





from __future__ import annotations







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = 0      )				->  int:
      """simple docstring"""





      a__	:						Optional[Any]      		=  key





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  list[str]:
      """simple docstring"""





      assert isinstance(snake_case					,			snake_case      ) and isinstance(snake_case					,			snake_case      )

      a__	:						Union[str, Any]      		=  key or self.__key or 1

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

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





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  list[str]:
      """simple docstring"""





      assert isinstance(snake_case					,			snake_case      ) and isinstance(snake_case					,			snake_case      )

      a__	:						Optional[Any]      		=  key or self.__key or 1

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

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





   def 	_snake_case ( self					,			snake_case					,			snake_case = 0      )				->  str:
      """simple docstring"""





      assert isinstance(snake_case					,			snake_case      ) and isinstance(snake_case					,			snake_case      )

      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__	:						Dict      		=  ""

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

      return ans





   def 	_snake_case ( self					,			snake_case					,			snake_case = 0      )				->  str:
      """simple docstring"""





      assert isinstance(snake_case					,			snake_case      ) and isinstance(snake_case					,			snake_case      )

      a__	:						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(snake_case      ) ^ key      )

      return ans





   def 	_snake_case ( self					,			snake_case					,			snake_case = 0      )				->  bool:
      """simple docstring"""





      assert isinstance(snake_case					,			snake_case      ) and isinstance(snake_case					,			snake_case      )

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

      except OSError:
         return False

      return True





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  bool:
      """simple docstring"""





      assert isinstance(snake_case					,			snake_case      ) and isinstance(snake_case					,			snake_case      )

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

      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")










from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :			str			      =						{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						["""DistilBertTokenizerFast"""]

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

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						[
      """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:
  SCREAMING_SNAKE_CASE__       :			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

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

