
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 :			Union[str, Any]              =	logging.get_logger(__name__)

_UpperCAmelCase :			List[str]              =	{
    "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     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Any     					=    "deberta-v2"



		def __init__(			self    ,				A_=128_100    ,				A_=1_536    ,				A_=24    ,				A_=24    ,				A_=6_144    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=512    ,				A_=0    ,				A_=0.02    ,				A_=1e-7    ,				A_=False    ,				A_=-1    ,				A_=0    ,				A_=True    ,				A_=None    ,				A_=0    ,				A_="gelu"    ,				**A_    ,				)  -> Tuple:
				"""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     lowercase (		_SCREAMING_SNAKE_CASE       ):



		@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


import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('swish'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('silu'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('mish'	)

				self.assertIsInstance(A_    ,				nn.Mish	)

				self.assertEqual(act(torch.tensor(-200    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('gelu'	)

				self.assertIsInstance(A_    ,				nn.GELU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)
