






"""simple docstring"""


import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput


@dataclass
class    snake_case						(				a__		):


   '''simple docstring'''

   A_		:      float
   A_		:      Optional[float]							=					None
   A_		:      Optional[float]							=					None





class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					ModelOutputTest(a=30    )
     self.assertEqual(x.a,			30    )
     self.assertIsNone(x.b    )
     self.assertIsNone(x.c    )
     with self.assertRaises(__UpperCAmelCase    ):
       __A		     =					x.d


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					ModelOutputTest(a=30,			b=10    )
     self.assertEqual(x[0],			30    )
     self.assertEqual(x[1],			10    )
     self.assertEqual(x[:2],			(30, 10)    )
     self.assertEqual(x[:],			(30, 10)    )

     __A		     =					ModelOutputTest(a=30,			c=10    )
     self.assertEqual(x[0],			30    )
     self.assertEqual(x[1],			10    )
     self.assertEqual(x[:2],			(30, 10)    )
     self.assertEqual(x[:],			(30, 10)    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					ModelOutputTest(a=30,			b=10    )
     self.assertEqual(x['''a'''],			30    )
     self.assertEqual(x['''b'''],			10    )
     with self.assertRaises(__UpperCAmelCase    ):
       __A		     =					x['''c''']

     __A		     =					ModelOutputTest(a=30,			c=10    )
     self.assertEqual(x['''a'''],			30    )
     self.assertEqual(x['''c'''],			10    )
     with self.assertRaises(__UpperCAmelCase    ):
       __A		     =					x['''b''']


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					ModelOutputTest(a=30    )
     self.assertEqual(list(x.keys()    ),			['''a''']    )
     self.assertEqual(list(x.values()    ),			[30]    )
     self.assertEqual(list(x.items()    ),			[('''a''', 30)]    )
     self.assertEqual(list(__UpperCAmelCase    ),			['''a''']    )

     __A		     =					ModelOutputTest(a=30,			b=10    )
     self.assertEqual(list(x.keys()    ),			['''a''', '''b''']    )
     self.assertEqual(list(x.values()    ),			[30, 10]    )
     self.assertEqual(list(x.items()    ),			[('''a''', 30), ('''b''', 10)]    )
     self.assertEqual(list(__UpperCAmelCase    ),			['''a''', '''b''']    )

     __A		     =					ModelOutputTest(a=30,			c=10    )
     self.assertEqual(list(x.keys()    ),			['''a''', '''c''']    )
     self.assertEqual(list(x.values()    ),			[30, 10]    )
     self.assertEqual(list(x.items()    ),			[('''a''', 30), ('''c''', 10)]    )
     self.assertEqual(list(__UpperCAmelCase    ),			['''a''', '''c''']    )

     with self.assertRaises(__UpperCAmelCase    ):
       __A		     =					x.update({'''d''': 20}    )
     with self.assertRaises(__UpperCAmelCase    ):
       del x["a"]
     with self.assertRaises(__UpperCAmelCase    ):
       __A		     =					x.pop('''a'''    )
     with self.assertRaises(__UpperCAmelCase    ):
       __A		     =					x.setdefault('''d''',			32    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					ModelOutputTest(a=30    )
     __A		     =					10
     self.assertEqual(x.a,			10    )
     self.assertEqual(x['''a'''],			10    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					ModelOutputTest(a=30    )
     __A		     =					10
     self.assertEqual(x.a,			10    )
     self.assertEqual(x['''a'''],			10    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					ModelOutputTest({'''a''': 30, '''b''': 10}    )
     self.assertEqual(list(x.keys()    ),			['''a''', '''b''']    )
     self.assertEqual(x.a,			30    )
     self.assertEqual(x.b,			10    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					ModelOutputTest([('''a''', 30), ('''b''', 10)]    )
     self.assertEqual(list(x.keys()    ),			['''a''', '''b''']    )
     self.assertEqual(x.a,			30    )
     self.assertEqual(x.b,			10    )

     with self.assertRaises(__UpperCAmelCase    ):
       __A		     =					ModelOutputTest([('''a''', 30), (10, 10)]    )

     __A		     =					ModelOutputTest(a=(30, 30)    )
     self.assertEqual(list(x.keys()    ),			['''a''']    )
     self.assertEqual(x.a,			(30, 30)    )








"""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


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''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      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						List[Any]     =		'deberta'


     def __init__(   self     ,					__UpperCAmelCase=50265     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=1e-7     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase="gelu"     ,					**__UpperCAmelCase     ,					)							->				Any:
          super().__init__(**__UpperCAmelCase				)

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

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

          _a     						=					pos_att_type
          _a     						=					vocab_size
          _a     						=					layer_norm_eps

          _a     						=					kwargs.get('''pooler_hidden_size'''     ,					__UpperCAmelCase				)
          _a     						=					pooler_dropout
          _a     						=					pooler_hidden_act







class      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a     						=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               _a     						=					{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:
          return 12





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = None     ,					)							->				Mapping[str, Any]:
          _a     						=					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