



from __future__ import annotations

from collections.abc import MutableSequence




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  Dict, _snake_case  :  int, _snake_case  :  MutableSequence[float]					)   ->None:
            if len(_snake_case					) != degree + 1:
                   raise ValueError(
                       'The number of coefficients should be equal to the degree + 1.'					)

            snake_case__							:	list[float]   	=	list(_snake_case					)
            snake_case__							:	Any   	=	degree






     def __add__(       self  :  Dict, _snake_case  :  Polynomial					)   ->Polynomial:

            if self.degree > polynomial_a.degree:
                   snake_case__							:	Tuple   	=	self.coefficients[:]
                   for i in range(polynomial_a.degree + 1					):
                          coefficients[i] += polynomial_a.coefficients[i]
                   return Polynomial(self.degree, _snake_case					)
            else:
                   snake_case__							:	Union[str, Any]   	=	polynomial_a.coefficients[:]
                   for i in range(self.degree + 1					):
                          coefficients[i] += self.coefficients[i]
                   return Polynomial(polynomial_a.degree, _snake_case					)






     def __sub__(       self  :  Dict, _snake_case  :  Polynomial					)   ->Polynomial:
            return self + polynomial_a * Polynomial(0, [-1]					)






     def __neg__(       self  :  str					)   ->Polynomial:
            return Polynomial(self.degree, [-c for c in self.coefficients]					)






     def __mul__(       self  :  str, _snake_case  :  Polynomial					)   ->Polynomial:
            snake_case__							:	list[float]   	=	[0] * (self.degree + polynomial_a.degree + 1)
            for i in range(self.degree + 1					):
                   for j in range(polynomial_a.degree + 1					):
                          coefficients[i + j] += (
                              self.coefficients[i] * polynomial_a.coefficients[j]
                          )

            return Polynomial(self.degree + polynomial_a.degree, _snake_case					)






     def    lowercase_   (       self  :  List[Any], _snake_case  :  int | float					)   ->int | float:
            snake_case__							:	int | float   	=	0
            for i in range(self.degree + 1					):
                   result += self.coefficients[i] * (substitution**i)
            return result






     def __str__(       self  :  Any					)   ->str:
            snake_case__							:	str   	=	''
            for i in range(self.degree, -1, -1					):
                   if self.coefficients[i] == 0:
                          continue
                   elif self.coefficients[i] > 0:
                          if polynomial:
                                 polynomial += " + "
                   else:
                          polynomial += " - "

                   if i == 0:
                          polynomial += str(abs(self.coefficients[i]					)					)
                   elif i == 1:
                          polynomial += str(abs(self.coefficients[i]					)					) + "x"
                   else:
                          polynomial += str(abs(self.coefficients[i]					)					) + "x^" + str(_snake_case					)

            return polynomial






     def __repr__(       self  :  Any					)   ->str:
            return self.__str__()






     def    lowercase_   (       self  :  Dict					)   ->Polynomial:
            snake_case__							:	list[float]   	=	[0] * self.degree
            for i in range(self.degree					):
                   snake_case__							:	int   	=	self.coefficients[i + 1] * (i + 1)
            return Polynomial(self.degree - 1, _snake_case					)






     def    lowercase_   (       self  :  Any, _snake_case  :  int | float = 0					)   ->Polynomial:
            snake_case__							:	list[float]   	=	[0] * (self.degree + 2)
            snake_case__							:	List[Any]   	=	constant
            for i in range(self.degree + 1					):
                   snake_case__							:	Dict   	=	self.coefficients[i] / (i + 1)
            return Polynomial(self.degree + 1, _snake_case					)






     def __eq__(       self  :  List[Any], _snake_case  :  object					)   ->bool:
            if not isinstance(_snake_case, _snake_case					):
                   return False

            if self.degree != polynomial_a.degree:
                   return False

            for i in range(self.degree + 1					):
                   if self.coefficients[i] != polynomial_a.coefficients[i]:
                          return False

            return True






     def __ne__(       self  :  List[str], _snake_case  :  object					)   ->bool:
            return not self.__eq__(_snake_case					)











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_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "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 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""deberta-v2"""






     def __init__(       self  :  Union[str, Any], _snake_case  :  Dict=1_2_8_1_0_0, _snake_case  :  Any=1_5_3_6, _snake_case  :  Tuple=2_4, _snake_case  :  int=2_4, _snake_case  :  Optional[int]=6_1_4_4, _snake_case  :  Optional[int]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  str=5_1_2, _snake_case  :  Optional[int]=0, _snake_case  :  Optional[int]=0.0_2, _snake_case  :  Dict=1e-7, _snake_case  :  int=False, _snake_case  :  Any=-1, _snake_case  :  List[str]=0, _snake_case  :  Tuple=True, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=0, _snake_case  :  Tuple="gelu", **_snake_case  :  Union[str, Any], )   ->Optional[int]:
            super().__init__(**_snake_case					)

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

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

            snake_case__							:	List[str]   	=	pos_att_type
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	Optional[int]   	=	layer_norm_eps

            snake_case__							:	Optional[int]   	=	kwargs.get('pooler_hidden_size', _snake_case					)
            snake_case__							:	int   	=	pooler_dropout
            snake_case__							:	str   	=	pooler_hidden_act




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	List[Any]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	int   	=	{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    lowercase_   (       self  :  Dict					)   ->int:
            return 1_2






     def    lowercase_   (       self  :  Tuple, _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 = 4_0, _snake_case  :  int = 4_0, _snake_case  :  "PreTrainedTokenizerBase" = None, )   ->Mapping[str, Any]:
            snake_case__							:	Union[str, Any]   	=	super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case					)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs






