
"""simple docstring"""



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class 				a			(					lowercase      ):
      # to overwrite at feature extractactor specific tests
      UpperCamelCase							:   int           =     None
      UpperCamelCase							:   List[str]           =     None



      @property
      def  __snake_case							(   self      ):
          return self.feat_extract_tester.prepare_feat_extract_dict()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					self.feature_extraction_class(**self.feat_extract_dict      )
          self.assertTrue(hasattr(UpperCamelCase_     ,	'feature_size'      )      )
          self.assertTrue(hasattr(UpperCamelCase_     ,	'sampling_rate'      )      )
          self.assertTrue(hasattr(UpperCamelCase_     ,	'padding_value'      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase__	:      str     							=					self.feature_extraction_class(**self.feat_extract_dict      )
          UpperCAmelCase__	:      List[str]     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      Dict     							=					BatchFeature({input_name: speech_inputs}      )

          self.assertTrue(all(len(UpperCamelCase_      ) == len(UpperCamelCase_      ) for x, y in zip(UpperCamelCase_     ,	processed_features[input_name]      )      )      )

          UpperCAmelCase__	:      List[Any]     							=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					BatchFeature({input_name: speech_inputs}     ,	tensor_type='np'      )

          UpperCAmelCase__	:      Optional[Any]     							=					processed_features[input_name]

          if len(batch_features_input.shape      ) < 3:
              UpperCAmelCase__	:      Dict     							=					batch_features_input[:, :, None]

          self.assertTrue(
              batch_features_input.shape
              == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )



      @require_torch
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					self.feature_extraction_class(**self.feat_extract_dict      )
          UpperCAmelCase__	:      List[Any]     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      Union[str, Any]     							=					BatchFeature({input_name: speech_inputs}     ,	tensor_type='pt'      )

          UpperCAmelCase__	:      Dict     							=					processed_features[input_name]

          if len(batch_features_input.shape      ) < 3:
              UpperCAmelCase__	:      Optional[Any]     							=					batch_features_input[:, :, None]

          self.assertTrue(
              batch_features_input.shape
              == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )



      @require_tf
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[int]     							=					self.feature_extraction_class(**self.feat_extract_dict      )
          UpperCAmelCase__	:      Tuple     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      Any     							=					BatchFeature({input_name: speech_inputs}     ,	tensor_type='tf'      )

          UpperCAmelCase__	:      Any     							=					processed_features[input_name]

          if len(batch_features_input.shape      ) < 3:
              UpperCAmelCase__	:      Dict     							=					batch_features_input[:, :, None]

          self.assertTrue(
              batch_features_input.shape
              == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )



      def  __snake_case							(   self     ,	UpperCamelCase_=False      ):
          def _inputs_have_equal_length(UpperCamelCase_      ):
              UpperCAmelCase__	:      List[Any]     							=					len(input[0]      )
              for input_slice in input[1:]:
                  if len(UpperCamelCase_      ) != length:
                      return False
              return True

          def _inputs_are_equal(UpperCamelCase_     ,	UpperCamelCase_      ):
              if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
                  return False

              for input_slice_a, input_slice_a in zip(UpperCamelCase_     ,	UpperCamelCase_      ):
                  if not np.allclose(np.asarray(UpperCamelCase_      )     ,	np.asarray(UpperCamelCase_      )     ,	atol=1E-3      ):
                      return False
              return True

          UpperCAmelCase__	:      Dict     							=					self.feature_extraction_class(**self.feat_extract_dict      )
          UpperCAmelCase__	:      Union[str, Any]     							=					self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      List[str]     							=					BatchFeature({input_name: speech_inputs}      )

          UpperCAmelCase__	:      int     							=					self.feat_extract_tester.seq_length_diff
          UpperCAmelCase__	:      Union[str, Any]     							=					self.feat_extract_tester.max_seq_length + pad_diff
          UpperCAmelCase__	:      int     							=					self.feat_extract_tester.min_seq_length
          UpperCAmelCase__	:      Tuple     							=					self.feat_extract_tester.batch_size
          UpperCAmelCase__	:      Dict     							=					self.feat_extract_tester.feature_size

          # test padding for List[int] + numpy
          UpperCAmelCase__	:      Any     							=					feat_extract.pad(UpperCamelCase_     ,	padding=UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					input_a[input_name]

          UpperCAmelCase__	:      Dict     							=					feat_extract.pad(UpperCamelCase_     ,	padding='longest'      )
          UpperCAmelCase__	:      int     							=					input_a[input_name]

          UpperCAmelCase__	:      Optional[Any]     							=					feat_extract.pad(UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[-1]      )      )
          UpperCAmelCase__	:      Union[str, Any]     							=					input_a[input_name]

          UpperCAmelCase__	:      List[str]     							=					feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	return_tensors='np'      )
          UpperCAmelCase__	:      Tuple     							=					input_a[input_name]

          # max_length parameter has to be provided when setting `padding="max_length"`
          with self.assertRaises(UpperCamelCase_      ):
              feat_extract.pad(UpperCamelCase_     ,	padding='max_length'      )[input_name]

          UpperCAmelCase__	:      Dict     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=UpperCamelCase_     ,	return_tensors='np'      )
          UpperCAmelCase__	:      Union[str, Any]     							=					input_a[input_name]

          self.assertFalse(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertTrue(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertTrue(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertTrue(_inputs_are_equal(UpperCamelCase_     ,	UpperCamelCase_      )      )
          self.assertTrue(len(input_a[0]      ) == pad_min_length      )
          self.assertTrue(len(input_a[1]      ) == pad_min_length + pad_diff      )
          self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]      ))      )
          self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)      )

          if feature_size > 1:
              self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size      )

          # test padding for `pad_to_multiple_of` for List[int] + numpy
          UpperCAmelCase__	:      List[Any]     							=					feat_extract.pad(UpperCamelCase_     ,	pad_to_multiple_of=10      )
          UpperCAmelCase__	:      int     							=					input_a[input_name]

          UpperCAmelCase__	:      int     							=					feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	pad_to_multiple_of=10      )
          UpperCAmelCase__	:      Tuple     							=					input_a[input_name]

          UpperCAmelCase__	:      Any     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	pad_to_multiple_of=10     ,	max_length=UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					input_a[input_name]

          UpperCAmelCase__	:      Union[str, Any]     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	pad_to_multiple_of=10     ,	max_length=UpperCamelCase_     ,	return_tensors='np'     ,	)
          UpperCAmelCase__	:      Optional[Any]     							=					input_a[input_name]

          self.assertTrue(all(len(UpperCamelCase_      ) % 10 == 0 for x in input_a      )      )
          self.assertTrue(_inputs_are_equal(UpperCamelCase_     ,	UpperCamelCase_      )      )

          UpperCAmelCase__	:      int     							=					pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
          self.assertTrue(all(len(UpperCamelCase_      ) == expected_mult_pad_length for x in input_a      )      )
          self.assertEqual(input_a.shape[:2]     ,	(batch_size, expected_mult_pad_length)      )

          if feature_size > 1:
              self.assertTrue(input_a.shape[2] == feature_size      )

          # Check padding value is correct
          UpperCAmelCase__	:      Union[str, Any]     							=					(np.ones(self.feat_extract_tester.feature_size      ) * feat_extract.padding_value).sum()
          self.assertTrue(
              abs(np.asarray(input_a[0]      )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      )
              < 1E-3      )
          self.assertTrue(
              abs(
                  np.asarray(input_a[1]      )[pad_min_length + pad_diff :].sum()
                  - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)      )
              < 1E-3      )
          self.assertTrue(
              abs(
                  np.asarray(input_a[2]      )[pad_min_length + 2 * pad_diff :].sum()
                  - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)      )
              < 1E-3      )
          self.assertTrue(
              abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      ) < 1E-3      )
          self.assertTrue(
              abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)      )
              < 1E-3      )



