



'''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


A_				:					List[Any]							=      logging.get_logger(__name__)

A_				:					Optional[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							):


 """simple docstring"""


 UpperCAmelCase		      =    """deberta"""







 def __init__(				self    ,a_=50_265    ,a_=768    ,a_=12    ,a_=12    ,a_=3_072    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=512    ,a_=0    ,a_=0.02    ,a_=1E-7    ,a_=False    ,a_=-1    ,a_=0    ,a_=True    ,a_=None    ,a_=0    ,a_="gelu"    ,**a_    ,)						->  Optional[Any]:
        super().__init__(**a_				)

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

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

        _UpperCAmelCase    :		Optional[Any]     	=					pos_att_type
        _UpperCAmelCase    :		str     	=					vocab_size
        _UpperCAmelCase    :		List[Any]     	=					layer_norm_eps

        _UpperCAmelCase    :		Dict     	=					kwargs.get("""pooler_hidden_size"""    ,a_				)
        _UpperCAmelCase    :		List[str]     	=					pooler_dropout
        _UpperCAmelCase    :		Any     	=					pooler_hidden_act







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


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







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,a_ = 3    ,a_ = 40    ,a_ = 40    ,a_ = None    ,)						->  Mapping[str, Any]:
        _UpperCAmelCase    :		Dict     	=					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 qiskit
from qiskit.providers import Backend




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						tuple[qiskit.QuantumCircuit, str, str]:





       '''simple docstring'''


       _UpperCAmelCase	,_UpperCAmelCase    :		Optional[Any]     	=					bin(lowerCAmelCase_						)[2:], bin(lowerCAmelCase_						)[2:]  # Remove leading '0b'

       # Ensure that both strings are of the same length
       if len(lowerCAmelCase_						) > len(lowerCAmelCase_						):
              _UpperCAmelCase    :		str     	=					y.zfill(len(lowerCAmelCase_						)						)
       else:
              _UpperCAmelCase    :		int     	=					x.zfill(len(lowerCAmelCase_						)						)

       # We need (3 * number of bits in the larger number)+1 qBits
       # The second parameter is the number of classical registers, to measure the result
       _UpperCAmelCase    :		Any     	=					qiskit.QuantumCircuit((len(lowerCAmelCase_						) * 3) + 1			,  len(lowerCAmelCase_						) + 1						)

       # We are essentially "not-ing" the bits that are 1
       # Reversed because it's easier to perform ops on more significant bits
       for i in range(len(lowerCAmelCase_						)						):
              if x[::-1][i] == "1":
                     circuit.x(lowerCAmelCase_						)
       for j in range(len(lowerCAmelCase_						)						):
              if y[::-1][j] == "1":
                     circuit.x(len(lowerCAmelCase_						) + j						)

       return circuit, x, y




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )->						Dict:





       '''simple docstring'''


       circuit.ccx(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
       circuit.cx(lowerCAmelCase_			,  lowerCAmelCase_						)
       circuit.ccx(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
       circuit.cx(lowerCAmelCase_			,  lowerCAmelCase_						)
       circuit.cx(lowerCAmelCase_			,  lowerCAmelCase_						)




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = qiskit.Aer.get_backend("""aer_simulator"""						)			,  )->						int:





       '''simple docstring'''


       if vala < 0 or vala < 0:
              raise ValueError("""Both Integers must be positive!"""						)

       # Store the Integers
       _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		Union[str, Any]     	=					store_two_classics(lowerCAmelCase_			,  lowerCAmelCase_						)
       for i in range(len(lowerCAmelCase_						)						):
              full_adder(lowerCAmelCase_			,  lowerCAmelCase_			,  len(lowerCAmelCase_						) + i			,  len(lowerCAmelCase_						) + len(lowerCAmelCase_						) + i			,  len(lowerCAmelCase_						) + len(lowerCAmelCase_						) + i + 1						)
              circuit.barrier()  # Optional, just for aesthetics

       # Measure the resultant qBits
       for i in range(len(lowerCAmelCase_						) + 1						):
              circuit.measure([(len(lowerCAmelCase_						) * 2) + i]			,  [i]						)

       _UpperCAmelCase    :		Optional[int]     	=					qiskit.execute(lowerCAmelCase_			,  lowerCAmelCase_			,  shots=1						).result()

       # The result is in binary. Convert it back to int
       return int(list(res.get_counts()						)[0]			,  2						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



