


from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__UpperCamelCase				:  List[str]      					=							logging.get_logger(__name__)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    def __init__(	self				:   Union[str, Any]       ,   __snake_case				:   int       ,   __snake_case				:   int       ,   __snake_case				:   float       ,   **__snake_case				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   feature_size
       UpperCAmelCase_							:      Any						=   sampling_rate
       UpperCAmelCase_							:      Optional[Any]						=   padding_value

       UpperCAmelCase_							:      List[Any]						=   kwargs.pop('''padding_side'''       ,   '''right'''	)
       UpperCAmelCase_							:      Optional[Any]						=   kwargs.pop('''return_attention_mask'''       ,   __snake_case	)

       super().__init__(**__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Union[
            BatchFeature,
            List[BatchFeature],
            Dict[str, BatchFeature],
            Dict[str, List[BatchFeature]],
            List[Dict[str, BatchFeature]],
        ]       ,   __snake_case				:   Union[bool, str, PaddingStrategy] = True       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   bool = False       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   Optional[bool] = None       ,   __snake_case				:   Optional[Union[str, TensorType]] = None       ,   ):





       '''simple docstring'''
       # If we have a list of dicts, let's convert it in a dict of lists
       # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
       if isinstance(__snake_case       ,   (list, tuple)	) and isinstance(processed_features[0]       ,   (dict, BatchFeature)	):
          UpperCAmelCase_							:      List[Any]						=   {
              key: [example[key] for example in processed_features] for key in processed_features[0].keys()
          }

       # The model's main input name, usually `input_values`, has be passed for padding
       if self.model_input_names[0] not in processed_features:
          raise ValueError(
              '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
              f''' to this method that includes {self.model_input_names[0]}, but you provided'''
              f''' {list(processed_features.keys()	)}'''	)

       UpperCAmelCase_							:      Union[str, Any]						=   processed_features[self.model_input_names[0]]
       UpperCAmelCase_							:      List[str]						=   (
           return_attention_mask if return_attention_mask is not None else self.return_attention_mask
       )

       if len(__snake_case	) == 0:
          if return_attention_mask:
             UpperCAmelCase_							:      Any						=   []
          return processed_features

       # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
       # and rebuild them afterwards if no return_tensors is specified
       # Note that we lose the specific device the tensor may be on for PyTorch

       UpperCAmelCase_							:      List[Any]						=   required_input[0]
       if isinstance(__snake_case       ,   (list, tuple)	):
          # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
          UpperCAmelCase_							:      Optional[int]						=   0
          while len(required_input[index]	) == 0:
             index += 1
          if index < len(__snake_case	):
             UpperCAmelCase_							:      List[Any]						=   required_input[index][0]

       if return_tensors is None:
          if is_tf_tensor(__snake_case	):
             UpperCAmelCase_							:      List[Any]						=   '''tf'''
          elif is_torch_tensor(__snake_case	):
             UpperCAmelCase_							:      Optional[Any]						=   '''pt'''
          elif isinstance(__snake_case       ,   (int, float, list, tuple, np.ndarray)	):
             UpperCAmelCase_							:      str						=   '''np'''
          else:
             raise ValueError(
                 f'''type of {first_element} unknown: {type(__snake_case	)}. '''
                 '''Should be one of a python, numpy, pytorch or tensorflow object.'''	)

       for key, value in processed_features.items():
          if isinstance(value[0]       ,   (int, float)	):
             UpperCAmelCase_							:      List[Any]						=   to_numpy(__snake_case	)
          else:
             UpperCAmelCase_							:      Union[str, Any]						=   [to_numpy(__snake_case	) for v in value]

        # Convert padding_strategy in PaddingStrategy
       UpperCAmelCase_							:      Any						=   self._get_padding_strategies(padding=__snake_case       ,   max_length=__snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   processed_features[self.model_input_names[0]]

