

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/config.json""",
    """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/config.json""",
    """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json""",
    """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json""",
    """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json""",
    """microsoft/deberta-xlarge-mnli""": """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json""",
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[int]						      ="deberta"







  def __init__(			self :    int       ,							lowerCAmelCase :    List[str]=5_02_65       ,							lowerCAmelCase :    Tuple=7_68       ,							lowerCAmelCase :    List[Any]=12       ,							lowerCAmelCase :    Any=12       ,							lowerCAmelCase :    Optional[int]=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu"       ,							lowerCAmelCase :    Optional[int]=0.1       ,							lowerCAmelCase :    Optional[Any]=0.1       ,							lowerCAmelCase :    List[str]=5_12       ,							lowerCAmelCase :    Dict=0       ,							lowerCAmelCase :    Optional[Any]=0.02       ,							lowerCAmelCase :    int=1e-7       ,							lowerCAmelCase :    List[Any]=False       ,							lowerCAmelCase :    List[Any]=-1       ,							lowerCAmelCase :    Dict=0       ,							lowerCAmelCase :    List[Any]=True       ,							lowerCAmelCase :    Tuple=None       ,							lowerCAmelCase :    Union[str, Any]=0       ,							lowerCAmelCase :    Optional[Any]="gelu"       ,							**lowerCAmelCase :    str       ,							)							->	int:






     """simple docstring"""







     super().__init__(**lowerCAmelCase    )

     __lowerCAmelCase  :			Dict					    =						hidden_size
     __lowerCAmelCase  :			List[str]					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			Any					    =						intermediate_size
     __lowerCAmelCase  :			Union[str, Any]					    =						hidden_act
     __lowerCAmelCase  :			Union[str, Any]					    =						hidden_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			Optional[int]					    =						max_position_embeddings
     __lowerCAmelCase  :			int					    =						type_vocab_size
     __lowerCAmelCase  :			Optional[int]					    =						initializer_range
     __lowerCAmelCase  :			List[Any]					    =						relative_attention
     __lowerCAmelCase  :			List[str]					    =						max_relative_positions
     __lowerCAmelCase  :			List[Any]					    =						pad_token_id
     __lowerCAmelCase  :			Optional[int]					    =						position_biased_input

     # Backwards compatibility
     if type(lowerCAmelCase    ) == str:
        __lowerCAmelCase  :			List[Any]					    =						[x.strip() for x in pos_att_type.lower().split("""|"""    )]

     __lowerCAmelCase  :			int					    =						pos_att_type
     __lowerCAmelCase  :			int					    =						vocab_size
     __lowerCAmelCase  :			List[str]					    =						layer_norm_eps

     __lowerCAmelCase  :			Any					    =						kwargs.get("""pooler_hidden_size"""       ,							lowerCAmelCase    )
     __lowerCAmelCase  :			Dict					    =						pooler_dropout
     __lowerCAmelCase  :			Optional[int]					    =						pooler_hidden_act






class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Mapping[str, Mapping[int, str]]:






     """simple docstring"""







     if self.task == "multiple-choice":
        __lowerCAmelCase  :			Tuple					    =						{0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
        __lowerCAmelCase  :			Tuple					    =						{0: """batch""", 1: """sequence"""}
     if self._config.type_vocab_size > 0:
        return OrderedDict(
            [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]    )
     else:
        return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]    )







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Any    )							->	int:






     """simple docstring"""







     return 12







  def        SCREAMING_SNAKE_CASE    (			self :    Any       ,							lowerCAmelCase :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    bool = False       ,							lowerCAmelCase :    Optional["TensorType"] = None       ,							lowerCAmelCase :    int = 3       ,							lowerCAmelCase :    int = 40       ,							lowerCAmelCase :    int = 40       ,							lowerCAmelCase :    "PreTrainedTokenizerBase" = None       ,							)							->	Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						super().generate_dummy_inputs(preprocessor=lowerCAmelCase       ,							framework=lowerCAmelCase    )
     if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
        del dummy_inputs["token_type_ids"]
     return dummy_inputs





from math import sqrt





def 	snake_case_      (__A     :						int     )			->	list[int]:
   __lowerCAmelCase  :			List[str]					    =						[1]
   for i in range(2					,   int(sqrt(__A     )     ) + 1					,   1     ):
      if number % i == 0:
         values.append(__A     )
         if int(number // i     ) != i:
            values.append(int(number // i     )     )
   return sorted(__A     )





def 	snake_case_      (__A     :						int     )			->	bool:
   return sum(factors(__A     )     ) > n





def 	snake_case_      (__A     :						int     )			->	bool:
   __lowerCAmelCase  :			Any					    =						factors(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						len(__A     )
   __lowerCAmelCase  :			str					    =						[[0 for i in range(number + 1     )] for j in range(r + 1     )]
   for i in range(r + 1     ):
      __lowerCAmelCase  :			Tuple					    =						True

   for i in range(1					,   number + 1     ):
      __lowerCAmelCase  :			List[str]					    =						False

   for i in range(1					,   r + 1     ):
      for j in range(1					,   number + 1     ):
         if j < values[i - 1]:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j]
         else:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0





def 	snake_case_      (__A     :						int     )			->	bool:
   return abundant(__A     ) and not semi_perfect(__A     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F'{number} is {"" if weird(number) else "not "}weird.')


