
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


lowercase     :       Tuple        =       logging.get_logger(__name__)

lowercase     :       List[Any]        =       {
    "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__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				Optional[int]											=							'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 , )							->	List[Any]:




									'''simple docstring'''


									super().__init__(**__UpperCamelCase    )

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

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

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

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







class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						@property
						def __lowerCamelCase					(				self    )							->	Mapping[str, Mapping[int, str]]:




									'''simple docstring'''


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




									'''simple docstring'''


									return 12




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = -1 , __UpperCamelCase = -1 , __UpperCamelCase = -1 , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = 3 , __UpperCamelCase = 40 , __UpperCamelCase = 40 , __UpperCamelCase = None , )							->	Mapping[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      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


from collections.abc import Callable

lowercase     :       Optional[Any]        =       Callable[[float], float]  # type alias for a real -> real function







def  UpperCAmelCase_   (_lowerCAmelCase :		RealFunc     ,			_lowerCAmelCase :		RealFunc     ,			_lowerCAmelCase :		int     ,			):
			__UpperCamelCase    :    Any								=      float(_lowerCAmelCase		)
			while True:
						try:
									__UpperCamelCase    :    List[Any]								=      prev_guess - function(_lowerCAmelCase		) / derivative(_lowerCAmelCase		)
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"		) from None
						if abs(prev_guess - next_guess		) < 10**-5:
									return next_guess
						__UpperCamelCase    :    Tuple								=      next_guess







def  UpperCAmelCase_   (_lowerCAmelCase :		float		):
			return (x**3) - (2 * x) - 5







def  UpperCAmelCase_   (_lowerCAmelCase :		float		):
			return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))