


'''simple docstring'''
import random
import sys

__SCREAMING_SNAKE_CASE				:							str		       =    '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def 			a_							(  ):
							A_ 						=		input("Enter message: "   )
							A_ 						=		"LFWOAYUISVKMNXPBDCRJTQEGHZ"
							A_ 						=		input("Encrypt/Decrypt [e/d]: "   )

							check_valid_key(UpperCamelCase_   )

							if resp.lower().startswith("e"   ):
														A_ 						=		"encrypt"
														A_ 						=		encrypt_message(UpperCamelCase_	,     UpperCamelCase_   )
							elif resp.lower().startswith("d"   ):
														A_ 						=		"decrypt"
														A_ 						=		decrypt_message(UpperCamelCase_	,     UpperCamelCase_   )

							print(f"\n{mode.title()}ion: \n{translated}"   )







def 			a_							(  UpperCamelCase_   ):
							A_ 						=		list(UpperCamelCase_   )
							A_ 						=		list(UpperCamelCase_   )
							key_list.sort()
							letters_list.sort()

							if key_list != letters_list:
														sys.exit("Error in the key or symbol set."   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							return translate_message(UpperCamelCase_	,     UpperCamelCase_	,     "encrypt"   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							return translate_message(UpperCamelCase_	,     UpperCamelCase_	,     "decrypt"   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		""
							A_ 						=		LETTERS
							A_ 						=		key

							if mode == "decrypt":
														A_						,				A_ 						=		chars_b, chars_a

							for symbol in message:
														if symbol.upper() in chars_a:
																					A_ 						=		chars_a.find(symbol.upper()   )
																					if symbol.isupper():
																												translated += chars_b[sym_index].upper()
																					else:
																												translated += chars_b[sym_index].lower()
														else:
																					translated += symbol

							return translated







def 			a_							(  ):
							A_ 						=		list(UpperCamelCase_   )
							random.shuffle(UpperCamelCase_   )
							return "".join(UpperCamelCase_   )


if __name__ == "__main__":
							main()









'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__SCREAMING_SNAKE_CASE				:							int		       =		get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[str]  ):
													A_ 						=		tempfile.mkdtemp()

													A_ 						=		["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
													A_ 						=		dict(zip(lowerCAmelCase   ,     range(len(lowerCAmelCase  )  )  )  )
													A_ 						=		Path(self.tmpdirname  )
													save_json(lowerCAmelCase   ,     save_dir / VOCAB_FILES_NAMES["vocab_file"]  )
													if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																				copyfile(lowerCAmelCase   ,     save_dir / VOCAB_FILES_NAMES["spm_file"]  )

													A_ 						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )
													tokenizer.save_pretrained(self.tmpdirname  )

													A_ 						=		{
													    "feature_size": 24,
													    "num_mel_bins": 24,
													    "padding_value": 0.0,
													    "sampling_rate": 1_60_00,
													    "return_attention_mask": False,
													    "do_normalize": True,
													}
													save_json(lowerCAmelCase   ,     save_dir / FEATURE_EXTRACTOR_NAME  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     **lowerCAmelCase       :			str  ):
													return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     **lowerCAmelCase       :			Optional[Any]  ):
													return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Any  ):
													shutil.rmtree(self.tmpdirname  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													A_ 						=		self.get_tokenizer()
													A_ 						=		self.get_feature_extractor()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													processor.save_pretrained(self.tmpdirname  )
													A_ 						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname  )

													self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()  )
													self.assertIsInstance(processor.tokenizer   ,     lowerCAmelCase  )

													self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()  )
													self.assertIsInstance(processor.feature_extractor   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Dict  ):
													A_ 						=		SpeechaTextProcessor(
													    tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()  )
													processor.save_pretrained(self.tmpdirname  )

													A_ 						=		self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"  )
													A_ 						=		self.get_feature_extractor(do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													A_ 						=		SpeechaTextProcessor.from_pretrained(
													    self.tmpdirname   ,     bos_token="(BOS)"   ,     eos_token="(EOS)"   ,     do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()  )
													self.assertIsInstance(processor.tokenizer   ,     lowerCAmelCase  )

													self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()  )
													self.assertIsInstance(processor.feature_extractor   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		floats_list((3, 10_00)  )

													A_ 						=		feature_extractor(lowerCAmelCase   ,     return_tensors="np"  )
													A_ 						=		processor(lowerCAmelCase   ,     return_tensors="np"  )

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2  )






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		"This is a test string"

													A_ 						=		processor(text=lowerCAmelCase  )

													A_ 						=		tokenizer(lowerCAmelCase  )

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]  )






						def 							_UpperCAmelCase					(     self       :			int  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													A_ 						=		processor.batch_decode(lowerCAmelCase  )
													A_ 						=		tokenizer.batch_decode(lowerCAmelCase  )

													self.assertListEqual(lowerCAmelCase   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													self.assertListEqual(
													    processor.model_input_names   ,     feature_extractor.model_input_names   ,     msg="`processor` and `feature_extractor` model input names do not match"   ,     )





