



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					( A_      ):
			# to overwrite at feature extractactor specific tests
			UpperCamelCase					:Optional[Any]     =			None
			UpperCamelCase					:str     =			None





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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		self.feature_extraction_class(**self.feat_extract_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """feature_size"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """sampling_rate"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """padding_value"""				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		self.feat_extract_tester.prepare_inputs_for_common()
						lowerCamelCase__ :      Tuple								=		self.feature_extraction_class(**self.feat_extract_dict				)
						lowerCamelCase__ :      Optional[int]								=		feat_extract.model_input_names[0]

						lowerCamelCase__ :      Optional[Any]								=		BatchFeature({input_name: speech_inputs}				)

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

						lowerCamelCase__ :      List[Any]								=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__magic_name__				)
						lowerCamelCase__ :      Dict								=		BatchFeature({input_name: speech_inputs}							,    tensor_type="""np"""				)

						lowerCamelCase__ :      Optional[Any]								=		processed_features[input_name]

						if len(batch_features_input.shape				) < 3:
									lowerCamelCase__ :      Optional[int]								=		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				):
						lowerCamelCase__ :      Union[str, Any]								=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__magic_name__				)
						lowerCamelCase__ :      Optional[int]								=		self.feature_extraction_class(**self.feat_extract_dict				)
						lowerCamelCase__ :      Tuple								=		feat_extract.model_input_names[0]

						lowerCamelCase__ :      Tuple								=		BatchFeature({input_name: speech_inputs}							,    tensor_type="""pt"""				)

						lowerCamelCase__ :      int								=		processed_features[input_name]

						if len(batch_features_input.shape				) < 3:
									lowerCamelCase__ :      Tuple								=		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				):
						lowerCamelCase__ :      List[Any]								=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		self.feature_extraction_class(**self.feat_extract_dict				)
						lowerCamelCase__ :      Dict								=		feat_extract.model_input_names[0]

						lowerCamelCase__ :      List[str]								=		BatchFeature({input_name: speech_inputs}							,    tensor_type="""tf"""				)

						lowerCamelCase__ :      int								=		processed_features[input_name]

						if len(batch_features_input.shape				) < 3:
									lowerCamelCase__ :      Union[str, 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)				)





			def        _snake_case    (self							,    __magic_name__=False				):
						def _inputs_have_equal_length(__magic_name__				):
									lowerCamelCase__ :      Union[str, Any]								=		len(input[0]				)
									for input_slice in input[1:]:
												if len(__magic_name__				) != length:
															return False
									return True

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

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

						lowerCamelCase__ :      List[Any]								=		self.feature_extraction_class(**self.feat_extract_dict				)
						lowerCamelCase__ :      Any								=		self.feat_extract_tester.prepare_inputs_for_common(numpify=__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		feat_extract.model_input_names[0]

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

						lowerCamelCase__ :      List[str]								=		self.feat_extract_tester.seq_length_diff
						lowerCamelCase__ :      List[str]								=		self.feat_extract_tester.max_seq_length + pad_diff
						lowerCamelCase__ :      List[str]								=		self.feat_extract_tester.min_seq_length
						lowerCamelCase__ :      Optional[int]								=		self.feat_extract_tester.batch_size
						lowerCamelCase__ :      Dict								=		self.feat_extract_tester.feature_size

						# test padding for List[int] + numpy
						lowerCamelCase__ :      Optional[int]								=		feat_extract.pad(__magic_name__							,    padding=__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		input_a[input_name]

						lowerCamelCase__ :      Dict								=		feat_extract.pad(__magic_name__							,    padding="""longest"""				)
						lowerCamelCase__ :      Optional[Any]								=		input_a[input_name]

						lowerCamelCase__ :      Union[str, Any]								=		feat_extract.pad(__magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[-1]				)				)
						lowerCamelCase__ :      Optional[Any]								=		input_a[input_name]

						lowerCamelCase__ :      Tuple								=		feat_extract.pad(__magic_name__							,    padding="""longest"""							,    return_tensors="""np"""				)
						lowerCamelCase__ :      Union[str, Any]								=		input_a[input_name]

