


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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    "<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			: int   =    dict(zip(__A						,    range(len(__A			)			)			)			)

     lowerCamelCase			: str   =    {
         "pad_token": "<pad>",
         "unk_token": "<unk>",
         "bos_token": "<s>",
         "eos_token": "</s>",
     }
     lowerCamelCase			: Union[str, Any]   =    {
         "feature_size": 1,
         "padding_value": 0.0,
         "sampling_rate": 1_6000,
         "return_attention_mask": False,
         "do_normalize": True,
     }

     lowerCamelCase			: Any   =    tempfile.mkdtemp()
     lowerCamelCase			: int   =    os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES["vocab_file"]			)
     lowerCamelCase			: str   =    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 _snake_case   (		self						,    **__A			):






     """simple docstring"""





     lowerCamelCase			: str   =    self.add_kwargs_tokens_map.copy()
     kwargs.update(__A			)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,    **__A			)






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





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






   def _snake_case   (		self			):






     """simple docstring"""





     shutil.rmtree(self.tmpdirname			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    self.get_tokenizer()
     lowerCamelCase			: Dict   =    self.get_feature_extractor()

     lowerCamelCase			: List[Any]   =    WavaVecaProcessor(tokenizer=__A						,    feature_extractor=__A			)

     processor.save_pretrained(self.tmpdirname			)
     lowerCamelCase			: Optional[Any]   =    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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    WavaVecaProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase			: Optional[Any]   =    self.get_tokenizer(bos_token="(BOS)"						,    eos_token="(EOS)"			)
     lowerCamelCase			: Tuple   =    self.get_feature_extractor(do_normalize=__A						,    padding_value=1.0			)

     lowerCamelCase			: Optional[int]   =    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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.get_feature_extractor()
     lowerCamelCase			: Optional[int]   =    self.get_tokenizer()

     lowerCamelCase			: int   =    WavaVecaProcessor(tokenizer=__A						,    feature_extractor=__A			)

     lowerCamelCase			: List[Any]   =    floats_list((3, 1000)			)

     lowerCamelCase			: str   =    feature_extractor(__A						,    return_tensors="np"			)
     lowerCamelCase			: List[str]   =    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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    self.get_feature_extractor()
     lowerCamelCase			: Dict   =    self.get_tokenizer()

     lowerCamelCase			: List[Any]   =    WavaVecaProcessor(tokenizer=__A						,    feature_extractor=__A			)

     lowerCamelCase			: Optional[Any]   =    "This is a test string"

     lowerCamelCase			: int   =    processor(text=__A			)

     lowerCamelCase			: List[str]   =    tokenizer(__A			)

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






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    self.get_feature_extractor()
     lowerCamelCase			: Union[str, Any]   =    self.get_tokenizer()

     lowerCamelCase			: List[Any]   =    WavaVecaProcessor(tokenizer=__A						,    feature_extractor=__A			)

     lowerCamelCase			: str   =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase			: Union[str, Any]   =    processor.batch_decode(__A			)
     lowerCamelCase			: str   =    tokenizer.batch_decode(__A			)

     self.assertListEqual(__A						,    __A			)






   def _snake_case   (		self			):






     """simple docstring"""





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

     lowerCamelCase			: str   =    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"						,    )







def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if number != int(SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if number < 0:
    raise ValueError("factorial() not defined for negative values"     )
  lowerCamelCase			: Optional[Any]   =    1
  for i in range(1				,						number + 1     ):
    value *= i
  return value






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if not isinstance(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if n < 0:
    raise ValueError("factorial() not defined for negative values"     )
  return 1 if n == 0 or n == 1 else n * factorial(n - 1     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _snake_case					=					int(input('''Enter a positive integer: ''').strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')



