



import qiskit




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Tuple              =						qiskit.Aer.get_backend("aer_simulator"    )

			A							:					Optional[Any]              =						qiskit.QuantumCircuit(4 ,    2    )
			# encode inputs in qubits 0 and 1
			if bita == 1:
						qc_ha.x(0    )
			if bita == 1:
						qc_ha.x(1    )
			qc_ha.barrier()

			# use cnots to write XOR of the inputs on qubit2
			qc_ha.cx(0 ,    2    )
			qc_ha.cx(1 ,    2    )

			# use ccx / toffoli gate to write AND of the inputs on qubit3
			qc_ha.ccx(0 ,    1 ,    3    )
			qc_ha.barrier()

			# extract outputs
			qc_ha.measure(2 ,    0    )  # extract XOR value
			qc_ha.measure(3 ,    1    )  # extract AND value

			# Execute the circuit on the qasm simulator
			A							:					Tuple              =						qiskit.execute(_lowercase ,    _lowercase ,    shots=1000    )

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


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  half_adder(1, 1)
							print(F"""Half Adder Output Qubit Counts: {counts}""")




import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__SCREAMING_SNAKE_CASE           =  os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
							from run_translation import main  # noqa


set_seed(42)
__SCREAMING_SNAKE_CASE           =  """sshleifer/student_marian_en_ro_6_1"""
__SCREAMING_SNAKE_CASE           =  """sshleifer/tiny-mbart"""

@require_torch
class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any]=False	,	__lowerCamelCase      :			str=None	,	__lowerCamelCase      :			List[str]=True	,	__lowerCamelCase      :			str=True	,	__lowerCamelCase      :			List[Any]=True	,	__lowerCamelCase      :			Optional[int]=True	,	)      ->				Dict:
									A							:					str              =						self.run_trainer(
									    eval_steps=1	,	max_len=12	,	model_name=__lowerCamelCase	,	num_train_epochs=1	,	distributed=__lowerCamelCase	,	extra_args_str=__lowerCamelCase	,	predict_with_generate=__lowerCamelCase	,	do_train=__lowerCamelCase	,	do_eval=__lowerCamelCase	,	do_predict=__lowerCamelCase	,	)
									A							:					Dict              =						TrainerState.load_from_json(os.path.join(__lowerCamelCase	,	"trainer_state.json" ) ).log_history

									if not do_eval:
												return

									A							:					List[Any]              =						[log for log in logs if "eval_loss" in log.keys()]

									A							:					Any              =						eval_metrics[0]
									if predict_with_generate:
												assert "eval_bleu" in first_step_stats

												A							:					List[str]              =						eval_metrics[-1]
												assert isinstance(last_step_stats["eval_bleu"]	,	__lowerCamelCase )
												assert not math.isnan(float(last_step_stats["eval_loss"] ) ), "eval_loss must not be `nan`"







						@require_torch_non_multi_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									self.run_seqaseq_quick()







						@require_torch_multi_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									self.run_seqaseq_quick(distributed=__lowerCamelCase )







						@require_torch_multi_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				str:
									self.run_seqaseq_quick(distributed=__lowerCamelCase )







						@unittest.skip("Requires an update of the env running those tests" )
						@require_torch_multi_gpu
						@require_fairscale
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									self.run_seqaseq_quick(distributed=__lowerCamelCase	,	extra_args_str="--sharded_ddp simple" )







						@unittest.skip("Requires an update of the env running those tests" )
						@require_torch_multi_gpu
						@require_fairscale
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				str:
									self.run_seqaseq_quick(distributed=__lowerCamelCase	,	extra_args_str="--sharded_ddp simple --fp16" )







						@unittest.skip("Requires an update of the env running those tests" )
						@require_torch_multi_gpu
						@require_fairscale
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									self.run_seqaseq_quick(distributed=__lowerCamelCase	,	extra_args_str="--sharded_ddp zero_dp_2"	,	predict_with_generate=__lowerCamelCase )







						@unittest.skip("Requires an update of the env running those tests" )
						@require_torch_multi_gpu
						@require_fairscale
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[str]:
									self.run_seqaseq_quick(
									    distributed=__lowerCamelCase	,	extra_args_str="--sharded_ddp zero_dp_2 --fp16"	,	predict_with_generate=__lowerCamelCase )







						@require_apex
						@require_torch_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Dict:
									# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
									# program and it breaks other tests that run from the same pytest worker, therefore until this is
									# sorted out it must be run only in an external program, that is distributed=True in this
									# test and only under one or more gpus - if we want cpu will need to make a special test
									#
									# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
									# 2nd main() call it botches the future eval.
									#
									self.run_seqaseq_quick(distributed=__lowerCamelCase	,	extra_args_str="--fp16 --fp16_backend=apex" )
									# test 2nd time - was getting eval_loss': nan'
									# to reproduce the problem set distributed=False
									self.run_seqaseq_quick(distributed=__lowerCamelCase	,	extra_args_str="--fp16 --fp16_backend=apex" )







						@parameterized.expand(["base", "low", "high", "mixed"] )
						@require_torch_multi_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			List[str] )      ->				Tuple:
									# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
									A							:					Dict              =						{
									    # test with the default log_level - should be info and thus log info once
									    "base": {"extra_args_str": "", "n_matches": 1},
									    # test with low log_level and log_level_replica - should be noisy on all processes
									    # now the info string should appear twice on 2 processes
									    "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
									    # test with high log_level and low log_level_replica
									    # now the info string should appear once only on the replica
									    "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
									    # test with high log_level and log_level_replica - should be quiet on all processes
									    "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
									}