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

						lowerCamelCase__ :      List[Any]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=__magic_name__							,    return_tensors="""np"""				)
						lowerCamelCase__ :      int								=		input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(__magic_name__				)				)
						self.assertTrue(_inputs_have_equal_length(__magic_name__				)				)
						self.assertTrue(_inputs_have_equal_length(__magic_name__				)				)
						self.assertTrue(_inputs_are_equal(__magic_name__							,    __magic_name__				)				)
						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
						lowerCamelCase__ :      int								=		feat_extract.pad(__magic_name__							,    pad_to_multiple_of=10				)
						lowerCamelCase__ :      Union[str, Any]								=		input_a[input_name]

						lowerCamelCase__ :      Any								=		feat_extract.pad(__magic_name__							,    padding="""longest"""							,    pad_to_multiple_of=10				)
						lowerCamelCase__ :      List[Any]								=		input_a[input_name]

						lowerCamelCase__ :      Union[str, Any]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    pad_to_multiple_of=10							,    max_length=__magic_name__				)
						lowerCamelCase__ :      int								=		input_a[input_name]

						lowerCamelCase__ :      Any								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    pad_to_multiple_of=10							,    max_length=__magic_name__							,    return_tensors="""np"""							,    )
						lowerCamelCase__ :      Optional[Any]								=		input_a[input_name]

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

						lowerCamelCase__ :      str								=		pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
						self.assertTrue(all(len(__magic_name__				) == 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
						lowerCamelCase__ :      Dict								=		(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							,    __magic_name__=False				):
						def _inputs_have_equal_length(__magic_name__				):
									lowerCamelCase__ :      Optional[int]								=		len(input[0]				)
									for input_slice in input[1:]:
												if len(__magic_name__				) != length:
															return False
									return True

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

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

						lowerCamelCase__ :      Optional[Any]								=		self.feature_extraction_class(**self.feat_extract_dict				)
						lowerCamelCase__ :      Optional[Any]								=		self.feat_extract_tester.prepare_inputs_for_common(numpify=__magic_name__				)
						lowerCamelCase__ :      int								=		feat_extract.model_input_names[0]

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

						# truncate to smallest
						lowerCamelCase__ :      List[str]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[0]				)							,    truncation=__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		input_a[input_name]

						lowerCamelCase__ :      Union[str, Any]								=		feat_extract.pad(__magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[0]				)				)
						lowerCamelCase__ :      str								=		input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(__magic_name__				)				)
						self.assertFalse(_inputs_have_equal_length(__magic_name__				)				)

						# truncate to smallest with np
						lowerCamelCase__ :      Optional[int]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[0]				)							,    return_tensors="""np"""							,    truncation=__magic_name__							,    )
						lowerCamelCase__ :      List[Any]								=		input_a[input_name]

						lowerCamelCase__ :      Optional[int]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[0]				)							,    return_tensors="""np"""				)
						lowerCamelCase__ :      Dict								=		input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(__magic_name__				)				)
						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(__magic_name__				)				)

						# truncate to middle
						lowerCamelCase__ :      Union[str, Any]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[1]				)							,    truncation=__magic_name__							,    return_tensors="""np"""							,    )
						lowerCamelCase__ :      List[str]								=		input_a[input_name]

						lowerCamelCase__ :      Optional[Any]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[1]				)							,    truncation=__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		input_a[input_name]

						lowerCamelCase__ :      List[str]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[1]				)							,    return_tensors="""np"""				)
						lowerCamelCase__ :      Optional[Any]								=		input_a[input_name]

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

						# 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(__magic_name__				)				)
						self.assertTrue(len(input_a[-1]				) == len(speech_inputs[-1]				)				)

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

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

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

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

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						lowerCamelCase__ :      List[str]								=		12
						lowerCamelCase__ :      Optional[Any]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[0]				)							,    pad_to_multiple_of=__magic_name__							,    truncation=__magic_name__							,    )
						lowerCamelCase__ :      Optional[Any]								=		input_a[input_name]

						lowerCamelCase__ :      Union[str, Any]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=len(speech_inputs[0]				)							,    pad_to_multiple_of=__magic_name__							,    )
						lowerCamelCase__ :      int								=		input_a[input_name]

						# retrieve expected_length as multiple of pad_to_multiple_of
						lowerCamelCase__ :      Optional[Any]								=		len(speech_inputs[0]				)
						if expected_length % pad_to_multiple_of != 0:
									lowerCamelCase__ :      str								=		((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(__magic_name__				)				)
						self.assertFalse(_inputs_have_equal_length(__magic_name__				)				)





			def        _snake_case    (self				):
						self._check_padding(numpify=__magic_name__				)





			def        _snake_case    (self				):
						self._check_padding(numpify=__magic_name__				)





			def        _snake_case    (self				):
						self._check_truncation(numpify=__magic_name__				)





			def        _snake_case    (self				):
						self._check_truncation(numpify=__magic_name__				)





