3, :3],   lowerCamelCase,   atol=1E-4))
											self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3],   lowerCamelCase,   atol=1E-4))

											# verify postprocessing
											_lowercase :							str   	=	image_processor.post_process_object_detection(
											    lowerCamelCase,   threshold=0.3,   target_sizes=[image.size[::-1]])[0]
											_lowercase :							Union[str, Any]   	=	torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]).to(lowerCamelCase)
											_lowercase :							Optional[Any]   	=	[75, 75, 17, 63, 17]
											_lowercase :							Union[str, Any]   	=	torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]).to(lowerCamelCase)

											self.assertEqual(len(results['scores']),   5)
											self.assertTrue(torch.allclose(results['scores'],   lowerCamelCase,   atol=1E-4))
											self.assertSequenceEqual(results['labels'].tolist(),   lowerCamelCase)
											self.assertTrue(torch.allclose(results['boxes'][0, :],   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))


