




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__       :      Union[str, Any]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      List[Any]  =   {
    '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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Any	     =      """deberta-v2"""







    def __init__(    self     ,  UpperCamelCase__=12_8100     ,  UpperCamelCase__=1536     ,  UpperCamelCase__=24     ,  UpperCamelCase__=24     ,  UpperCamelCase__=6144     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=512     ,  UpperCamelCase__=0     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=1e-7     ,  UpperCamelCase__=False     ,  UpperCamelCase__=-1     ,  UpperCamelCase__=0     ,  UpperCamelCase__=True     ,  UpperCamelCase__=None     ,  UpperCamelCase__=0     ,  UpperCamelCase__="gelu"     ,  **UpperCamelCase__     ,  )     ->      Optional[int]:
           super().__init__(**UpperCamelCase__     )

           lowerCamelCase   :      List[str]			 =					hidden_size
           lowerCamelCase   :      Optional[int]			 =					num_hidden_layers
           lowerCamelCase   :      Optional[Any]			 =					num_attention_heads
           lowerCamelCase   :      Union[str, Any]			 =					intermediate_size
           lowerCamelCase   :      Any			 =					hidden_act
           lowerCamelCase   :      Dict			 =					hidden_dropout_prob
           lowerCamelCase   :      Dict			 =					attention_probs_dropout_prob
           lowerCamelCase   :      Tuple			 =					max_position_embeddings
           lowerCamelCase   :      Optional[Any]			 =					type_vocab_size
           lowerCamelCase   :      List[str]			 =					initializer_range
           lowerCamelCase   :      Tuple			 =					relative_attention
           lowerCamelCase   :      List[str]			 =					max_relative_positions
           lowerCamelCase   :      Optional[int]			 =					pad_token_id
           lowerCamelCase   :      int			 =					position_biased_input

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

           lowerCamelCase   :      List[Any]			 =					pos_att_type
           lowerCamelCase   :      Dict			 =					vocab_size
           lowerCamelCase   :      Dict			 =					layer_norm_eps

           lowerCamelCase   :      Any			 =					kwargs.get("pooler_hidden_size"     ,  UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					pooler_dropout
           lowerCamelCase   :      int			 =					pooler_hidden_act




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = 3     ,  UpperCamelCase__ = 40     ,  UpperCamelCase__ = 40     ,  UpperCamelCase__ = None     ,  )     ->      Mapping[str, Any]:
           lowerCamelCase   :      Union[str, Any]			 =					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












def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Any:
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       lowerCamelCase   :      str			 =					(boundary[1] - boundary[0]) / steps
       lowerCamelCase   :      List[str]			 =					boundary[0]
       lowerCamelCase   :      Union[str, Any]			 =					boundary[1]
       lowerCamelCase   :      int			 =					make_points(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					0.0
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       for i in x_i:
              # print(i)
              y += h * f(_SCREAMING_SNAKE_CASE    )
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       return y

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       lowerCamelCase   :      int			 =					a + h
       while x < (b - h):
              yield x
              lowerCamelCase   :      List[str]			 =					x + h

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:  # enter your function here
       lowerCamelCase   :      str			 =					(x - 0) * (x - 0)
       return y

def      A      (      )	-> int:
       lowerCamelCase   :      int			 =					0.0  # Lower bound of integration
       lowerCamelCase   :      int			 =					1.0  # Upper bound of integration
       lowerCamelCase   :      Dict			 =					10.0  # define number of steps or resolution
       lowerCamelCase   :      int			 =					[a, b]  # define boundary of integration
       lowerCamelCase   :      str			 =					method_a(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       print(f'''y = {y}'''    )


if __name__ == "__main__":
       main()






