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


A__           =							logging.get_logger(__name__)

A__           =							{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
       _UpperCAmelCase						    =     'deberta-v2'







       def __init__(				self     :    Optional[Any]	,					__snake_case     :    Union[str, Any]=128100	,					__snake_case     :    Optional[int]=1536	,					__snake_case     :    Dict=24	,					__snake_case     :    int=24	,					__snake_case     :    Tuple=6144	,					__snake_case     :    Union[str, Any]="gelu"	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    Dict=0.1	,					__snake_case     :    int=512	,					__snake_case     :    int=0	,					__snake_case     :    int=0.0_2	,					__snake_case     :    int=1e-7	,					__snake_case     :    List[str]=False	,					__snake_case     :    Union[str, Any]=-1	,					__snake_case     :    List[Any]=0	,					__snake_case     :    Optional[Any]=True	,					__snake_case     :    Tuple=None	,					__snake_case     :    Any=0	,					__snake_case     :    int="gelu"	,					**__snake_case     :    Any	,					):
          super().__init__(**_a		)

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

          # Backwards compatibility
          if type(_a		) == str:
             lowerCamelCase						:str						  =     [x.strip() for x in pos_att_type.lower().split('''|'''		)]

          lowerCamelCase						:Any						  =     pos_att_type
          lowerCamelCase						:Optional[int]						  =     vocab_size
          lowerCamelCase						:Tuple						  =     layer_norm_eps

          lowerCamelCase						:Any						  =     kwargs.get('''pooler_hidden_size'''	,					_a		)
          lowerCamelCase						:Union[str, Any]						  =     pooler_dropout
          lowerCamelCase						:List[Any]						  =     pooler_hidden_act


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







       @property
       def 				snake_case  (				self     :    Any		):




          if self.task == "multiple-choice":
             lowerCamelCase						:Any						  =     {0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
             lowerCamelCase						:Any						  =     {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 				snake_case  (				self     :    Optional[int]		):
          return 12







       def 				snake_case  (				self     :    int	,					__snake_case     :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,					__snake_case     :    int = -1	,					__snake_case     :    int = -1	,					__snake_case     :    int = -1	,					__snake_case     :    bool = False	,					__snake_case     :    Optional["TensorType"] = None	,					__snake_case     :    int = 3	,					__snake_case     :    int = 40	,					__snake_case     :    int = 40	,					__snake_case     :    "PreTrainedTokenizerBase" = None	,					):
          lowerCamelCase						:Any						  =     super().generate_dummy_inputs(preprocessor=_a	,					framework=_a		)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
          return dummy_inputs




import math





def  _lowerCamelCase					(   a_				: int):

			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(a_) + 1)						,			6):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True





def  _lowerCamelCase					(   a_				: float = 0.1):
			lowerCamelCase						:Dict						  =     3
			lowerCamelCase						:List[Any]						  =     3

			while primes / (2 * j - 1) >= ratio:
						for i in range(j * j + j + 1						,			(j + 2) * (j + 2)						,			j + 1):
									primes += is_prime(a_)
						j += 2
			return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()