      def  __snake_case							(   self     ,	UpperCamelCase_=False      ):
          def _inputs_have_equal_length(UpperCamelCase_      ):
              UpperCAmelCase__	:      int     							=					len(input[0]      )
              for input_slice in input[1:]:
                  if len(UpperCamelCase_      ) != length:
                      return False
              return True

          def _inputs_are_equal(UpperCamelCase_     ,	UpperCamelCase_      ):
              if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
                  return False

              for input_slice_a, input_slice_a in zip(UpperCamelCase_     ,	UpperCamelCase_      ):
                  if not np.allclose(np.asarray(UpperCamelCase_      )     ,	np.asarray(UpperCamelCase_      )     ,	atol=1E-3      ):
                      return False
              return True

          UpperCAmelCase__	:      Any     							=					self.feature_extraction_class(**self.feat_extract_dict      )
          UpperCAmelCase__	:      str     							=					self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      str     							=					BatchFeature({input_name: speech_inputs}      )

          # truncate to smallest
          UpperCAmelCase__	:      List[str]     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[0]      )     ,	truncation=UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					input_a[input_name]

          UpperCAmelCase__	:      Tuple     							=					feat_extract.pad(UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[0]      )      )
          UpperCAmelCase__	:      Tuple     							=					input_a[input_name]

          self.assertTrue(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertFalse(_inputs_have_equal_length(UpperCamelCase_      )      )

          # truncate to smallest with np
          UpperCAmelCase__	:      str     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[0]      )     ,	return_tensors='np'     ,	truncation=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      Dict     							=					input_a[input_name]

          UpperCAmelCase__	:      List[Any]     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[0]      )     ,	return_tensors='np'      )
          UpperCAmelCase__	:      Tuple     							=					input_a[input_name]

          self.assertTrue(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertTrue(input_a.shape[1] == len(speech_inputs[0]      )      )

          # since truncation forces padding to be smaller than longest input
          # function can't return `np.ndarray`, but has to return list
          self.assertFalse(_inputs_have_equal_length(UpperCamelCase_      )      )

          # truncate to middle
          UpperCAmelCase__	:      Optional[int]     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[1]      )     ,	truncation=UpperCamelCase_     ,	return_tensors='np'     ,	)
          UpperCAmelCase__	:      List[Any]     							=					input_a[input_name]

          UpperCAmelCase__	:      List[Any]     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[1]      )     ,	truncation=UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					input_a[input_name]

          UpperCAmelCase__	:      Optional[Any]     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[1]      )     ,	return_tensors='np'      )
          UpperCAmelCase__	:      List[Any]     							=					input_a[input_name]

          self.assertTrue(input_a.shape[1] == len(speech_inputs[1]      )      )
          self.assertTrue(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertTrue(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertTrue(_inputs_are_equal(UpperCamelCase_     ,	UpperCamelCase_      )      )

          # since truncation forces padding to be smaller than longest input
          # function can't return `np.ndarray`, but has to return list
          self.assertFalse(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertTrue(len(input_a[-1]      ) == len(speech_inputs[-1]      )      )

          # padding has to be max_length when setting `truncation=True`
          with self.assertRaises(UpperCamelCase_      ):
              feat_extract.pad(UpperCamelCase_     ,	truncation=UpperCamelCase_      )[input_name]

          # padding has to be max_length when setting `truncation=True`
          with self.assertRaises(UpperCamelCase_      ):
              feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	truncation=UpperCamelCase_      )[input_name]

          # padding has to be max_length when setting `truncation=True`
          with self.assertRaises(UpperCamelCase_      ):
              feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	truncation=UpperCamelCase_      )[input_name]

          # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
          with self.assertRaises(UpperCamelCase_      ):
              feat_extract.pad(UpperCamelCase_     ,	padding='max_length'     ,	truncation=UpperCamelCase_      )[input_name]

          # test truncation for `pad_to_multiple_of` for List[int] + numpy
          UpperCAmelCase__	:      List[str]     							=					12
          UpperCAmelCase__	:      Tuple     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[0]      )     ,	pad_to_multiple_of=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      Union[str, Any]     							=					input_a[input_name]

