




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer







class 		lowercase							(     UpperCamelCase__	):
     _a											=		"Wav2Vec2FeatureExtractor"
     _a											=		"AutoTokenizer"

     def __init__(						self	,  _a	,  _a       )		->							List[str]:
        super().__init__(_a	,  _a       )
        _A							:  int    =		self.feature_extractor
        _A							:  Optional[int]    =		False

     @classmethod
     def       a__			(						cls	,  _a	,  **_a       )		->							str:
        try:
           return super().from_pretrained(_a	,  **_a       )
        except OSError:
           warnings.warn(
               F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
               """ include a `tokenizer_class` attribute is deprecated and will be """
               """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
               """ attribute to either your `config.json` or `tokenizer_config.json` """
               """file to suppress this warning: """	,  _a	,  )

           _A							:  Optional[Any]    =		WavaVecaFeatureExtractor.from_pretrained(_a	,  **_a       )
           _A							:  str    =		WavaVecaCTCTokenizer.from_pretrained(_a	,  **_a       )

           return cls(feature_extractor=_a	,  tokenizer=_a       )

     def __call__(						self	,  *_a	,  **_a       )		->							List[Any]:
        # For backward compatibility
        if self._in_target_context_manager:
           return self.current_processor(*_a	,  **_a       )

        if "raw_speech" in kwargs:
           warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""       )
           _A							:  Dict    =		kwargs.pop("""raw_speech"""       )
        else:
           _A							:  int    =		kwargs.pop("""audio"""	,  _a       )
        _A							:  str    =		kwargs.pop("""sampling_rate"""	,  _a       )
        _A							:  Tuple    =		kwargs.pop("""text"""	,  _a       )
        if len(_a       ) > 0:
           _A							:  Dict    =		args[0]
           _A							:  Optional[Any]    =		args[1:]

        if audio is None and text is None:
           raise ValueError("""You need to specify either an `audio` or `text` input to process."""       )

        if audio is not None:
           _A							:  Optional[Any]    =		self.feature_extractor(_a	,  *_a	,  sampling_rate=_a	,  **_a       )
        if text is not None:
           _A							:  str    =		self.tokenizer(_a	,  **_a       )

        if text is None:
           return inputs
        elif audio is None:
           return encodings
        else:
           _A							:  Dict    =		encodings["""input_ids"""]
           return inputs

     def       a__			(						self	,  *_a	,  **_a       )		->							Dict:
        # For backward compatibility
        if self._in_target_context_manager:
           return self.current_processor.pad(*_a	,  **_a       )

        _A							:  Optional[Any]    =		kwargs.pop("""input_features"""	,  _a       )
        _A							:  Dict    =		kwargs.pop("""labels"""	,  _a       )
        if len(_a       ) > 0:
           _A							:  Union[str, Any]    =		args[0]
           _A							:  List[Any]    =		args[1:]

        if input_features is not None:
           _A							:  Dict    =		self.feature_extractor.pad(_a	,  *_a	,  **_a       )
        if labels is not None:
           _A							:  List[Any]    =		self.tokenizer.pad(_a	,  **_a       )

        if labels is None:
           return input_features
        elif input_features is None:
           return labels
        else:
           _A							:  List[str]    =		labels["""input_ids"""]
           return input_features

     def       a__			(						self	,  *_a	,  **_a       )		->							Optional[Any]:
        return self.tokenizer.batch_decode(*_a	,  **_a       )

     def       a__			(						self	,  *_a	,  **_a       )		->							Any:
        return self.tokenizer.decode(*_a	,  **_a       )



     @contextmanager
     def       a__			(						self       )		->							Optional[Any]:
        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your audio inputs, or in a separate call."""       )
        _A							:  List[str]    =		True
        _A							:  Optional[int]    =		self.tokenizer
        yield
        _A							:  List[str]    =		self.feature_extractor
        _A							:  str    =		False







from __future__ import annotations







class 		lowercase							:

     def __init__(						self	,  _a = 0       )		->							str:
           _A							:  Any    =		key

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  Any    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[str]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""encrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.encrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True



     def       a__			(						self	,  _a	,  _a       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""decrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.decrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")

