
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


_A							=       logging.get_logger(__name__)

_A							=       {
    """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					):
 A__    :					Optional[Any]    =     """deberta"""

 def __init__(   self  ,						__UpperCamelCase=5_0_2_6_5  ,						__UpperCamelCase=7_6_8  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=3_0_7_2  ,						__UpperCamelCase="gelu"  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=5_1_2  ,						__UpperCamelCase=0  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=1e-7  ,						__UpperCamelCase=False  ,						__UpperCamelCase=-1  ,						__UpperCamelCase=0  ,						__UpperCamelCase=True  ,						__UpperCamelCase=None  ,						__UpperCamelCase=0  ,						__UpperCamelCase="gelu"  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        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 		lowercase_							(							_lowerCamelCase					):

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        return 1_2

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = -1  ,						__UpperCamelCase = -1  ,						__UpperCamelCase = -1  ,						__UpperCamelCase = False  ,						__UpperCamelCase = None  ,						__UpperCamelCase = 3  ,						__UpperCamelCase = 4_0  ,						__UpperCamelCase = 4_0  ,						__UpperCamelCase = None  ,						):




        """simple docstring"""


        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








'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

        return np.mean((labels - prediction) ** 2				)







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               print("""Error: X and y have different lengths"""				)
               return
        if y.ndim != 1:
               print("""Error: Data set labels must be one dimensional"""				)
               return

        if len(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

        _UpperCAmelCase    :		Union[str, Any]     	=					0
        _UpperCAmelCase    :		Optional[Any]     	=					self.mean_squared_error(a_    ,np.mean(a_				)				) * 2
        for i in range(len(a_				)				):
               if len(x[:i]				) < self.min_leaf_size:
                      continue
               elif len(x[i:]				) < self.min_leaf_size:
                      continue
               else:
                      _UpperCAmelCase    :		Dict     	=					self.mean_squared_error(x[:i]    ,np.mean(y[:i]				)				)
                      _UpperCAmelCase    :		Any     	=					self.mean_squared_error(x[i:]    ,np.mean(y[i:]				)				)
                      _UpperCAmelCase    :		Optional[Any]     	=					error_left + error_right
                      if error < min_error:
                             _UpperCAmelCase    :		Union[str, Any]     	=					i
                             _UpperCAmelCase    :		Optional[int]     	=					error

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

               _UpperCAmelCase    :		List[str]     	=					x[best_split]
               _UpperCAmelCase    :		List[Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               _UpperCAmelCase    :		Union[str, Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               self.left.train(a_    ,a_				)
               self.right.train(a_    ,a_				)
        else:
               _UpperCAmelCase    :		Any     	=					np.mean(a_				)

        return







 def _snake_case					(				self    ,a_				)						->  List[Any]:
        if self.prediction is not None:
               return self.prediction
        elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                      return self.right.predict(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					np.array([tree.predict(lowerCAmelCase_						) for x in test_cases]						)
       _UpperCAmelCase    :		Optional[Any]     	=					np.mean((predictions - test_cases) ** 2						)

       print("""Test values: """ + str(lowerCAmelCase_						)						)
       print("""Predictions: """ + str(lowerCAmelCase_						)						)
       print("""Average error: """ + str(lowerCAmelCase_						)						)


if __name__ == "__main__":
   main()
   import doctest

   doctest.testmod(name="""mean_squarred_error""", verbose=True)