          UpperCAmelCase__	:      Union[str, Any]     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=len(speech_inputs[0]      )     ,	pad_to_multiple_of=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      List[str]     							=					input_a[input_name]

          # retrieve expected_length as multiple of pad_to_multiple_of
          UpperCAmelCase__	:      List[str]     							=					len(speech_inputs[0]      )
          if expected_length % pad_to_multiple_of != 0:
              UpperCAmelCase__	:      Optional[Any]     							=					((len(speech_inputs[0]      ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

          self.assertTrue(len(input_a[0]      ) == expected_length      )
          self.assertTrue(_inputs_have_equal_length(UpperCamelCase_      )      )
          self.assertFalse(_inputs_have_equal_length(UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          self._check_padding(numpify=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          self._check_padding(numpify=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          self._check_truncation(numpify=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          self._check_truncation(numpify=UpperCamelCase_      )



      @require_torch
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					self.feature_extraction_class(**self.feat_extract_dict      )
          UpperCAmelCase__	:      Union[str, Any]     							=					self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase__	:      int     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      int     							=					BatchFeature({input_name: speech_inputs}      )

          UpperCAmelCase__	:      Dict     							=					feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	return_tensors='np'      )[input_name]
          UpperCAmelCase__	:      List[str]     							=					feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	return_tensors='pt'      )[input_name]

          self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_pt.numpy().astype(np.floataa      ).sum()      ) < 1E-2      )



      @require_tf
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.feature_extraction_class(**self.feat_extract_dict      )
          UpperCAmelCase__	:      List[Any]     							=					self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase__	:      Any     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      Any     							=					BatchFeature({input_name: speech_inputs}      )

          UpperCAmelCase__	:      Optional[Any]     							=					feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	return_tensors='np'      )[input_name]
          UpperCAmelCase__	:      int     							=					feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	return_tensors='tf'      )[input_name]

          self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_tf.numpy().astype(np.floataa      ).sum()      ) < 1E-2      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.feat_extract_dict
          UpperCAmelCase__	:      List[Any]     							=					True
          UpperCAmelCase__	:      int     							=					self.feature_extraction_class(**UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase__	:      Tuple     							=					[len(UpperCamelCase_      ) for x in speech_inputs]
          UpperCAmelCase__	:      List[Any]     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      int     							=					BatchFeature({input_name: speech_inputs}      )

          UpperCAmelCase__	:      List[str]     							=					feat_extract.pad(UpperCamelCase_     ,	padding='longest'     ,	return_tensors='np'      )
          self.assertIn('attention_mask'     ,	UpperCamelCase_      )
          self.assertListEqual(list(processed.attention_mask.shape      )     ,	list(processed[input_name].shape[:2]      )      )
          self.assertListEqual(processed.attention_mask.sum(-1      ).tolist()     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.feat_extract_dict
          UpperCAmelCase__	:      int     							=					True
          UpperCAmelCase__	:      Any     							=					self.feature_extraction_class(**UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase__	:      List[Any]     							=					[len(UpperCamelCase_      ) for x in speech_inputs]
          UpperCAmelCase__	:      List[str]     							=					feat_extract.model_input_names[0]

          UpperCAmelCase__	:      str     							=					BatchFeature({input_name: speech_inputs}      )
          UpperCAmelCase__	:      Optional[Any]     							=					min(UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					feat_extract.pad(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	return_tensors='np'      )
          self.assertIn('attention_mask'     ,	UpperCamelCase_      )
          self.assertListEqual(
              list(processed_pad.attention_mask.shape      )     ,	[processed_pad[input_name].shape[0], max_length]      )
          self.assertListEqual(
              processed_pad.attention_mask[:, :max_length].sum(-1      ).tolist()     ,	[max_length for x in speech_inputs]      )



"""simple docstring"""



from __future__ import annotations

UpperCamelCase__          =	tuple[int, int, int]
UpperCamelCase__          =	tuple[str, str, str]


