

import qiskit




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 2			)->						qiskit.result.counts.Counts:



    """simple docstring"""






    UpperCamelCase_			  =						qubits

    # Using Aer's simulator
    UpperCamelCase_			  =						qiskit.Aer.get_backend("aer_simulator"			)

    # Creating a Quantum Circuit acting on the q register
    UpperCamelCase_			  =						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    # Adding a H gate on qubit 0 (now q0 in superposition)
    circuit.h(0			)

    for i in range(1			,						SCREAMING_SNAKE_CASE_			):
        # Adding CX (CNOT) gate
        circuit.cx(i - 1			,						SCREAMING_SNAKE_CASE_			)

    # Mapping the quantum measurement to the classical bits
    circuit.measure(list(range(SCREAMING_SNAKE_CASE_			)			)			,						list(range(SCREAMING_SNAKE_CASE_			)			)			)

    # Now measuring any one qubit would affect other qubits to collapse
    # their super position and have same state as the measured one.

    # Executing the circuit on the simulator
    UpperCamelCase_			  =						qiskit.execute(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						shots=1_0_0_0			)

    return job.result().get_counts(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 print(F'''Total count for various states are: {quantum_entanglement(3)}''')



from typing import List, Optional, Union

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






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
