
'''simple docstring'''





import qiskit

def      A (       A_							:					int = 2       ):
						snake_case       :     Dict =    qubits

						# Using Aer's simulator
						snake_case       :     str =    qiskit.Aer.get_backend('''aer_simulator'''       )

						# Creating a Quantum Circuit acting on the q register
						snake_case       :     int =    qiskit.QuantumCircuit(A_    ,							A_       )

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

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

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

						# 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
						snake_case       :     Union[str, Any] =    qiskit.execute(A_    ,							A_    ,							shots=1000       )

						return job.result().get_counts(A_       )


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



'''simple docstring'''





import os
import sys

UpperCAmelCase    					=   os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCAmelCase    					=   [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]

@add_start_docstrings(AutoConfig.__doc__       )
def      A (       *A_							:					Optional[Any]    ,							**A_							:					List[str]       ):
						return AutoConfig.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoTokenizer.__doc__       )
def      A (       *A_							:					Dict    ,							**A_							:					str       ):
						return AutoTokenizer.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModel.__doc__       )
def      A (       *A_							:					Union[str, Any]    ,							**A_							:					Optional[Any]       ):
						return AutoModel.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForCausalLM.__doc__       )
def      A (       *A_							:					str    ,							**A_							:					Optional[Any]       ):
						return AutoModelForCausalLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForMaskedLM.__doc__       )
def      A (       *A_							:					List[str]    ,							**A_							:					Optional[Any]       ):
						return AutoModelForMaskedLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForSequenceClassification.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					List[str]       ):
						return AutoModelForSequenceClassification.from_pretrained(*A_    ,							**A_       )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					Any       ):
						return AutoModelForQuestionAnswering.from_pretrained(*A_    ,							**A_       )

