

'''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


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    '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__			(				_UpperCAmelCase      ):
    _A			    =							"deberta"




    def __init__(					self				,     lowercase__=5_0265				,     lowercase__=768				,     lowercase__=12				,     lowercase__=12				,     lowercase__=3072				,     lowercase__="gelu"				,     lowercase__=0.1				,     lowercase__=0.1				,     lowercase__=512				,     lowercase__=0				,     lowercase__=0.02				,     lowercase__=1e-7				,     lowercase__=False				,     lowercase__=-1				,     lowercase__=0				,     lowercase__=True				,     lowercase__=None				,     lowercase__=0				,     lowercase__="gelu"				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

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

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

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       pos_att_type
          SCREAMING_SNAKE_CASE_      :			Any     =       vocab_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       layer_norm_eps

          SCREAMING_SNAKE_CASE_      :			str     =       kwargs.get("pooler_hidden_size"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       pooler_dropout
          SCREAMING_SNAKE_CASE_      :			str     =       pooler_hidden_act






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.task == "multiple-choice":
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       {0: "batch", 1: "choice", 2: "sequence"}
          else:
                SCREAMING_SNAKE_CASE_      :			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   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 12







    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = -1				,     lowercase__ = -1				,     lowercase__ = -1				,     lowercase__ = False				,     lowercase__ = None				,     lowercase__ = 3				,     lowercase__ = 40				,     lowercase__ = 40				,     lowercase__ = None				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       super().generate_dummy_inputs(preprocessor=lowercase__				,     framework=lowercase__			)
          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'''






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime




class 		a__				(							a__  ):





							'''simple docstring'''






							def __init__(		self					,    *lowerCamelCase_					,    **lowerCamelCase_   )       ->							Optional[int]:
														super().__init__(*lowerCamelCase_					,    **lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[int]:
														lowerCAmelCase__          =	(
														    next_prime(value % self.size_table   )
														    if not is_prime(value % self.size_table   )
														    else value % self.size_table
														)  # gt = bigger than
														return next_prime_gt - (data % next_prime_gt)







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[int]:
														return (increment * self.__hash_function_a(lowerCamelCase_					,    lowerCamelCase_   )) % self.size_table







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_=None   )       ->							Any:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	self.hash_function(lowerCamelCase_   )

														while self.values[new_key] is not None and self.values[new_key] != key:
																					lowerCAmelCase__          =	(
																					    self.__hash_double_function(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )
																					    if self.balanced_factor() >= self.lim_charge
																					    else None
																					)
																					if new_key is None:
																												break
																					else:
																												i += 1

														return new_key