"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase_:					List[str]     =					{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					List[Any]     =					["DebertaTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Dict     =					[
     "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DebertaForMaskedLM",
     "DebertaForQuestionAnswering",
     "DebertaForSequenceClassification",
     "DebertaForTokenClassification",
     "DebertaModel",
     "DebertaPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Optional[Any]     =					[
     "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDebertaForMaskedLM",
     "TFDebertaForQuestionAnswering",
     "TFDebertaForSequenceClassification",
     "TFDebertaForTokenClassification",
     "TFDebertaModel",
     "TFDebertaPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

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





"""simple docstring"""


lowerCAmelCase_:					Any     =					"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          lowercase__				 =							f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(A      )

     lowercase__				 =							"".join(bin(A      )[2:].zfill(8      ) for byte in data      )

     lowercase__				 =							len(A      ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase__				 =							b"=" * ((6 - len(A      ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(A      ) % 6)
     else:
          lowercase__				 =							b""

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]    ,				2      )]
             for index in range(0    ,				len(A      )    ,				6      )      ).encode()
         + padding
     )







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ) and not isinstance(A    ,				A      ):
          lowercase__				 =							(
              "argument should be a bytes-like object or ASCII string, "
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(A      )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(A    ,				A      ):
          try:
               lowercase__				 =							encoded_data.decode("utf-8"      )
          except UnicodeDecodeError:
               raise ValueError("base64 encoded data should only contain ASCII characters"      )

     lowercase__				 =							encoded_data.count("="      )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]      ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data      ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(A      ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase__				 =							encoded_data[:-padding]

          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
     else:
          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )

     lowercase__				 =							[
         int(binary_stream[index : index + 8]    ,				2      )
         for index in range(0    ,				len(A      )    ,				8      )
     ]

     return bytes(A      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




