

'''simple docstring'''


import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase__										= 4
lowerCAmelCase__										= 3



class 					lowercase						(  a_ ):
							pass







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Dict:
	for shard in shards:
		for i in range(_lowerCamelCase  ):
			yield {"i": i, "shard": shard}







def 						SCREAMING_SNAKE_CASE(  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Optional[int]       	=					int(os.environ['RANK']  )
	UpperCAmelCase_		:      List[Any]       	=					int(os.environ['WORLD_SIZE']  )

	UpperCAmelCase_		:      Any       	=					ArgumentParser()
	parser.add_argument('--streaming'   ,type=_lowerCamelCase  )
	parser.add_argument('--local_rank'   ,type=_lowerCamelCase  )
	parser.add_argument('--num_workers'   ,type=_lowerCamelCase   ,default=0  )
	UpperCAmelCase_		:      int       	=					parser.parse_args()
	UpperCAmelCase_		:      Optional[int]       	=					args.streaming
	UpperCAmelCase_		:      Union[str, Any]       	=					args.num_workers

	UpperCAmelCase_		:      Dict       	=					{'shards': [f"""shard_{shard_idx}""" for shard_idx in range(_lowerCamelCase  )]}
	UpperCAmelCase_		:      Optional[int]       	=					IterableDataset.from_generator(_lowerCamelCase   ,gen_kwargs=_lowerCamelCase  )
	if not streaming:
		UpperCAmelCase_		:      Any       	=					Dataset.from_list(list(_lowerCamelCase  )  )

	UpperCAmelCase_		:      str       	=					split_dataset_by_node(_lowerCamelCase   ,rank=_lowerCamelCase   ,world_size=_lowerCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					torch.utils.data.DataLoader(_lowerCamelCase   ,num_workers=_lowerCamelCase  )

	UpperCAmelCase_		:      List[Any]       	=					NUM_SHARDS * NUM_ITEMS_PER_SHARD
	UpperCAmelCase_		:      Union[str, Any]       	=					full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)  )

	UpperCAmelCase_		:      Tuple       	=					sum(1 for _ in dataloader  )
	if local_size != expected_local_size:
		raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""  )


if __name__ == "__main__":
						main()









'''simple docstring'''


import qiskit







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    qiskit.result.counts.Counts:
	UpperCAmelCase_		:      Tuple       	=					qiskit.Aer.get_backend('aer_simulator'  )

	UpperCAmelCase_		:      List[str]       	=					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
	UpperCAmelCase_		:      List[str]       	=					qiskit.execute(UpperCamelCase   ,UpperCamelCase   ,shots=1_0_0_0  )

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


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






