
"""simple docstring"""






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 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Union[str, Any]     					=     """deberta"""







   def __init__( self       ,							__UpperCAmelCase=5_02_65       ,							__UpperCAmelCase=7_68       ,							__UpperCAmelCase=12       ,							__UpperCAmelCase=12       ,							__UpperCAmelCase=30_72       ,							__UpperCAmelCase="gelu"       ,							__UpperCAmelCase=0.1       ,							__UpperCAmelCase=0.1       ,							__UpperCAmelCase=5_12       ,							__UpperCAmelCase=0       ,							__UpperCAmelCase=0.02       ,							__UpperCAmelCase=1E-7       ,							__UpperCAmelCase=False       ,							__UpperCAmelCase=-1       ,							__UpperCAmelCase=0       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=0       ,							__UpperCAmelCase="gelu"       ,							**__UpperCAmelCase       ,							)   ->str:
      super().__init__(**__UpperCAmelCase)

      a_						   =     hidden_size
      a_						   =     num_hidden_layers
      a_						   =     num_attention_heads
      a_						   =     intermediate_size
      a_						   =     hidden_act
      a_						   =     hidden_dropout_prob
      a_						   =     attention_probs_dropout_prob
      a_						   =     max_position_embeddings
      a_						   =     type_vocab_size
      a_						   =     initializer_range
      a_						   =     relative_attention
      a_						   =     max_relative_positions
      a_						   =     pad_token_id
      a_						   =     position_biased_input

      # Backwards compatibility
      if type(__UpperCAmelCase) == str:
         a_						   =     [x.strip() for x in pos_att_type.lower().split("|")]

      a_						   =     pos_att_type
      a_						   =     vocab_size
      a_						   =     layer_norm_eps

      a_						   =     kwargs.get("pooler_hidden_size"       ,							__UpperCAmelCase)
      a_						   =     pooler_dropout
      a_						   =     pooler_hidden_act






class 							snake_case       (     SCREAMING_SNAKE_CASE_		):







   @property
   def    UpperCAmelCase__	( self)   ->Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
         a_						   =     {0: "batch", 1: "choice", 2: "sequence"}
      else:
         a_						   =     {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    UpperCAmelCase__	( self)   ->int:
      return 12







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = -1       ,							__UpperCAmelCase = -1       ,							__UpperCAmelCase = -1       ,							__UpperCAmelCase = False       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = 3       ,							__UpperCAmelCase = 40       ,							__UpperCAmelCase = 40       ,							__UpperCAmelCase = None       ,							)   ->Mapping[str, Any]:
      a_						   =     super().generate_dummy_inputs(preprocessor=__UpperCAmelCase       ,							framework=__UpperCAmelCase)
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs

"""simple docstring"""






import math
def    UpperCamelCase						(					UpperCAmelCase     )						->bool:

   """simple docstring"""



   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5 ,   int(math.sqrt(UpperCAmelCase     ) + 1     ) ,   6     ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def    UpperCamelCase						(					UpperCAmelCase = 600_851_475_143     )						->int:

   """simple docstring"""



   try:
      a_						   =     int(UpperCAmelCase     )
   except (TypeError, ValueError):
      raise TypeError("Parameter n must be int or castable to int."     )
   if n <= 0:
      raise ValueError("Parameter n must be greater than or equal to one."     )
   a_						   =     0
   if is_prime(UpperCAmelCase     ):
      return n
   while n % 2 == 0:
      n //= 2
   if is_prime(UpperCAmelCase     ):
      return n
   for i in range(3 ,   int(math.sqrt(UpperCAmelCase     )     ) + 1 ,   2     ):
      if n % i == 0:
         if is_prime(n // i     ):
            a_						   =     n // i
            break
         elif is_prime(UpperCAmelCase     ):
            a_						   =     i
   return max_number


if __name__ == "__main__":
      print(F"""{solution() = }""")