									A							:					List[str]              =						experiments[experiment_id]
									A							:					Union[str, Any]              =						{"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
									A							:					Union[str, Any]              =						"Running training"
									with CaptureStderr() as cl:
												self.run_seqaseq_quick(**__lowerCamelCase	,	extra_args_str=data["extra_args_str"] )
									A							:					Dict              =						len(re.findall(__lowerCamelCase	,	cl.err ) )
									self.assertEqual(__lowerCamelCase	,	data["n_matches"] )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[int]:
									A							:					int              =						self.run_trainer(
									    eval_steps=2	,	max_len=1_28	,	model_name=__lowerCamelCase	,	learning_rate=3e-4	,	num_train_epochs=10	,	distributed=__lowerCamelCase	,	)

									# Check metrics
									A							:					str              =						TrainerState.load_from_json(os.path.join(__lowerCamelCase	,	"trainer_state.json" ) ).log_history
									A							:					Dict              =						[log for log in logs if "eval_loss" in log.keys()]
									A							:					Dict              =						eval_metrics[0]
									A							:					int              =						eval_metrics[-1]

									assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
									assert isinstance(last_step_stats["eval_bleu"]	,	__lowerCamelCase )

									# test if do_predict saves generations and metrics
									A							:					Optional[Any]              =						os.listdir(__lowerCamelCase )
									A							:					Any              =						{os.path.basename(__lowerCamelCase ) for p in contents}
									assert "generated_predictions.txt" in contents
									assert "predict_results.json" in contents







						@slow
						@require_bitsandbytes
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				List[str]:
									from transformers.training_args import OptimizerNames

									def train_and_return_metrics(__lowerCamelCase      :			str ) -> Tuple[int, float]:
												A							:					Optional[int]              =						"--skip_memory_metrics 0"

												A							:					str              =						self.run_trainer(
												    max_len=1_28	,	model_name=__lowerCamelCase	,	learning_rate=3e-4	,	num_train_epochs=1	,	optim=__lowerCamelCase	,	distributed=__lowerCamelCase	,	extra_args_str=__lowerCamelCase	,	do_eval=__lowerCamelCase	,	do_predict=__lowerCamelCase	,	n_gpus_to_use=1	,	)

												# Check metrics
												A							:					Union[str, Any]              =						TrainerState.load_from_json(Path(__lowerCamelCase	,	"trainer_state.json" ) ).log_history
												A							:					str              =						int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20 )
												A							:					List[Any]              =						int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20 )

												A							:					int              =						logs[0]["train_loss"]
												return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

									A       , A       , A							:					int              =						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
									A       , A       , A							:					Optional[int]              =						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )

									A							:					Tuple              =						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

									A							:					Dict              =						gpu_peak_mem_orig + gpu_alloc_mem_orig
									A							:					Dict              =						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
									A							:					int              =						gpu_total_mem_orig - gpu_total_mem_bnb

									# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
									# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
									# in 2 bytes and the diff in optim memory usage is derived as so:
									#
									# - normal 25*8=~200MB (8 bytes per param)
									# - bnb    25*2= ~50MB (2 bytes per param)
									#
									# Thus we should expect ~150MB total memory saved.
									#
									# Peak memory should be the same - the total should be different by about that same margin
									#
									# After leaving a small margin to accommodate for differences between gpus let's check
									# that we have at least 120MB in savings
									A							:					Tuple              =						1_20

									# uncomment the following if this test starts failing - requires py38 for a new print feature
									# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
									# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
									# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
									# print(f"{gpu_alloc_mem_diff=}MB")
									# print(f"{gpu_peak_mem_diff=}MB")
									# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
									# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

									self.assertGreater(
									    __lowerCamelCase	,	__lowerCamelCase	,	"should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
									    F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
									    F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"""	,	)

									self.assertGreater(
									    __lowerCamelCase	,	__lowerCamelCase	,	"should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
									    F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
									    F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"""	,	)

									self.assertEqual(
									    __lowerCamelCase	,	__lowerCamelCase	,	F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			float = 3e-3	,	__lowerCamelCase      :			str = "adafactor"	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			str = None	,	__lowerCamelCase      :			int = 0	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			int = None	,	)      ->				List[str]:
									A							:					Optional[int]              =						self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
									A							:					Optional[int]              =						self.get_auto_remove_tmp_dir()
									A							:					int              =						F"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__lowerCamelCase )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCamelCase )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

									A							:					Optional[Any]              =						F"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__lowerCamelCase )}
        """.split()

									A							:					Optional[Any]              =						"\n            --do_predict\n        ".split()

									A							:					Optional[int]              =						[]
									if do_train:
												args += args_train

									if do_eval:
												args += args_eval

									if do_predict:
												args += args_predict

									if predict_with_generate:
												args += "--predict_with_generate".split()

									if do_train:
												if optim == "adafactor":
															args += "--adafactor".split()
												else:
															args += F"""--optim {optim}""".split()

									if extra_args_str is not None:
												args += extra_args_str.split()

									if distributed:
												if n_gpus_to_use is None:
															A							:					Dict              =						get_gpu_count()
												A							:					Any              =						get_torch_dist_unique_port()
												A							:					Optional[Any]              =						F"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
												A							:					Any              =						[sys.executable] + distributed_args + args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(__lowerCamelCase	,	env=self.get_env() )
									else:
												A							:					List[Any]              =						["run_translation.py"] + args
												with patch.object(__lowerCamelCase	,	"argv"	,	__lowerCamelCase ):
															main()

									return output_dir