       UpperCAmelCase_							:      Any						=   len(__snake_case	)
       if not all(len(__snake_case	) == batch_size for v in processed_features.values()	):
          raise ValueError('''Some items in the output dictionary have a different batch size than others.'''	)

       UpperCAmelCase_							:      int						=   []
       for i in range(__snake_case	):
          UpperCAmelCase_							:      List[Any]						=   {k: v[i] for k, v in processed_features.items()}
          # truncation
          UpperCAmelCase_							:      Tuple						=   self._truncate(
              __snake_case       ,   max_length=__snake_case       ,   pad_to_multiple_of=__snake_case       ,   truncation=__snake_case       ,   )
          truncated_inputs.append(__snake_case	)

       if padding_strategy == PaddingStrategy.LONGEST:
          # make sure that `max_length` cannot be longer than the longest truncated length
          UpperCAmelCase_							:      List[str]						=   max(len(input_slice[self.model_input_names[0]]	) for input_slice in truncated_inputs	)
          UpperCAmelCase_							:      Any						=   PaddingStrategy.MAX_LENGTH

       UpperCAmelCase_							:      Optional[Any]						=   {}
       for i in range(__snake_case	):
          # padding
          UpperCAmelCase_							:      Dict						=   self._pad(
              truncated_inputs[i]       ,   max_length=__snake_case       ,   padding_strategy=__snake_case       ,   pad_to_multiple_of=__snake_case       ,   return_attention_mask=__snake_case       ,   )

          for key, value in outputs.items():
             if key not in batch_outputs:
                UpperCAmelCase_							:      Optional[Any]						=   []
             if value.dtype is np.dtype(np.floataa	):
                UpperCAmelCase_							:      Dict						=   value.astype(np.floataa	)
             batch_outputs[key].append(__snake_case	)

       return BatchFeature(__snake_case       ,   tensor_type=__snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   Union[Dict[str, np.ndarray], BatchFeature]       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   Optional[bool] = None       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   processed_features[self.model_input_names[0]]

       if padding_strategy == PaddingStrategy.LONGEST:
          UpperCAmelCase_							:      List[Any]						=   len(__snake_case	)

       if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
          UpperCAmelCase_							:      Union[str, Any]						=   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       UpperCAmelCase_							:      Tuple						=   padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case	) < max_length

       if return_attention_mask and "attention_mask" not in processed_features:
          UpperCAmelCase_							:      str						=   np.ones(len(__snake_case	)       ,   dtype=np.intaa	)

       if needs_to_be_padded:
          UpperCAmelCase_							:      Dict						=   max_length - len(__snake_case	)
          if self.padding_side == "right":
             if return_attention_mask:
                UpperCAmelCase_							:      str						=   np.pad(
                    processed_features['''attention_mask''']       ,   (0, difference)	)
             UpperCAmelCase_							:      Optional[Any]						=   ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
             UpperCAmelCase_							:      Optional[int]						=   np.pad(
                 __snake_case       ,   __snake_case       ,   '''constant'''       ,   constant_values=self.padding_value	)
          elif self.padding_side == "left":
             if return_attention_mask:
                UpperCAmelCase_							:      List[str]						=   np.pad(
                    processed_features['''attention_mask''']       ,   (difference, 0)	)
             UpperCAmelCase_							:      List[str]						=   ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
             UpperCAmelCase_							:      int						=   np.pad(
                 __snake_case       ,   __snake_case       ,   '''constant'''       ,   constant_values=self.padding_value	)
          else:
             raise ValueError('''Invalid padding strategy:''' + str(self.padding_side	)	)

       return processed_features





    def 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   Union[Dict[str, np.ndarray], BatchFeature]       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   Optional[bool] = None       ,   ):





       '''simple docstring'''
       if not truncation:
          return processed_features
       elif truncation and max_length is None:
          raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''	)

       UpperCAmelCase_							:      List[Any]						=   processed_features[self.model_input_names[0]]

