
"""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)

lowerCamelCase__        =		logging.getLogger()
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Tuple						      =				{}
	__lowerCAmelCase      :						str						      =				os.path.join(_A	,  'all_results.json'		)
	if os.path.exists(_A		):
		with open(_A	,  'r'		) as f:
			__lowerCAmelCase      :						List[str]						      =				json.load(_A		)
	else:
		raise ValueError(F"can\'t find {path}"		)
	return results


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




@require_torch_tpu
class 			A__	(     lowerCAmelCase__):

		def 							__lowerCamelCase							(				self			):
			import xla_spawn

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

			with patch.object(_SCREAMING_SNAKE_CASE	,		'argv'	,		_SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase      :						Optional[Any]						      =				time()
				xla_spawn.main()
				__lowerCAmelCase      :						Optional[Any]						      =				time()

				__lowerCAmelCase      :						Union[str, Any]						      =				get_results(_SCREAMING_SNAKE_CASE			)
				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	,		5_00			)







		def 							__lowerCamelCase							(				self			):
			import xla_spawn

			__lowerCAmelCase      :						Optional[int]						      =				'\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
			with patch.object(_SCREAMING_SNAKE_CASE	,		'argv'	,		_SCREAMING_SNAKE_CASE			):
				xla_spawn.main()

"""simple docstring"""







import qiskit
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Union[str, Any]						      =				qiskit.Aer.get_backend('aer_simulator'		)

	# Create a Quantum Circuit acting on the q register
	__lowerCAmelCase      :						str						      =				qiskit.QuantumCircuit(_UpperCamelCase	,  _UpperCamelCase		)

	# Map the quantum measurement to the classical bits
	circuit.measure([0]	,  [0]		)

	# Execute the circuit on the simulator
	__lowerCAmelCase      :						Optional[int]						      =				qiskit.execute(_UpperCamelCase	,  _UpperCamelCase	,  shots=1000		)

	# Return the histogram data of the results of the experiment.
	return job.result().get_counts(_UpperCamelCase		)


if __name__ == "__main__":
							print(f'Total count for various states are: {single_qubit_measure(1, 1)}')