
import qiskit
from qiskit.providers import Backend




def 						A_			(				snake_case				: int			,   snake_case				: int			)   ->     tuple[qiskit.QuantumCircuit, str, str]:



						'''simple docstring'''

						__UpperCamelCase	,				__UpperCamelCase  	=      bin(snake_case			)[2:], bin(snake_case			)[2:]  # Remove leading '0b'

						# Ensure that both strings are of the same length
						if len(snake_case			) > len(snake_case			):
												__UpperCamelCase  	=      y.zfill(len(snake_case			)			)
						else:
												__UpperCamelCase  	=      x.zfill(len(snake_case			)			)

						# We need (3 * number of bits in the larger number)+1 qBits
						# The second parameter is the number of classical registers, to measure the result
						__UpperCamelCase  	=      qiskit.QuantumCircuit((len(snake_case			) * 3) + 1			,   len(snake_case			) + 1			)

						# We are essentially "not-ing" the bits that are 1
						# Reversed because it's easier to perform ops on more significant bits
						for i in range(len(snake_case			)			):
												if x[::-1][i] == "1":
																		circuit.x(snake_case			)
						for j in range(len(snake_case			)			):
												if y[::-1][j] == "1":
																		circuit.x(len(snake_case			) + j			)

						return circuit, x, y




def 						A_			(				snake_case				: qiskit.QuantumCircuit			,   snake_case				: int			,   snake_case				: int			,   snake_case				: int			,   snake_case				: int			,   )   ->     List[Any]:



						'''simple docstring'''

						circuit.ccx(snake_case			,   snake_case			,   snake_case			)
						circuit.cx(snake_case			,   snake_case			)
						circuit.ccx(snake_case			,   snake_case			,   snake_case			)
						circuit.cx(snake_case			,   snake_case			)
						circuit.cx(snake_case			,   snake_case			)




def 						A_			(				snake_case				: int			,   snake_case				: int			,   snake_case				: Backend = qiskit.Aer.get_backend('''aer_simulator'''			)			,   )   ->     int:



						'''simple docstring'''

						if vala < 0 or vala < 0:
												raise ValueError('''Both Integers must be positive!'''			)

						# Store the Integers
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      store_two_classics(snake_case			,   snake_case			)
						for i in range(len(snake_case			)			):
												full_adder(snake_case			,   snake_case			,   len(snake_case			) + i			,   len(snake_case			) + len(snake_case			) + i			,   len(snake_case			) + len(snake_case			) + i + 1			)
												circuit.barrier()  # Optional, just for aesthetics

						# Measure the resultant qBits
						for i in range(len(snake_case			) + 1			):
												circuit.measure([(len(snake_case			) * 2) + i]			,   [i]			)

						__UpperCamelCase  	=      qiskit.execute(snake_case			,   snake_case			,   shots=1			).result()

						# The result is in binary. Convert it back to int
						return int(list(res.get_counts()			)[0]			,   2			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()








"""simple docstring"""






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def        A_					(					UpperCAmelCase__				)     ->     str:
   a   :	Optional[int]						=    min(re.search('^ +'       ,   UpperCAmelCase__				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
   return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(UpperCAmelCase__				)]				)


SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

SCREAMING_SNAKE_CASE__				:  List[str]						= "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


SCREAMING_SNAKE_CASE__				:  Dict						= "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Any:
     with tempfile.TemporaryDirectory() as tmp_dir:
        a   :	Optional[Any]						=    Path(__UpperCAmelCase						) / 'README.md'
        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	Optional[Any]						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{}						)

        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertEqual(__UpperCAmelCase  ,					{}						)






  def 							lowercase_     (						self						)				->					Tuple:
     a   :	List[str]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
     assert DatasetMetadata.from_yaml_string(__UpperCAmelCase						)

     a   :	Optional[Any]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
     with self.assertRaises(__UpperCAmelCase						):
        DatasetMetadata.from_yaml_string(__UpperCAmelCase						)

     a   :	List[Any]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
     assert DatasetMetadata.from_yaml_string(__UpperCAmelCase						)