       # find `max_length` that fits `pad_to_multiple_of`
       if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
          UpperCAmelCase_							:      str						=   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       UpperCAmelCase_							:      Union[str, Any]						=   len(__snake_case	) > max_length

       if needs_to_be_truncated:
          UpperCAmelCase_							:      int						=   processed_features[self.model_input_names[0]][:max_length]
          if "attention_mask" in processed_features:
             UpperCAmelCase_							:      Any						=   processed_features['''attention_mask'''][:max_length]

       return processed_features





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Dict=False       ,   __snake_case				:   Union[str, Any]=None	):





       '''simple docstring'''

       # Get padding strategy
       if padding is not False:
          if padding is True:
             UpperCAmelCase_							:      int						=   PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
          elif not isinstance(__snake_case       ,   __snake_case	):
             UpperCAmelCase_							:      str						=   PaddingStrategy(__snake_case	)
          elif isinstance(__snake_case       ,   __snake_case	):
             UpperCAmelCase_							:      List[Any]						=   padding
       else:
          UpperCAmelCase_							:      List[str]						=   PaddingStrategy.DO_NOT_PAD

       # Set max length if needed
       if max_length is None:
          if padding_strategy == PaddingStrategy.MAX_LENGTH:
             raise ValueError(
                 f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''	)

        # Test if we have a padding value
       if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
          raise ValueError(
              '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
              ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''	)

       return padding_strategy



import math

import qiskit
def      snake_case_						( __lowercase = 1			,       __lowercase = 1			,       __lowercase = 1       ):
   if (
       isinstance(__lowercase			,       __lowercase       )
       or isinstance(__lowercase			,       __lowercase       )
       or isinstance(__lowercase			,       __lowercase       )
   ):
      raise TypeError('''inputs must be integers.'''       )

   if (input_a < 0) or (input_a < 0) or (carry_in < 0):
      raise ValueError('''inputs must be positive.'''       )

   if (
       (math.floor(__lowercase       ) != input_a)
       or (math.floor(__lowercase       ) != input_a)
       or (math.floor(__lowercase       ) != carry_in)
   ):
      raise ValueError('''inputs must be exact integers.'''       )

   if (input_a > 2) or (input_a > 2) or (carry_in > 2):
      raise ValueError('''inputs must be less or equal to 2.'''       )

   # build registers
   UpperCAmelCase_							:      Any						=   qiskit.QuantumRegister(4			,       '''qr'''       )
   UpperCAmelCase_							:      List[str]						=   qiskit.ClassicalRegister(2			,       '''cr'''       )
   # list the entries
   UpperCAmelCase_							:      Any						=   [input_a, input_a, carry_in]

   UpperCAmelCase_							:      Dict						=   qiskit.QuantumCircuit(__lowercase			,       __lowercase       )

   for i in range(0			,       3       ):
      if entry[i] == 2:
         quantum_circuit.h(__lowercase       )  # for hadamard entries
      elif entry[i] == 1:
         quantum_circuit.x(__lowercase       )  # for 1 entries
      elif entry[i] == 0:
         quantum_circuit.i(__lowercase       )  # for 0 entries

    # build the circuit
   quantum_circuit.ccx(0			,       1			,       3       )  # ccx = toffoli gate
   quantum_circuit.cx(0			,       1       )
   quantum_circuit.ccx(1			,       2			,       3       )
   quantum_circuit.cx(1			,       2       )
   quantum_circuit.cx(0			,       1       )

   quantum_circuit.measure([2, 3]			,       __lowercase       )  # measure the last two qbits

   UpperCAmelCase_							:      Optional[int]						=   qiskit.Aer.get_backend('''aer_simulator'''       )
   UpperCAmelCase_							:      List[str]						=   qiskit.execute(__lowercase			,       __lowercase			,       shots=1_0_0_0       )

   return job.result().get_counts(__lowercase       )


if __name__ == "__main__":
       print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')