


'''simple docstring'''
import qiskit


def     a   (    __a = 2     )						->			qiskit.result.counts.Counts:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				qubits

					# Using Aer's simulator
					UpperCamelCase__     :Any							      =				qiskit.Aer.get_backend('''aer_simulator'''     )

					# Creating a Quantum Circuit acting on the q register
					UpperCamelCase__     :List[str]							      =				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
					UpperCamelCase__     :List[str]							      =				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 json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

__snake_case							   =  logging.getLogger()


def     a   (    __a     )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :str							      =				{}
					UpperCamelCase__     :Dict							      =				os.path.join(__a				,      '''all_results.json'''     )
					if os.path.exists(__a     ):
										with open(__a				,      '''r'''     ) as f:
															UpperCamelCase__     :int							      =				json.load(__a     )
					else:
										raise ValueError(f'''can\'t find {path}'''     )
					return results


__snake_case							   =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class      lowercase			(							A__      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							import xla_spawn

							UpperCamelCase__     :Any							      =				self.get_auto_remove_tmp_dir()
							UpperCamelCase__     :List[str]							      =				F'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

							with patch.object(UpperCamelCase_					,     '''argv'''					,     UpperCamelCase_							):
												UpperCamelCase__     :List[Any]							      =				time()
												xla_spawn.main()
												UpperCamelCase__     :List[Any]							      =				time()

												UpperCamelCase__     :Tuple							      =				get_results(UpperCamelCase_							)
												self.assertGreaterEqual(result['''eval_accuracy''']					,     0.75							)

												# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
												self.assertLess(end - start					,     500							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							import xla_spawn

							UpperCamelCase__     :Tuple							      =				'''
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            '''.split()
							with patch.object(UpperCamelCase_					,     '''argv'''					,     UpperCamelCase_							):
												xla_spawn.main()