'''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 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = '<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(' '		)
       UpperCAmelCase_				:  Optional[Any]  = dict(zip(snake_case_ ,						range(len(snake_case_		)		)		)		)

       UpperCAmelCase_				:  List[str]  = {
           'pad_token': '<pad>',
           'unk_token': '<unk>',
           'bos_token': '<s>',
           'eos_token': '</s>',
       }
       UpperCAmelCase_				:  Optional[Any]  = {
           'feature_size': 1,
           'padding_value': 0.0,
           'sampling_rate': 1_6_0_0_0,
           'return_attention_mask': False,
           'do_normalize': True,
       }

       UpperCAmelCase_				:  Any  = tempfile.mkdtemp()
       UpperCAmelCase_				:  Optional[int]  = os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['vocab_file']		)
       UpperCAmelCase_				:  List[Any]  = 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 _UpperCamelCase	(					self ,						**snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = self.add_kwargs_tokens_map.copy()
       kwargs.update(snake_case_		)
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,						**snake_case_		)


  def _UpperCamelCase	(					self ,						**snake_case_		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       shutil.rmtree(self.tmpdirname		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_tokenizer()
       UpperCAmelCase_				:  Union[str, Any]  = self.get_feature_extractor()

       UpperCAmelCase_				:  List[str]  = WavaVecaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       processor.save_pretrained(self.tmpdirname		)
       UpperCAmelCase_				:  Optional[Any]  = 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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = WavaVecaProcessor(tokenizer=self.get_tokenizer() ,						feature_extractor=self.get_feature_extractor()		)
       processor.save_pretrained(self.tmpdirname		)

       UpperCAmelCase_				:  Tuple  = self.get_tokenizer(bos_token='(BOS)' ,						eos_token='(EOS)'		)
       UpperCAmelCase_				:  Optional[int]  = self.get_feature_extractor(do_normalize=snake_case_ ,						padding_value=1.0		)

       UpperCAmelCase_				:  int  = 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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  List[Any]  = self.get_tokenizer()

       UpperCAmelCase_				:  str  = WavaVecaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  List[str]  = floats_list((3, 1_0_0_0)		)

       UpperCAmelCase_				:  Dict  = feature_extractor(snake_case_ ,						return_tensors='np'		)
       UpperCAmelCase_				:  Any  = 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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Tuple  = self.get_tokenizer()

       UpperCAmelCase_				:  Optional[int]  = WavaVecaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Tuple  = 'This is a test string'

       UpperCAmelCase_				:  Optional[Any]  = processor(text=snake_case_		)

       UpperCAmelCase_				:  Tuple  = tokenizer(snake_case_		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  List[Any]  = self.get_tokenizer()

       UpperCAmelCase_				:  List[str]  = WavaVecaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCAmelCase_				:  List[Any]  = processor.batch_decode(snake_case_		)
       UpperCAmelCase_				:  List[str]  = tokenizer.batch_decode(snake_case_		)

       self.assertListEqual(snake_case_ ,						snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  int  = self.get_tokenizer()

       UpperCAmelCase_				:  Any  = 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' ,						)









from math import pi



def   UpperCamelCase(   lowercase_				)      ->							float:





  '''simple docstring'''





  return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()