


'''simple docstring'''
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=['image_processor', 'tokenizer']
       __UpperCAmelCase					='BlipImageProcessor'
       __UpperCAmelCase					='AutoTokenizer'






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      Optional[Any]:
            _A				=							False
            super().__init__(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							self.image_processor






       def __call__( self			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = True			,     _UpperCamelCase = False			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = 0			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = False			,     _UpperCamelCase = False			,     _UpperCamelCase = False			,     _UpperCamelCase = False			,     _UpperCamelCase = False			,     _UpperCamelCase = True			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      BatchEncoding:
            if images is None and text is None:
                 raise ValueError('You have to specify either images or text.'   )

            # Get only text
            if images is None:
                 _A				=							self.tokenizer
                 _A				=							self.tokenizer(
                     text=_UpperCamelCase			,     add_special_tokens=_UpperCamelCase			,     padding=_UpperCamelCase			,     truncation=_UpperCamelCase			,     max_length=_UpperCamelCase			,     stride=_UpperCamelCase			,     pad_to_multiple_of=_UpperCamelCase			,     return_attention_mask=_UpperCamelCase			,     return_overflowing_tokens=_UpperCamelCase			,     return_special_tokens_mask=_UpperCamelCase			,     return_offsets_mapping=_UpperCamelCase			,     return_token_type_ids=_UpperCamelCase			,     return_length=_UpperCamelCase			,     verbose=_UpperCamelCase			,     return_tensors=_UpperCamelCase			,     **_UpperCamelCase			,     )
                 return text_encoding

            # add pixel_values
            _A				=							self.image_processor(_UpperCamelCase			,     return_tensors=_UpperCamelCase   )

            if text is not None:
                 _A				=							self.tokenizer(
                     text=_UpperCamelCase			,     add_special_tokens=_UpperCamelCase			,     padding=_UpperCamelCase			,     truncation=_UpperCamelCase			,     max_length=_UpperCamelCase			,     stride=_UpperCamelCase			,     pad_to_multiple_of=_UpperCamelCase			,     return_attention_mask=_UpperCamelCase			,     return_overflowing_tokens=_UpperCamelCase			,     return_special_tokens_mask=_UpperCamelCase			,     return_offsets_mapping=_UpperCamelCase			,     return_token_type_ids=_UpperCamelCase			,     return_length=_UpperCamelCase			,     verbose=_UpperCamelCase			,     return_tensors=_UpperCamelCase			,     **_UpperCamelCase			,     )
            else:
                 _A				=							None

            if text_encoding is not None:
                 encoding_image_processor.update(_UpperCamelCase   )

            return encoding_image_processor






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      int:
            return self.tokenizer.batch_decode(*_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      List[str]:
            return self.tokenizer.decode(*_UpperCamelCase			,     **_UpperCamelCase   )






       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							self.tokenizer.model_input_names
            _A				=							self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









'''simple docstring'''
import math

import qiskit

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1		)					->	qiskit.result.counts.Counts:







     """simple docstring"""
     if (
         isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
     ):
          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(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != 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
     _A				=							qiskit.QuantumRegister(4							,    'qr'		)
     _A				=							qiskit.ClassicalRegister(2							,    'cr'		)
     # list the entries
     _A				=							[input_a, input_a, carry_in]

     _A				=							qiskit.QuantumCircuit(__UpperCamelCase							,    __UpperCamelCase		)

     for i in range(0							,    3		):
          if entry[i] == 2:
               quantum_circuit.h(__UpperCamelCase		)  # for hadamard entries
          elif entry[i] == 1:
               quantum_circuit.x(__UpperCamelCase		)  # for 1 entries
          elif entry[i] == 0:
               quantum_circuit.i(__UpperCamelCase		)  # 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]							,    __UpperCamelCase		)  # measure the last two qbits

     _A				=							qiskit.Aer.get_backend('aer_simulator'		)
     _A				=							qiskit.execute(__UpperCamelCase							,    __UpperCamelCase							,    shots=1_0_0_0		)

     return job.result().get_counts(__UpperCamelCase		)


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





