"""simple docstring"""


from __future__ import annotations






class    a__    :


   def __init__(     self,				_UpperCAmelCase = 0  ):
        '''simple docstring'''



        lowercase__				 =							key


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        assert isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) and isinstance(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							key or self.__key or 1

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

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        assert isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) and isinstance(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							key or self.__key or 1

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

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = 0  ):
        '''simple docstring'''



        assert isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) and isinstance(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							key or self.__key or 1

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

        # This will be returned
        lowercase__				 =							""

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

        return ans


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = 0  ):
        '''simple docstring'''



        assert isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) and isinstance(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							key or self.__key or 1

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

        # This will be returned
        lowercase__				 =							""

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

        return ans


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = 0  ):
        '''simple docstring'''



        assert isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) and isinstance(_UpperCAmelCase,				_UpperCAmelCase  )

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

        except OSError:
             return False

        return True


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        assert isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) and isinstance(_UpperCAmelCase,				_UpperCAmelCase  )

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

        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")





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_:					Union[str, Any]     =					{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Union[str, Any]     =					["DistilBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Any     =					[
     "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DistilBertForMaskedLM",
     "DistilBertForMultipleChoice",
     "DistilBertForQuestionAnswering",
     "DistilBertForSequenceClassification",
     "DistilBertForTokenClassification",
     "DistilBertModel",
     "DistilBertPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Tuple     =					[
     "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDistilBertForMaskedLM",
     "TFDistilBertForMultipleChoice",
     "TFDistilBertForQuestionAnswering",
     "TFDistilBertForSequenceClassification",
     "TFDistilBertForTokenClassification",
     "TFDistilBertMainLayer",
     "TFDistilBertModel",
     "TFDistilBertPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Optional[Any]     =					[
     "FlaxDistilBertForMaskedLM",
     "FlaxDistilBertForMultipleChoice",
     "FlaxDistilBertForQuestionAnswering",
     "FlaxDistilBertForSequenceClassification",
     "FlaxDistilBertForTokenClassification",
     "FlaxDistilBertModel",
     "FlaxDistilBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase_:					Optional[int]     =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




