




'''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 			A    (		unittest.TestCase    ):







							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							List[str]:


														"""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(lowerCAmelCase_							,			range(len(lowerCAmelCase_					)					)					)					)

														_a	     =				{
														    '''pad_token''': '''<pad>''',
														    '''unk_token''': '''<unk>''',
														    '''bos_token''': '''<s>''',
														    '''eos_token''': '''</s>''',
														}
														_a	     =				{
														    '''feature_size''': 1,
														    '''padding_value''': 0.0,
														    '''sampling_rate''': 1_60_00,
														    '''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							,			lowerCAmelCase_					)
														with open(self.vocab_file							,			'''w'''							,			encoding='''utf-8'''					) as fp:
																					fp.write(json.dumps(lowerCAmelCase_					) + '''\n'''					)

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







							def    __lowerCAmelCase			(  self       :  Dict							,			**lowerCAmelCase_       :  Dict					)			->							Dict:


														"""simple docstring"""


														_a	     =				self.add_kwargs_tokens_map.copy()
														kwargs.update(lowerCAmelCase_					)
														return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname							,			**lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Any							,			**lowerCAmelCase_       :  int					)			->							List[Any]:


														"""simple docstring"""


														return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname							,			**lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							Tuple:


														"""simple docstring"""


														shutil.rmtree(self.tmpdirname					)







							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							List[str]:


														"""simple docstring"""


														_a	     =				self.get_tokenizer()
														_a	     =				self.get_feature_extractor()

														_a	     =				WavaVecaProcessor(tokenizer=lowerCAmelCase_							,			feature_extractor=lowerCAmelCase_					)

														processor.save_pretrained(self.tmpdirname					)
														_a	     =				WavaVecaProcessor.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    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							Dict:


														"""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=lowerCAmelCase_							,			padding_value=1.0					)

														_a	     =				WavaVecaProcessor.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    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Any:


														"""simple docstring"""


														_a	     =				self.get_feature_extractor()
														_a	     =				self.get_tokenizer()

														_a	     =				WavaVecaProcessor(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    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							List[str]:


														"""simple docstring"""


														_a	     =				self.get_feature_extractor()
														_a	     =				self.get_tokenizer()

														_a	     =				WavaVecaProcessor(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    __lowerCAmelCase			(  self       :  Tuple					)			->							Optional[Any]:


														"""simple docstring"""


														_a	     =				self.get_feature_extractor()
														_a	     =				self.get_tokenizer()

														_a	     =				WavaVecaProcessor(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    __lowerCAmelCase			(  self       :  Optional[int]					)			->							List[Any]:


														"""simple docstring"""


														_a	     =				self.get_feature_extractor()
														_a	     =				self.get_tokenizer()

														_a	     =				WavaVecaProcessor(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'''							,			)







'''simple docstring'''


from __future__ import annotations
def 		snake_case_    (UpperCamelCase       :  int      ):





							'''simple docstring'''


							_a	     =				[0] * max_one

							for i in range(0 ,						UpperCamelCase      ):
														_a	     =				i

							for i in range(2 ,						UpperCamelCase      ):
														if totients[i] == i:
																					for j in range(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      ):
																												totients[j] -= totients[j] // i

							return totients
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							return sorted(str(UpperCamelCase      )      ) == sorted(str(UpperCamelCase      )      )
def 		snake_case_    (UpperCamelCase       :  int = 1000_0000      ):





							'''simple docstring'''


							_a	     =				1  # i
							_a	     =				0  # φ(i)
							_a	     =				get_totients(max_n + 1      )

							for i in range(2 ,						max_n + 1      ):
														_a	     =				totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCamelCase ,						UpperCamelCase      ):
																					_a	     =				i
																					_a	     =				t

							return min_numerator


if __name__ == "__main__":
				print(F'''{solution() = }''')

