"""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


_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   {
    '''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							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Dict    =    "deberta"







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=50265 ,				_SCREAMING_SNAKE_CASE=768 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=3072 ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=512 ,				_SCREAMING_SNAKE_CASE=0 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=1E-7 ,				_SCREAMING_SNAKE_CASE=False ,				_SCREAMING_SNAKE_CASE=-1 ,				_SCREAMING_SNAKE_CASE=0 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=0 ,				_SCREAMING_SNAKE_CASE="gelu" ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      super().__init__(**_SCREAMING_SNAKE_CASE	)

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

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

      __lowerCamelCase    		=					pos_att_type
      __lowerCamelCase    		=					vocab_size
      __lowerCamelCase    		=					layer_norm_eps

      __lowerCamelCase    		=					kwargs.get('pooler_hidden_size' ,				_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					pooler_dropout
      __lowerCamelCase    		=					pooler_hidden_act






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   @property
   def    _A				(	self	):


      '''simple docstring'''

      if self.task == "multiple-choice":
         __lowerCamelCase    		=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         __lowerCamelCase    		=					{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    _A				(	self	):


      '''simple docstring'''

      return 12







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = -1 ,				_SCREAMING_SNAKE_CASE = -1 ,				_SCREAMING_SNAKE_CASE = -1 ,				_SCREAMING_SNAKE_CASE = False ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = 3 ,				_SCREAMING_SNAKE_CASE = 40 ,				_SCREAMING_SNAKE_CASE = 40 ,				_SCREAMING_SNAKE_CASE = None ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					super().generate_dummy_inputs(preprocessor=_SCREAMING_SNAKE_CASE ,				framework=_SCREAMING_SNAKE_CASE	)
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs














__lowerCamelCase						= {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase						= {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    List[Any]			=						True
							_a  :    Optional[Any]			=						[]

							for neighbour in graph[vert]:
														if not visited[neighbour]:
																					connected_verts += dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )

							return [vert, *connected_verts]






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    Optional[int]			=						len(UpperCAmelCase     )
							_a  :    Optional[Any]			=						graph_size * [False]
							_a  :    Optional[int]			=						[]

							for i in range(UpperCAmelCase     ):
														if not visited[i]:
																					_a  :    List[str]			=						dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )
																					components_list.append(UpperCAmelCase     )

							return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()