



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 		_snake_case      (			unittest.TestCase				):





 """simple docstring"""



 def lowercase_		(						self					)	->							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(a	,    range(len(a					)					)					)					)

   _A           =    {
       '''pad_token''': '''<pad>''',
       '''unk_token''': '''<unk>''',
       '''bos_token''': '''<s>''',
       '''eos_token''': '''</s>''',
   }
   _A           =    {
       '''feature_size''': 1,
       '''padding_value''': 0.0,
       '''sampling_rate''': 1_6_0_0_0,
       '''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	,    a					)
   with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''					) as fp:
     fp.write(json.dumps(a					) + '''\n'''					)

   with open(self.feature_extraction_file	,    '''w'''	,    encoding='''utf-8'''					) as fp:
     fp.write(json.dumps(a					) + '''\n'''					)
 def lowercase_		(						self	,    **a					)	->							Union[str, Any]:




   """simple docstring"""







   _A           =    self.add_kwargs_tokens_map.copy()
   kwargs.update(a					)
   return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,    **a					)
 def lowercase_		(						self	,    **a					)	->							Any:




   """simple docstring"""







   return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,    **a					)
 def lowercase_		(						self					)	->							List[Any]:




   """simple docstring"""







   shutil.rmtree(self.tmpdirname					)
 def lowercase_		(						self					)	->							str:




   """simple docstring"""







   _A           =    self.get_tokenizer()
   _A           =    self.get_feature_extractor()

   _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)

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

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

   self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()					)
   self.assertIsInstance(processor.feature_extractor	,    a					)
 def lowercase_		(						self					)	->							List[str]:




   """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=a	,    padding_value=1.0					)

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

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

   self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()					)
   self.assertIsInstance(processor.feature_extractor	,    a					)
 def lowercase_		(						self					)	->							int:




   """simple docstring"""







   _A           =    self.get_feature_extractor()
   _A           =    self.get_tokenizer()

   _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)

   _A           =    floats_list((3, 1_0_0_0)					)

   _A           =    feature_extractor(a	,    return_tensors='''np'''					)
   _A           =    processor(a	,    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 lowercase_		(						self					)	->							str:




   """simple docstring"""







   _A           =    self.get_feature_extractor()
   _A           =    self.get_tokenizer()

   _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)

   _A           =    '''This is a test string'''

   _A           =    processor(text=a					)

   _A           =    tokenizer(a					)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]					)
 def lowercase_		(						self					)	->							str:




   """simple docstring"""







   _A           =    self.get_feature_extractor()
   _A           =    self.get_tokenizer()

   _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)

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

   _A           =    processor.batch_decode(a					)
   _A           =    tokenizer.batch_decode(a					)

   self.assertListEqual(a	,    a					)



 def lowercase_		(						self					)	->							int:




   """simple docstring"""







   _A           =    self.get_feature_extractor()
   _A           =    self.get_tokenizer()

   _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)

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




from ..utils import requires_backends


__lowerCamelCase  				=					None



class 		_snake_case      :





 """simple docstring"""



 def __init__(						self	,    *a	,    **a					)	->							Tuple:




   """simple docstring"""







   requires_backends(self	,    ['''detectron2''']					)



 @classmethod
 def lowercase_		(						cls	,    *a	,    **a					)	->							Optional[int]:




   """simple docstring"""







   requires_backends(cls	,    ['''detectron2''']					)