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

						lowerCamelCase__ :      Optional[Any]								=		BatchFeature({input_name: speech_inputs}				)

						lowerCamelCase__ :      Union[str, Any]								=		feat_extract.pad(__magic_name__							,    padding="""longest"""							,    return_tensors="""np"""				)[input_name]
						lowerCamelCase__ :      str								=		feat_extract.pad(__magic_name__							,    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				):
						lowerCamelCase__ :      List[str]								=		self.feature_extraction_class(**self.feat_extract_dict				)
						lowerCamelCase__ :      Optional[int]								=		self.feat_extract_tester.prepare_inputs_for_common()
						lowerCamelCase__ :      Optional[Any]								=		feat_extract.model_input_names[0]

						lowerCamelCase__ :      List[Any]								=		BatchFeature({input_name: speech_inputs}				)

						lowerCamelCase__ :      str								=		feat_extract.pad(__magic_name__							,    padding="""longest"""							,    return_tensors="""np"""				)[input_name]
						lowerCamelCase__ :      Dict								=		feat_extract.pad(__magic_name__							,    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				):
						lowerCamelCase__ :      Optional[int]								=		self.feat_extract_dict
						lowerCamelCase__ :      Tuple								=		True
						lowerCamelCase__ :      List[Any]								=		self.feature_extraction_class(**__magic_name__				)
						lowerCamelCase__ :      str								=		self.feat_extract_tester.prepare_inputs_for_common()
						lowerCamelCase__ :      Optional[int]								=		[len(__magic_name__				) for x in speech_inputs]
						lowerCamelCase__ :      Tuple								=		feat_extract.model_input_names[0]

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

						lowerCamelCase__ :      Union[str, Any]								=		feat_extract.pad(__magic_name__							,    padding="""longest"""							,    return_tensors="""np"""				)
						self.assertIn("""attention_mask"""							,    __magic_name__				)
						self.assertListEqual(list(processed.attention_mask.shape				)							,    list(processed[input_name].shape[:2]				)				)
						self.assertListEqual(processed.attention_mask.sum(-1				).tolist()							,    __magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		self.feat_extract_dict
						lowerCamelCase__ :      Tuple								=		True
						lowerCamelCase__ :      List[str]								=		self.feature_extraction_class(**__magic_name__				)
						lowerCamelCase__ :      Dict								=		self.feat_extract_tester.prepare_inputs_for_common()
						lowerCamelCase__ :      Optional[Any]								=		[len(__magic_name__				) for x in speech_inputs]
						lowerCamelCase__ :      Optional[Any]								=		feat_extract.model_input_names[0]

						lowerCamelCase__ :      Union[str, Any]								=		BatchFeature({input_name: speech_inputs}				)
						lowerCamelCase__ :      Union[str, Any]								=		min(__magic_name__				)

						lowerCamelCase__ :      Optional[int]								=		feat_extract.pad(
						    __magic_name__							,    padding="""max_length"""							,    max_length=__magic_name__							,    truncation=__magic_name__							,    return_tensors="""np"""				)
						self.assertIn("""attention_mask"""							,    __magic_name__				)
						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]				)








from string import ascii_uppercase

_lowercase     							=						{char: i for i, char in enumerate(ascii_uppercase)}
_lowercase     							=						dict(enumerate(ascii_uppercase))






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		len(UpperCamelCase )
			lowerCamelCase__ :      int								=		0
			while True:
						if x == i:
									lowerCamelCase__ :      Union[str, Any]								=		0
						if len(UpperCamelCase ) == len(UpperCamelCase ):
									break
						key += key[i]
						i += 1
			return key






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		""""""
			lowerCamelCase__ :      str								=		0
			for letter in message:
						if letter == " ":
									cipher_text += " "
						else:
									lowerCamelCase__ :      Optional[int]								=		(dicta[letter] - dicta[key_new[i]]) % 26
									i += 1
									cipher_text += dicta[x]
			return cipher_text






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Dict								=		""""""
			lowerCamelCase__ :      Tuple								=		0
			for letter in cipher_text:
						if letter == " ":
									or_txt += " "
						else:
									lowerCamelCase__ :      List[Any]								=		(dicta[letter] + dicta[key_new[i]] + 26) % 26
									i += 1
									or_txt += dicta[x]
			return or_txt






def      _A				()  ->None:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		"""THE GERMAN ATTACK"""
			lowerCamelCase__ :      Union[str, Any]								=		"""SECRET"""
			lowerCamelCase__ :      List[str]								=		generate_key(UpperCamelCase      ,			UpperCamelCase )
			lowerCamelCase__ :      List[Any]								=		cipher_text(UpperCamelCase      ,			UpperCamelCase )
			print(f"Encrypted Text = {s}" )
			print(f"Original Text = {original_text(UpperCamelCase      ,			UpperCamelCase )}" )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()