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

# -------------------------- default selection --------------------------
# rotors --------------------------
UpperCamelCase__          =	'EGZWVONAHDCLFQMSIPJBYUKXTR'
UpperCamelCase__          =	'FOBHMDKEXQNRAULPGSJVTYICZW'
UpperCamelCase__          =	'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
UpperCamelCase__          =	{
    '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 --------------------------
UpperCamelCase__          =	'RMDJXFUWGISLHVTCQNKYPBEZOA'
UpperCamelCase__          =	'SGLCPQWZHKXAREONTFBVIYJUDM'
UpperCamelCase__          =	'HVSICLTYKQUBXDWAJZOMFGPREN'
UpperCamelCase__          =	'RZWQHFMVDBKICJLNTUXAGYPSOE'
UpperCamelCase__          =	'LFKIJODBEGAMQPXVUHYSTCZRWN'
UpperCamelCase__          =	'KOAEGVDHXPQZMLFTYWJNBRCIUS'
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    # Checks if there are 3 unique rotors

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

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

    # Validates string and returns dict
    UpperCAmelCase__	:      Optional[int]     							=					_plugboard(_snake_case )

    return rotpos, rotsel, pbdict
def 					lowerCamelCase   (     _snake_case ):

    # 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 ):
        UpperCAmelCase__	:      Union[str, Any]     							=					F'''Plugboard setting isn\'t type string ({type(_snake_case )})'''
        raise TypeError(_snake_case )
    elif len(_snake_case ) % 2 != 0:
        UpperCAmelCase__	:      Dict     							=					F'''Odd number of symbols ({len(_snake_case )})'''
        raise Exception(_snake_case )
    elif pbstring == "":
        return {}

    pbstring.replace(' '    ,'' )

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

    # Created the dictionary
    UpperCAmelCase__	:      str     							=					{}
    for j in range(0    ,len(_snake_case ) - 1    ,2 ):
        UpperCAmelCase__	:      Dict     							=					pbstring[j + 1]
        UpperCAmelCase__	:      int     							=					pbstring[j]

    return pb
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case = (rotora, rotora, rotora)    ,_snake_case = ""    ,):
    UpperCAmelCase__	:      str     							=					text.upper()
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					_validator(
        _snake_case    ,_snake_case    ,plugb.upper() )

    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					rotor_position
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    UpperCAmelCase__	:      Any     							=					[]

    # encryption/decryption process --------------------------
    for symbol in text:
        if symbol in abc:
            # 1st plugboard --------------------------
            if symbol in plugboard:
                UpperCAmelCase__	:      Dict     							=					plugboard[symbol]

            # rotor ra --------------------------
            UpperCAmelCase__	:      List[str]     							=					abc.index(_snake_case ) + rotorposa
            UpperCAmelCase__	:      Optional[Any]     							=					rotora[index % len(_snake_case )]

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

            # rotor rc --------------------------
            UpperCAmelCase__	:      Optional[Any]     							=					abc.index(_snake_case ) + rotorposa
            UpperCAmelCase__	:      Any     							=					rotora[index % len(_snake_case )]

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

            UpperCAmelCase__	:      Union[str, Any]     							=					reflector[symbol]

            # 2nd rotors
            UpperCAmelCase__	:      List[str]     							=					abc[rotora.index(_snake_case ) - rotorposa]
            UpperCAmelCase__	:      Optional[Any]     							=					abc[rotora.index(_snake_case ) - rotorposa]
            UpperCAmelCase__	:      List[Any]     							=					abc[rotora.index(_snake_case ) - rotorposa]

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

            # moves/resets rotor positions
            rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      Dict     							=					0
                rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      Any     							=					0
                rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      int     							=					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__":
    UpperCamelCase__          =	'This is my Python script that emulates the Enigma machine from WWII.'
    UpperCamelCase__          =	(1, 1, 1)
    UpperCamelCase__          =	'pictures'
    UpperCamelCase__          =	(rotora, rotora, rotora)
    UpperCamelCase__          =	enigma(message, rotor_pos, rotor_sel, pb)

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

