
'''simple docstring'''
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 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						def 					__A					( self				:					List[str]  )							->					Dict:
										__magic_name__	  = '''<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(" "  )
										__magic_name__	  = dict(zip(_snake_case	,				range(len(_snake_case  )  )  )  )

										__magic_name__	  = {
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										__magic_name__	  = {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_60_00,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										__magic_name__	  = tempfile.mkdtemp()
										__magic_name__	  = os.path.join(self.tmpdirname	,				VOCAB_FILES_NAMES["vocab_file"]  )
										__magic_name__	  = 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 					__A					( self				:					Any	,				**_lowerCamelCase				:					List[Any]  )							->					Optional[int]:
										__magic_name__	  = self.add_kwargs_tokens_map.copy()
										kwargs.update(_snake_case  )
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,				**_snake_case  )
						def 					__A					( self				:					List[Any]	,				**_lowerCamelCase				:					int  )							->					List[str]:
										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,				**_snake_case  )
						def 					__A					( self				:					Optional[Any]  )							->					Optional[Any]:
										shutil.rmtree(self.tmpdirname  )
						def 					__A					( self				:					Union[str, Any]  )							->					List[str]:
										__magic_name__	  = self.get_tokenizer()
										__magic_name__	  = self.get_feature_extractor()

										__magic_name__	  = WavaVecaProcessor(tokenizer=_snake_case	,				feature_extractor=_snake_case  )

										processor.save_pretrained(self.tmpdirname  )
										__magic_name__	  = 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 					__A					( self				:					int  )							->					Optional[int]:
										__magic_name__	  = WavaVecaProcessor(tokenizer=self.get_tokenizer()	,				feature_extractor=self.get_feature_extractor()  )
										processor.save_pretrained(self.tmpdirname  )

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

										__magic_name__	  = 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 					__A					( self				:					Tuple  )							->					List[Any]:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = WavaVecaProcessor(tokenizer=_snake_case	,				feature_extractor=_snake_case  )

										__magic_name__	  = floats_list((3, 10_00)  )

										__magic_name__	  = feature_extractor(_snake_case	,				return_tensors="np"  )
										__magic_name__	  = 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 					__A					( self				:					str  )							->					Optional[int]:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = WavaVecaProcessor(tokenizer=_snake_case	,				feature_extractor=_snake_case  )

										__magic_name__	  = '''This is a test string'''

										__magic_name__	  = processor(text=_snake_case  )

										__magic_name__	  = tokenizer(_snake_case  )

										for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]	,				encoded_processor[key]  )
						def 					__A					( self				:					Any  )							->					Optional[Any]:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = WavaVecaProcessor(tokenizer=_snake_case	,				feature_extractor=_snake_case  )

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

										__magic_name__	  = processor.batch_decode(_snake_case  )
										__magic_name__	  = tokenizer.batch_decode(_snake_case  )

										self.assertListEqual(_snake_case	,				_snake_case  )

						def 					__A					( self				:					Optional[int]  )							->					Tuple:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = 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"	,				)







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				barcode //= 10  # exclude the last digit
				__magic_name__	  = False
				__magic_name__	  = 0

				# extract and check each digit
				while barcode != 0:
								__magic_name__	  = 1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__magic_name__	  = not checker

				return (10 - (s % 10)) % 10




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				return len(str(lowerCamelCase_						)						) == 13 and get_check_digit(lowerCamelCase_						) == barcode % 10




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				if str(lowerCamelCase_						).isalpha():
								__magic_name__	  = F'Barcode \'{barcode}\' has alphabetic characters.'
								raise ValueError(lowerCamelCase_						)
				elif int(lowerCamelCase_						) < 0:
								raise ValueError("The entered barcode has a negative value. Try again."						)
				else:
								return int(lowerCamelCase_						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__magic_name__   :   Optional[int]  							=get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(F'''\'{barcode}\' is a valid barcode.''')
						else:
												print(F'''\'{barcode}\' is NOT a valid barcode.''')



