
"""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


_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        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'deberta-v2'



		def __init__(   self							,						__a=12_81_00							,						__a=15_36							,						__a=24							,						__a=24							,						__a=61_44							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=5_12							,						__a=0							,						__a=0.02							,						__a=1e-7							,						__a=False							,						__a=-1							,						__a=0							,						__a=True							,						__a=None							,						__a=0							,						__a="gelu"							,						**__a							,						)			->			Optional[int]:






							'''simple docstring'''
							super().__init__(**__a)

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

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

							_UpperCamelCase									=    pos_att_type
							_UpperCamelCase									=    vocab_size
							_UpperCamelCase									=    layer_norm_eps

							_UpperCamelCase									=    kwargs.get('''pooler_hidden_size'''							,						__a)
							_UpperCamelCase									=    pooler_dropout
							_UpperCamelCase									=    pooler_hidden_act






class        _UpperCAmelCase(       lowerCamelCase      ):



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






							'''simple docstring'''
							if self.task == "multiple-choice":
												_UpperCamelCase									=    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												_UpperCamelCase									=    {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 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return 12




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






							'''simple docstring'''
							_UpperCamelCase									=    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








"""simple docstring"""





from collections.abc import Callable

import numpy as np



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						np.array:


					"""simple docstring"""





					_UpperCamelCase									=    int(np.ceil((x_end - xa) / step_size      )      )
					_UpperCamelCase									=    np.zeros((n + 1,)      )
					_UpperCamelCase									=    ya
					_UpperCamelCase									=    xa

					for k in range(__snake_case      ):
										_UpperCamelCase									=    y[k] + step_size * ode_func(__snake_case,	y[k]      )
										_UpperCamelCase									=    y[k] + (
										    (step_size / 2) * (ode_func(__snake_case,	y[k]      ) + ode_func(x + step_size,	__snake_case      ))
										)
										x += step_size

					return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()






