


"""simple docstring"""


import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=()    , _UpperCamelCase=None    , _UpperCamelCase="no"    , _UpperCamelCase="29500"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			False
			__lowerCAmelCase				    =			False
			if any(key.startswith("KAGGLE"      ) for key in os.environ.keys()      ):
						__lowerCAmelCase				    =			True
			elif "IPython" in sys.modules:
						__lowerCAmelCase				    =			"google.colab" in str(sys.modules["IPython"].get_ipython()      )

			try:
						__lowerCAmelCase				    =			PrecisionType(mixed_precision.lower()      )
			except ValueError:
						raise ValueError(
						    f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."      )

			if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"    , _UpperCamelCase      ) is not None):
						# TPU launch
						import torch_xla.distributed.xla_multiprocessing as xmp

						if len(AcceleratorState._shared_state      ) > 0:
									raise ValueError(
									    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
									    "your training function. Restart your notebook and make sure no cells initializes an "
									    "`Accelerator`."      )
						if num_processes is None:
									__lowerCAmelCase				    =			8

						__lowerCAmelCase				    =			PrepareForLaunch(_UpperCamelCase    , distributed_type="TPU"      )
						print(f"Launching a training on {num_processes} TPU cores."      )
						xmp.spawn(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , start_method="fork"      )
			elif in_colab:
						# No need for a distributed launch otherwise as it's either CPU or one GPU.
						if torch.cuda.is_available():
									print("Launching training on one GPU."      )
						else:
									print("Launching training on one CPU."      )
						function(*_UpperCamelCase      )
			else:
						if num_processes is None:
									raise ValueError(
									    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."      )

						if num_processes > 1:
									# Multi-GPU launch
									from torch.multiprocessing import start_processes
									from torch.multiprocessing.spawn import ProcessRaisedException

									if len(AcceleratorState._shared_state      ) > 0:
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
												    "inside your training function. Restart your notebook and make sure no cells initializes an "
												    "`Accelerator`."      )

									if torch.cuda.is_initialized():
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
												    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
												    "function."      )

									# torch.distributed will expect a few environment variable to be here. We set the ones common to each
									# process here (the other ones will be set be the launcher).
									with patch_environment(
									    world_size=_UpperCamelCase    , master_addr="127.0.01"    , master_port=_UpperCamelCase    , mixed_precision=_UpperCamelCase      ):
												__lowerCAmelCase				    =			PrepareForLaunch(_UpperCamelCase    , distributed_type="MULTI_GPU"      )
												print(f"Launching training on {num_processes} GPUs."      )
												try:
															start_processes(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , start_method="fork"      )
												except ProcessRaisedException as e:
															if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																		raise RuntimeError(
																		    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
																		    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
																		    "Please review your imports and test them when running the `notebook_launcher()` to identify "
																		    "which one is problematic."      ) from e

						else:
									# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
									if is_mps_available():
												__lowerCAmelCase				    =			"1"
												print("Launching training on MPS."      )
									elif torch.cuda.is_available():
												print("Launching training on one GPU."      )
									else:
												print("Launching training on CPU."      )
									function(*_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=()    , _UpperCamelCase=2      ):

			'''simple docstring'''




			from torch.multiprocessing import start_processes

			with tempfile.NamedTemporaryFile() as tmp_file:
						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=_UpperCamelCase    , master_addr="127.0.01"    , master_port="29500"    , accelerate_mixed_precision="no"    , accelerate_debug_rdv_file=tmp_file.name    , accelerate_use_cpu="yes"    , ):
									__lowerCAmelCase				    =			PrepareForLaunch(_UpperCamelCase    , debug=_UpperCamelCase      )
									start_processes(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , start_method="fork"      )












import math

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int = 3					)		->			qiskit.result.counts.Counts:






	"""simple docstring"""



	if isinstance(__magic_name__	,			__magic_name__					):
		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(__magic_name__					) != number_of_qubits:
		raise ValueError("""number of qubits must be exact integer."""					)
	if number_of_qubits > 10:
		raise ValueError("""number of qubits too large to simulate(>10)."""					)

	UpperCamelCase     :int          =	QuantumRegister(__magic_name__	,			"""qr"""					)
	UpperCamelCase     :str          =	ClassicalRegister(__magic_name__	,			"""cr"""					)

	UpperCamelCase     :str          =	QuantumCircuit(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :List[Any]          =	number_of_qubits

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

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

	# measure all the qubits
	quantum_circuit.measure(__magic_name__	,			__magic_name__					)
	# simulate with 10000 shots
	UpperCamelCase     :str          =	Aer.get_backend("""qasm_simulator"""					)
	UpperCamelCase     :Dict          =	execute(__magic_name__	,			__magic_name__	,			shots=1_0000					)

	return job.result().get_counts(__magic_name__					)


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