


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


lowerCAmelCase       =							logging.get_logger(__name__)

lowerCAmelCase       =							{
    '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        _a							(			UpperCamelCase__			):
							_lowercase	:			int        = '''deberta-v2'''

							def __init__(							self:	str    ,						UpperCamelCase_:	Any=128_100    ,						UpperCamelCase_:	Dict=1_536    ,						UpperCamelCase_:	Union[str, Any]=24    ,						UpperCamelCase_:	Dict=24    ,						UpperCamelCase_:	Any=6_144    ,						UpperCamelCase_:	Optional[Any]="gelu"    ,						UpperCamelCase_:	int=0.1    ,						UpperCamelCase_:	Union[str, Any]=0.1    ,						UpperCamelCase_:	int=512    ,						UpperCamelCase_:	Optional[int]=0    ,						UpperCamelCase_:	Optional[int]=0.02    ,						UpperCamelCase_:	Optional[int]=1E-7    ,						UpperCamelCase_:	List[str]=False    ,						UpperCamelCase_:	Dict=-1    ,						UpperCamelCase_:	str=0    ,						UpperCamelCase_:	Tuple=True    ,						UpperCamelCase_:	Optional[Any]=None    ,						UpperCamelCase_:	Dict=0    ,						UpperCamelCase_:	Tuple="gelu"    ,						**UpperCamelCase_:	Tuple    ,						)			->   Tuple:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)

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

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

									lowercase__            =      pos_att_type
									lowercase__            =      vocab_size
									lowercase__            =      layer_norm_eps

									lowercase__            =      kwargs.get('''pooler_hidden_size'''    ,						UpperCamelCase_							)
									lowercase__            =      pooler_dropout
									lowercase__            =      pooler_hidden_act


class        _a							(			UpperCamelCase__			):

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





									"""simple docstring"""







									if self.task == "multiple-choice":
											lowercase__            =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowercase__            =      {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:	Union[str, Any]							)			->   int:





									"""simple docstring"""







									return 12


							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,						UpperCamelCase_:	int = -1    ,						UpperCamelCase_:	int = -1    ,						UpperCamelCase_:	int = -1    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	Optional["TensorType"] = None    ,						UpperCamelCase_:	int = 3    ,						UpperCamelCase_:	int = 40    ,						UpperCamelCase_:	int = 40    ,						UpperCamelCase_:	"PreTrainedTokenizerBase" = None    ,						)			->   Mapping[str, Any]:





									"""simple docstring"""







									lowercase__            =      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 __future__ import annotations

from collections.abc import Iterator


class        _a							:

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	int							)			->   None:





									"""simple docstring"""







									lowercase__            =      value
									lowercase__            =      None
									lowercase__            =      None


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Node							)			->   None:





									"""simple docstring"""







									lowercase__            =      tree

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Node | None							)			->   int:





									"""simple docstring"""







									if node is None:
											return 0
									return node.value + (
									    self.depth_first_search(node.left							) + self.depth_first_search(node.right							)
									)


							def __iter__(							self:	List[str]							)			->   Iterator[int]:





									"""simple docstring"""







									yield self.depth_first_search(self.tree							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


