



'''simple docstring'''






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


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



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    ["""input_values""", """attention_mask"""]







 def __init__(				self    ,a_=1    ,a_=16_000    ,a_=0.0    ,a_=False    ,a_=True    ,**a_    ,)						->  Dict:
        super().__init__(feature_size=a_    ,sampling_rate=a_    ,padding_value=a_    ,**a_				)
        _UpperCAmelCase    :		str     	=					return_attention_mask
        _UpperCAmelCase    :		Union[str, Any]     	=					do_normalize







 @staticmethod
 def _snake_case					(				a_    ,a_    ,a_ = 0.0				)						->  List[np.ndarray]:
        if attention_mask is not None:
               _UpperCAmelCase    :		Dict     	=					np.array(a_    ,np.intaa				)
               _UpperCAmelCase    :		Optional[int]     	=					[]

               for vector, length in zip(a_    ,attention_mask.sum(-1				)				):
                      _UpperCAmelCase    :		Dict     	=					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7				)
                      if length < normed_slice.shape[0]:
                             _UpperCAmelCase    :		Union[str, Any]     	=					padding_value

                      normed_input_values.append(a_				)
        else:
               _UpperCAmelCase    :		int     	=					[(x - x.mean()) / np.sqrt(x.var() + 1E-7				) for x in input_values]

        return normed_input_values







 def __call__(				self    ,a_    ,a_ = False    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,**a_    ,)						->  BatchFeature:

        if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                          f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                          f''' {self.sampling_rate} and not {sampling_rate}.'''				)
        else:
               logger.warning(
                   """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
                   """Failing to do so can result in silent errors that might be hard to debug."""				)

        _UpperCAmelCase    :		Union[str, Any]     	=					isinstance(a_    ,np.ndarray				) and len(raw_speech.shape				) > 1
        if is_batched_numpy and len(raw_speech.shape				) > 2:
               raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''				)
        _UpperCAmelCase    :		Optional[int]     	=					is_batched_numpy or (
            isinstance(a_    ,(list, tuple)				) and (isinstance(raw_speech[0]    ,(np.ndarray, tuple, list)				))
        )

        # always return batch
        if not is_batched:
               _UpperCAmelCase    :		List[Any]     	=					[raw_speech]

        # convert into correct format for padding
        _UpperCAmelCase    :		Union[str, Any]     	=					BatchFeature({"""input_values""": raw_speech}				)

        _UpperCAmelCase    :		str     	=					self.pad(
            a_    ,padding=a_    ,max_length=a_    ,truncation=a_    ,pad_to_multiple_of=a_    ,return_attention_mask=a_    ,)

        # convert input values to correct format
        _UpperCAmelCase    :		Any     	=					padded_inputs["""input_values"""]
        if not isinstance(input_values[0]    ,np.ndarray				):
               _UpperCAmelCase    :		str     	=					[np.asarray(a_    ,dtype=np.floataa				) for array in input_values]
        elif (
            not isinstance(a_    ,np.ndarray				)
            and isinstance(input_values[0]    ,np.ndarray				)
            and input_values[0].dtype is np.dtype(np.floataa				)
        ):
               _UpperCAmelCase    :		int     	=					[array.astype(np.floataa				) for array in input_values]
        elif isinstance(a_    ,np.ndarray				) and input_values.dtype is np.dtype(np.floataa				):
               _UpperCAmelCase    :		List[str]     	=					input_values.astype(np.floataa				)

        # convert attention_mask to correct format
        _UpperCAmelCase    :		Any     	=					padded_inputs.get("""attention_mask"""				)
        if attention_mask is not None:
               _UpperCAmelCase    :		Dict     	=					[np.asarray(a_    ,dtype=np.intaa				) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if self.do_normalize:
               _UpperCAmelCase    :		List[Any]     	=					(
                   attention_mask
                   if self._get_padding_strategies(a_    ,max_length=a_				) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               _UpperCAmelCase    :		Dict     	=					self.zero_mean_unit_var_norm(
                   padded_inputs["""input_values"""]    ,attention_mask=a_    ,padding_value=self.padding_value				)

        if return_tensors is not None:
               _UpperCAmelCase    :		Union[str, Any]     	=					padded_inputs.convert_to_tensors(a_				)

        return padded_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()



