



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase__     = logging.getLogger()





def 							A			(  )       ->				Any:







			'''simple docstring'''
			_UpperCAmelCase	 =     argparse.ArgumentParser()
			parser.add_argument('-f'					)
			_UpperCAmelCase	 =     parser.parse_args()
			return args.f







class     __lowerCAmelCase					(    A		):



			def 					_lowerCamelCase (		self     :						Any)     ->					None:



						"""simple docstring"""






						_UpperCAmelCase	 =     logging.StreamHandler(sys.stdout)
						logger.addHandler(A)



			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						Optional[int])     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_gpu_count()

						if n_gpu > 1:
									pass
									# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
									# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
									# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
									# cmd = [sys.executable] + distributed_args + args
									# execute_subprocess_async(cmd, env=self.get_env())
									# XXX: test the results - need to save them first into .json file
						else:
									args.insert(0				,	'run_glue_deebert.py')
									with patch.object(A				,	'argv'				,	A):
												_UpperCAmelCase	 =     run_glue_deebert.main()
												for value in result.values():
															self.assertGreaterEqual(A				,	0.6_6_6)



			@slow
			@require_torch_non_multi_gpu
			def 					_lowerCamelCase (		self     :						int)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     '\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
						self.run_and_check(A)

						_UpperCAmelCase	 =     '\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
						self.run_and_check(A)

						_UpperCAmelCase	 =     '\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
						self.run_and_check(A)





import qiskit





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				qiskit.result.counts.Counts:







			'''simple docstring'''
			_UpperCAmelCase	 =     qiskit.Aer.get_backend('aer_simulator'					)

			# Create a Quantum Circuit acting on the q register
			_UpperCAmelCase	 =     qiskit.QuantumCircuit(_UpperCAmelCase							,     _UpperCAmelCase					)

			# Apply X (NOT) Gate to Qubits 0 & 1
			circuit.x(0					)
			circuit.x(1					)

			# Map the quantum measurement to the classical bits
			circuit.measure([0, 1]							,     [0, 1]					)

			# Execute the circuit on the qasm simulator
			_UpperCAmelCase	 =     qiskit.execute(_UpperCAmelCase							,     _UpperCAmelCase							,     shots=1_000					)

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


if __name__ == "__main__":
							UpperCAmelCase__     = single_qubit_measure(2, 2)
							print(f"""Total count for various states are: {counts}""")
