





"""simple docstring"""






import warnings


warnings.warn(
    '''memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '''
    '''`from accelerate import find_executable_batch_size` to avoid this warning.''',
    FutureWarning,
)













"""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_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    '''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 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """deberta-v2"""






       def __init__(   self    ,  a__=12_81_00    ,  a__=15_36    ,  a__=24    ,  a__=24    ,  a__=61_44    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=5_12    ,  a__=0    ,  a__=0.02    ,  a__=1E-7    ,  a__=False    ,  a__=-1    ,  a__=0    ,  a__=True    ,  a__=None    ,  a__=0    ,  a__="gelu"    ,  **a__    ,  ):
              super().__init__(**a__     )

              _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(a__     ) == 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'    ,  a__     )
              _UpperCAmelCase     =     pooler_dropout
              _UpperCAmelCase     =     pooler_hidden_act



class 	lowerCAmelCase					(      snake_case							):






       @property
       def 							__A (   self     ):
              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 							__A (   self     ):
              return 12






       def 							__A (   self    ,  a__    ,  a__ = -1    ,  a__ = -1    ,  a__ = -1    ,  a__ = False    ,  a__ = None    ,  a__ = 3    ,  a__ = 40    ,  a__ = 40    ,  a__ = None    ,  ):
              _UpperCAmelCase     =     super().generate_dummy_inputs(preprocessor=a__    ,  framework=a__     )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs






