




"""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_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    '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 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''deberta-v2'''







   def __init__(		self       ,					lowerCamelCase_=1_2_8_1_0_0       ,					lowerCamelCase_=1_5_3_6       ,					lowerCamelCase_=2_4       ,					lowerCamelCase_=2_4       ,					lowerCamelCase_=6_1_4_4       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=5_1_2       ,					lowerCamelCase_=0       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=1e-7       ,					lowerCamelCase_=False       ,					lowerCamelCase_=-1       ,					lowerCamelCase_=0       ,					lowerCamelCase_=True       ,					lowerCamelCase_=None       ,					lowerCamelCase_=0       ,					lowerCamelCase_="gelu"       ,					**lowerCamelCase_       ,					)				->   Optional[int]:
       super().__init__(**lowerCamelCase_)

       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(lowerCamelCase_) == 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'''       ,					lowerCamelCase_)
       UpperCamelCase              =	pooler_dropout
       UpperCamelCase              =	pooler_hidden_act

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       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:
       return 1_2







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = -1       ,					lowerCamelCase_ = -1       ,					lowerCamelCase_ = -1       ,					lowerCamelCase_ = False       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = 3       ,					lowerCamelCase_ = 4_0       ,					lowerCamelCase_ = 4_0       ,					lowerCamelCase_ = None       ,					)				->   Mapping[str, Any]:
       UpperCamelCase              =	super().generate_dummy_inputs(preprocessor=lowerCamelCase_       ,					framework=lowerCamelCase_)
       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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""dpr"""



					def __init__(	self     :						Optional[Any]      ,       _lowercase     :						Dict=30_522      ,       _lowercase     :						Tuple=768      ,       _lowercase     :						int=12      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Union[str, Any]=3_072      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Dict=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						Union[str, Any]=512      ,       _lowercase     :						Optional[int]=2      ,       _lowercase     :						Union[str, Any]=0.02      ,       _lowercase     :						List[Any]=1E-12      ,       _lowercase     :						List[str]=0      ,       _lowercase     :						Dict="absolute"      ,       _lowercase     :						int = 0      ,       **_lowercase     :						int      ,       )    ->							int:
								super().__init__(pad_token_id=_lowercase      ,       **_lowercase)

								A_									= vocab_size
								A_									= hidden_size
								A_									= num_hidden_layers
								A_									= num_attention_heads
								A_									= hidden_act
								A_									= intermediate_size
								A_									= hidden_dropout_prob
								A_									= attention_probs_dropout_prob
								A_									= max_position_embeddings
								A_									= type_vocab_size
								A_									= initializer_range
								A_									= layer_norm_eps
								A_									= projection_dim
								A_									= position_embedding_type




