


'''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 					snake_case   (   unittest.TestCase      ):





  """simple docstring"""




  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  "<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(" "					)
   lowerCamelCase_								=  dict(zip(UpperCamelCase      ,  range(len(UpperCamelCase					)					)					)					)

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

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

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






  def   snake_case		(   self      ,  **UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  self.add_kwargs_tokens_map.copy()
   kwargs.update(UpperCamelCase					)
   return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,  **UpperCamelCase					)






  def   snake_case		(   self      ,  **UpperCamelCase					):







   """simple docstring"""
   return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,  **UpperCamelCase					)






  def   snake_case		(   self					):







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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.get_tokenizer()
   lowerCamelCase_								=  self.get_feature_extractor()

   lowerCamelCase_								=  WavaVecaProcessor(tokenizer=UpperCamelCase      ,  feature_extractor=UpperCamelCase					)

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

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

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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  WavaVecaProcessor(tokenizer=self.get_tokenizer()      ,  feature_extractor=self.get_feature_extractor()					)
   processor.save_pretrained(self.tmpdirname					)

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

   lowerCamelCase_								=  WavaVecaProcessor.from_pretrained(
       self.tmpdirname      ,  bos_token="(BOS)"      ,  eos_token="(EOS)"      ,  do_normalize=UpperCamelCase      ,  padding_value=1.0					)

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

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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.get_feature_extractor()
   lowerCamelCase_								=  self.get_tokenizer()

   lowerCamelCase_								=  WavaVecaProcessor(tokenizer=UpperCamelCase      ,  feature_extractor=UpperCamelCase					)

   lowerCamelCase_								=  floats_list((3, 1000)					)

   lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  return_tensors="np"					)
   lowerCamelCase_								=  processor(UpperCamelCase      ,  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   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.get_feature_extractor()
   lowerCamelCase_								=  self.get_tokenizer()

   lowerCamelCase_								=  WavaVecaProcessor(tokenizer=UpperCamelCase      ,  feature_extractor=UpperCamelCase					)

   lowerCamelCase_								=  "This is a test string"

   lowerCamelCase_								=  processor(text=UpperCamelCase					)

   lowerCamelCase_								=  tokenizer(UpperCamelCase					)

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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.get_feature_extractor()
   lowerCamelCase_								=  self.get_tokenizer()

   lowerCamelCase_								=  WavaVecaProcessor(tokenizer=UpperCamelCase      ,  feature_extractor=UpperCamelCase					)

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

   lowerCamelCase_								=  processor.batch_decode(UpperCamelCase					)
   lowerCamelCase_								=  tokenizer.batch_decode(UpperCamelCase					)

   self.assertListEqual(UpperCamelCase      ,  UpperCamelCase					)







  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.get_feature_extractor()
   lowerCamelCase_								=  self.get_tokenizer()

   lowerCamelCase_								=  WavaVecaProcessor(tokenizer=UpperCamelCase      ,  feature_extractor=UpperCamelCase					)

   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 (				UpperCAmelCase_						:						float		):
 return -num if num < 0 else num





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_min() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs_val(UpperCAmelCase_		) < abs_val(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs(UpperCAmelCase_		) > abs(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max_sort() arg is an empty sequence"		)
 return sorted(UpperCAmelCase_ ,    key=UpperCAmelCase_		)[-1]





def __snake_case (				):
 assert abs_val(0		) == 0
 assert abs_val(34		) == 34
 assert abs_val(-100000000000		) == 100000000000

 lowerCamelCase_								=  [-3, -1, 2, -11]
 assert abs_max(UpperCAmelCase_		) == -11
 assert abs_max_sort(UpperCAmelCase_		) == -11
 assert abs_min(UpperCAmelCase_		) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34



