import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def       _snake_case	(   lowerCAmelCase							:   str = 3	):
		"""simple docstring"""







		if isinstance(lowerCAmelCase__   , lowerCAmelCase__	):
				raise TypeError("number of qubits must be a integer."	)
		if number_of_qubits <= 0:
				raise ValueError("number of qubits must be > 0."	)
		if math.floor(lowerCAmelCase__	) != number_of_qubits:
				raise ValueError("number of qubits must be exact integer."	)
		if number_of_qubits > 1_0:
				raise ValueError("number of qubits too large to simulate(>10)."	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    QuantumRegister(lowerCAmelCase__   , "qr"	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ClassicalRegister(lowerCAmelCase__   , "cr"	)

		SCREAMING_SNAKE_CASE_  :		int				      =    QuantumCircuit(lowerCAmelCase__   , lowerCAmelCase__	)

		SCREAMING_SNAKE_CASE_  :		str				      =    number_of_qubits

		for i in range(lowerCAmelCase__	):
				quantum_circuit.h(number_of_qubits - i - 1	)
				counter -= 1
				for j in range(lowerCAmelCase__	):
						quantum_circuit.cp(np.pi / 2 ** (counter - j)   , lowerCAmelCase__   , lowerCAmelCase__	)

		for k in range(number_of_qubits // 2	):
				quantum_circuit.swap(lowerCAmelCase__   , number_of_qubits - k - 1	)

		# measure all the qubits
		quantum_circuit.measure(lowerCAmelCase__   , lowerCAmelCase__	)
		# simulate with 10000 shots
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    Aer.get_backend("qasm_simulator"	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    execute(lowerCAmelCase__   , lowerCAmelCase__   , shots=1_0_0_0_0	)

		return job.result().get_counts(lowerCAmelCase__	)


if __name__ == "__main__":
		print(
		    f'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
		)


import os
import sys

lowercase__		:Tuple   =    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,
)


lowercase__		:List[Any]   =    [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]



@add_start_docstrings(AutoConfig.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoConfig.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoTokenizer.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoTokenizer.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModel.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModel.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForCausalLM.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForMaskedLM.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForSequenceClassification.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForQuestionAnswering.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)




