


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     =       logging.get_logger(__name__)

UpperCAmelCase     =       {
    """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'''

 __snake_case				=    """deberta"""



 def __init__(      self						, _UpperCAmelCase=5_02_65						, _UpperCAmelCase=7_68						, _UpperCAmelCase=12						, _UpperCAmelCase=12						, _UpperCAmelCase=30_72						, _UpperCAmelCase="gelu"						, _UpperCAmelCase=0.1						, _UpperCAmelCase=0.1						, _UpperCAmelCase=5_12						, _UpperCAmelCase=0						, _UpperCAmelCase=0.02						, _UpperCAmelCase=1E-7						, _UpperCAmelCase=False						, _UpperCAmelCase=-1						, _UpperCAmelCase=0						, _UpperCAmelCase=True						, _UpperCAmelCase=None						, _UpperCAmelCase=0						, _UpperCAmelCase="gelu"						, **_UpperCAmelCase						, ):
       super().__init__(**__UpperCAmelCase   )

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

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

       snake_case_			= pos_att_type
       snake_case_			= vocab_size
       snake_case_			= layer_norm_eps

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







class lowerCAmelCase_		(					_a			):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__	(      self   ):
       if self.task == "multiple-choice":
             snake_case_			= {0: "batch", 1: "choice", 2: "sequence"}
       else:
             snake_case_			= {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   ):
       return 12





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, _UpperCAmelCase = 3						, _UpperCAmelCase = 40						, _UpperCAmelCase = 40						, _UpperCAmelCase = None						, ):
       snake_case_			= 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



"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__lowercase          = logging.get_logger(__name__)






class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					*__UpperCAmelCase							:       Dict							,					**__UpperCAmelCase							:       Any):
      warnings.warn(
          "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use LevitImageProcessor instead."							,					__UpperCAmelCase							,					)
      super().__init__(*__UpperCAmelCase							,					**__UpperCAmelCase)






