




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


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{
    '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    ):
					_lowerCamelCase						    =					"""deberta"""

					def __init__(							self					,			__lowercase=50_265					,			__lowercase=768					,			__lowercase=12					,			__lowercase=12					,			__lowercase=3_072					,			__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   )

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

						# Backwards compatibility
						if type(__lowercase   ) == str:
							lowerCamelCase_				:						Optional[Any]				  =				[x.strip() for x in pos_att_type.lower().split('|'   )]

						lowerCamelCase_				:						List[str]				  =				pos_att_type
						lowerCamelCase_				:						int				  =				vocab_size
						lowerCamelCase_				:						Optional[Any]				  =				layer_norm_eps

						lowerCamelCase_				:						str				  =				kwargs.get('pooler_hidden_size'					,			__lowercase   )
						lowerCamelCase_				:						Optional[Any]				  =				pooler_dropout
						lowerCamelCase_				:						Optional[int]				  =				pooler_hidden_act


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					@property
					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self.task == "multiple-choice":
							lowerCamelCase_				:						Tuple				  =				{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
							lowerCamelCase_				:						Dict				  =				{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   ):







						'''simple docstring'''




						return 12





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = -1					,			__lowercase = -1					,			__lowercase = -1					,			__lowercase = False					,			__lowercase = None					,			__lowercase = 3					,			__lowercase = 40					,			__lowercase = 40					,			__lowercase = None					,			):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				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




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

