

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list






class 			__magic_name__					(    unittest.TestCase   ):





			def 	__magic_name__					(	self      )     ->				List[Any]:




						'''simple docstring'''
						__a						   ='<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '      )
						__a						   =dict(zip(__snake_case      ,						range(len(__snake_case      )      )      )      )

						__a						   ={
						    'pad_token': '<pad>',
						    'unk_token': '<unk>',
						    'bos_token': '<s>',
						    'eos_token': '</s>',
						}
						__a						   ={
						    'feature_size': 1,
						    'padding_value': 0.0,
						    'sampling_rate': 1_6000,
						    'return_attention_mask': False,
						    'do_normalize': True,
						}

						__a						   =tempfile.mkdtemp()
						__a						   =os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']      )
						__a						   =os.path.join(self.tmpdirname      ,						__snake_case      )
						with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'      ) as fp:
									fp.write(json.dumps(__snake_case      ) + '\n'      )

						with open(self.feature_extraction_file      ,						'w'      ,						encoding='utf-8'      ) as fp:
									fp.write(json.dumps(__snake_case      ) + '\n'      )





			def 	__magic_name__					(	self      ,						**__snake_case      )     ->				int:




						'''simple docstring'''
						__a						   =self.add_kwargs_tokens_map.copy()
						kwargs.update(__snake_case      )
						return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,						**__snake_case      )





			def 	__magic_name__					(	self      ,						**__snake_case      )     ->				Optional[int]:




						'''simple docstring'''
						return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,						**__snake_case      )





			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						shutil.rmtree(self.tmpdirname      )





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =self.get_tokenizer()
						__a						   =self.get_feature_extractor()

						__a						   =WavaVecaProcessor(tokenizer=__snake_case      ,						feature_extractor=__snake_case      )

						processor.save_pretrained(self.tmpdirname      )
						__a						   =WavaVecaProcessor.from_pretrained(self.tmpdirname      )

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

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





			def 	__magic_name__					(	self      )     ->				List[Any]:




						'''simple docstring'''
						__a						   =WavaVecaProcessor(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=__snake_case      ,						padding_value=1.0      )

						__a						   =WavaVecaProcessor.from_pretrained(
						    self.tmpdirname      ,						bos_token='(BOS)'      ,						eos_token='(EOS)'      ,						do_normalize=__snake_case      ,						padding_value=1.0      )

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

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





			def 	__magic_name__					(	self      )     ->				List[Any]:




						'''simple docstring'''
						__a						   =self.get_feature_extractor()
						__a						   =self.get_tokenizer()

						__a						   =WavaVecaProcessor(tokenizer=__snake_case      ,						feature_extractor=__snake_case      )

						__a						   =floats_list((3, 1000)      )

						__a						   =feature_extractor(__snake_case      ,						return_tensors='np'      )
						__a						   =processor(__snake_case      ,						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 	__magic_name__					(	self      )     ->				Union[str, Any]:




						'''simple docstring'''
						__a						   =self.get_feature_extractor()
						__a						   =self.get_tokenizer()

						__a						   =WavaVecaProcessor(tokenizer=__snake_case      ,						feature_extractor=__snake_case      )

						__a						   ='This is a test string'

						__a						   =processor(text=__snake_case      )

						__a						   =tokenizer(__snake_case      )

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





			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						__a						   =self.get_feature_extractor()
						__a						   =self.get_tokenizer()

						__a						   =WavaVecaProcessor(tokenizer=__snake_case      ,						feature_extractor=__snake_case      )

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

						__a						   =processor.batch_decode(__snake_case      )
						__a						   =tokenizer.batch_decode(__snake_case      )

						self.assertListEqual(__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a						   =self.get_feature_extractor()
						__a						   =self.get_tokenizer()

						__a						   =WavaVecaProcessor(tokenizer=__snake_case      ,						feature_extractor=__snake_case      )

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









import os
import sys
import time

from . import transposition_cipher as trans_cipher






def  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   ='Prehistoric Men.txt'
			__a						   ='Output.txt'
			__a						   =int(input('Enter key: '			)			)
			__a						   =input('Encrypt/Decrypt [e/d]: '			)

			if not os.path.exists(_snake_case			):
						print(F'File {input_file} does not exist. Quitting...'			)
						sys.exit()
			if os.path.exists(_snake_case			):
						print(F'Overwrite {output_file}? [y/n]'			)
						__a						   =input('> '			)
						if not response.lower().startswith('y'			):
									sys.exit()

			__a						   =time.time()
			if mode.lower().startswith('e'			):
						with open(_snake_case			) as f:
									__a						   =f.read()
						__a						   =trans_cipher.encrypt_message(_snake_case ,  _snake_case			)
			elif mode.lower().startswith('d'			):
						with open(_snake_case			) as f:
									__a						   =f.read()
						__a						   =trans_cipher.decrypt_message(_snake_case ,  _snake_case			)

			with open(_snake_case ,  'w'			) as output_obj:
						output_obj.write(_snake_case			)

			__a						   =round(time.time() - start_time ,  2			)
			print(('Done (', total_time, 'seconds )')			)


if __name__ == "__main__":
						main()






