
"""simple docstring"""

import qiskit
from qiskit.providers import Backend




def       lowercase__  (   lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   Optional[Any]	)	-> str:






    """simple docstring"""




    UpperCAmelCase       =   bin(SCREAMING_SNAKE_CASE_	)[2:], bin(SCREAMING_SNAKE_CASE_	)[2:]  # Remove leading '0b'

    # Ensure that both strings are of the same length
    if len(SCREAMING_SNAKE_CASE_	) > len(SCREAMING_SNAKE_CASE_	):
        UpperCAmelCase       =   y.zfill(len(SCREAMING_SNAKE_CASE_	)	)
    else:
        UpperCAmelCase       =   x.zfill(len(SCREAMING_SNAKE_CASE_	)	)

    # 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       =   qiskit.QuantumCircuit((len(SCREAMING_SNAKE_CASE_	) * 3) + 1	,			len(SCREAMING_SNAKE_CASE_	) + 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(SCREAMING_SNAKE_CASE_	)	):
        if x[::-1][i] == "1":
            circuit.x(SCREAMING_SNAKE_CASE_	)
    for j in range(len(SCREAMING_SNAKE_CASE_	)	):
        if y[::-1][j] == "1":
            circuit.x(len(SCREAMING_SNAKE_CASE_	) + j	)

    return circuit, x, y




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Any	,			)	-> Optional[int]:






    """simple docstring"""




    circuit.ccx(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)
    circuit.cx(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)
    circuit.ccx(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)
    circuit.cx(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)
    circuit.cx(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   str	,			lowerCAmelCase			:   Tuple = qiskit.Aer.get_backend('aer_simulator'	)	,			)	-> Union[str, Any]:






    """simple docstring"""




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

    # Store the Integers
    UpperCAmelCase       =   store_two_classics(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)
    for i in range(len(SCREAMING_SNAKE_CASE_	)	):
        full_adder(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	,			len(SCREAMING_SNAKE_CASE_	) + i	,			len(SCREAMING_SNAKE_CASE_	) + len(SCREAMING_SNAKE_CASE_	) + i	,			len(SCREAMING_SNAKE_CASE_	) + len(SCREAMING_SNAKE_CASE_	) + i + 1	)
        circuit.barrier()  # Optional, just for aesthetics

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

    UpperCAmelCase       =   qiskit.execute(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	,			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()








"""simple docstring"""

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):




      @register_to_config
      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = False				,    )  ->   List[Any]:
          super().__init__()

          UpperCAmelCase       =   nn.Linear(lowercase_				,    lowercase_				,    bias=lowercase_							)

          UpperCAmelCase       =   nn.Embedding(lowercase_				,    lowercase_							)
          UpperCAmelCase       =   False

          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)

          UpperCAmelCase       =   TaConfig(
              d_model=lowercase_				,    num_heads=lowercase_				,    d_kv=lowercase_				,    d_ff=lowercase_				,    feed_forward_proj=lowercase_				,    dropout_rate=lowercase_				,    is_decoder=lowercase_				,    is_encoder_decoder=lowercase_				,    )
          UpperCAmelCase       =   nn.ModuleList()
          for lyr_num in range(lowercase_							):
              UpperCAmelCase       =   TaBlock(lowercase_							)
              self.encoders.append(lowercase_							)

          UpperCAmelCase       =   TaLayerNorm(lowercase_							)
          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   self.input_proj(lowercase_							)

          # terminal relative positional encodings
          UpperCAmelCase       =   encoder_inputs.shape[1]
          UpperCAmelCase       =   torch.arange(lowercase_				,    device=encoder_inputs.device							)

          UpperCAmelCase       =   encoder_inputs_mask.sum(-1							)
          UpperCAmelCase       =   torch.roll(input_positions.unsqueeze(0							)				,    tuple(seq_lens.tolist()							)				,    dims=0							)
          x += self.position_encoding(lowercase_							)

          UpperCAmelCase       =   self.dropout_pre(lowercase_							)

          # inverted the attention mask
          UpperCAmelCase       =   encoder_inputs.size()
          UpperCAmelCase       =   self.get_extended_attention_mask(lowercase_				,    lowercase_							)

          for lyr in self.encoders:
              UpperCAmelCase       =   lyr(lowercase_				,    lowercase_							)[0]
          UpperCAmelCase       =   self.layer_norm(lowercase_							)

          return self.dropout_post(lowercase_							), encoder_inputs_mask



