'''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


lowerCAmelCase_			:			str    	=   logging.get_logger(__name__)

lowerCAmelCase_			:			Union[str, 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 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            ='deberta'


   def __init__( self					:						List[Any] ,		__lowerCAmelCase					:						str=5_0265 ,		__lowerCAmelCase					:						Dict=768 ,		__lowerCAmelCase					:						List[str]=12 ,		__lowerCAmelCase					:						str=12 ,		__lowerCAmelCase					:						Union[str, Any]=3072 ,		__lowerCAmelCase					:						List[str]="gelu" ,		__lowerCAmelCase					:						Optional[int]=0.1 ,		__lowerCAmelCase					:						Optional[Any]=0.1 ,		__lowerCAmelCase					:						int=512 ,		__lowerCAmelCase					:						Optional[int]=0 ,		__lowerCAmelCase					:						Any=0.02 ,		__lowerCAmelCase					:						List[str]=1e-7 ,		__lowerCAmelCase					:						str=False ,		__lowerCAmelCase					:						Dict=-1 ,		__lowerCAmelCase					:						Union[str, Any]=0 ,		__lowerCAmelCase					:						str=True ,		__lowerCAmelCase					:						Any=None ,		__lowerCAmelCase					:						Any=0 ,		__lowerCAmelCase					:						Dict="gelu" ,		**__lowerCAmelCase					:						Dict ,		)   ->       Tuple:
         super().__init__(**__lowerCAmelCase)

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

         # Backwards compatibility
         if type(__lowerCAmelCase) == str:
               lowercase_     			=   [x.strip() for x in pos_att_type.lower().split("|")]

         lowercase_     			=   pos_att_type
         lowercase_     			=   vocab_size
         lowercase_     			=   layer_norm_eps

         lowercase_     			=   kwargs.get("pooler_hidden_size" ,		__lowerCAmelCase)
         lowercase_     			=   pooler_dropout
         lowercase_     			=   pooler_hidden_act





class 				lowercase    (    __lowerCamelCase			):


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


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,		__lowerCAmelCase					:						int = -1 ,		__lowerCAmelCase					:						int = -1 ,		__lowerCAmelCase					:						int = -1 ,		__lowerCAmelCase					:						bool = False ,		__lowerCAmelCase					:						Optional["TensorType"] = None ,		__lowerCAmelCase					:						int = 3 ,		__lowerCAmelCase					:						int = 40 ,		__lowerCAmelCase					:						int = 40 ,		__lowerCAmelCase					:						"PreTrainedTokenizerBase" = None ,		)   ->       Mapping[str, Any]:
         lowercase_     			=   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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_			:			Tuple    	=   {"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Optional[int]    	=   ["ViltFeatureExtractor"]
       lowerCAmelCase_			:			str    	=   ["ViltImageProcessor"]
       lowerCAmelCase_			:			Any    	=   ["ViltProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Any    	=   [
           "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ViltForImageAndTextRetrieval",
           "ViltForImagesAndTextClassification",
           "ViltForTokenClassification",
           "ViltForMaskedLM",
           "ViltForQuestionAnswering",
           "ViltLayer",
           "ViltModel",
           "ViltPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vilt import ViltFeatureExtractor
              from .image_processing_vilt import ViltImageProcessor
              from .processing_vilt import ViltProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vilt import (
                  VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViltForImageAndTextRetrieval,
                  ViltForImagesAndTextClassification,
                  ViltForMaskedLM,
                  ViltForQuestionAnswering,
                  ViltForTokenClassification,
                  ViltLayer,
                  ViltModel,
                  ViltPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_			:			str    	=   _LazyModule(__name__, globals()["__file__"], _import_structure)






