


import qiskit






def 					UpperCamelCase   (      __magic_name__	:				int = 2	)  ->    qiskit.result.counts.Counts:
		"""simple docstring"""




		lowercase__	     =						qubits

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

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

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

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

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

		# 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
		lowercase__	     =						qiskit.execute(__magic_name__       ,       __magic_name__       ,       shots=1000	)

		return job.result().get_counts(__magic_name__	)


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







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						gather(SCREAMING_SNAKE_CASE )
			assert gathered_tensor.tolist() == list(range(1       ,    state.num_processes**2 + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[state.process_index]
			SCREAMING_SNAKE_CASE_								=						gather_object(SCREAMING_SNAKE_CASE )
			assert len(SCREAMING_SNAKE_CASE ) == state.num_processes, F'{gathered_obj}, {len(SCREAMING_SNAKE_CASE )} != {state.num_processes}'
			assert gathered_obj == list(range(state.num_processes ) ), F'{gathered_obj} != {list(range(state.num_processes ) )}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						broadcast(SCREAMING_SNAKE_CASE )
			assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
			assert broadcasted_tensor.tolist() == list(range(1       ,    state.num_processes + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if state.is_main_process:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes + 1 ).to(state.device )
			else:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes ).to(state.device )
			SCREAMING_SNAKE_CASE_								=						pad_across_processes(SCREAMING_SNAKE_CASE )
			assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
			if not state.is_main_process:
						assert padded_tensor.tolist() == list(range(0       ,    state.num_processes ) ) + [0]

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'sum' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([4.0, 6] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'mean' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([2.0, 3] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			main()

def lowercase    (  )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						PartialState()
			state.print(F'State: {state}' )
			state.print('testing gather' )
			test_gather(SCREAMING_SNAKE_CASE )
			state.print('testing gather_object' )
			test_gather_object(SCREAMING_SNAKE_CASE )
			state.print('testing broadcast' )
			test_broadcast(SCREAMING_SNAKE_CASE )
			state.print('testing pad_across_processes' )
			test_pad_across_processes(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_sum' )
			test_reduce_sum(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_mean' )
			test_reduce_mean(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				main()
