



import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
 import sqlitea

 import sqlalchemy

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, "sqlalchemy.sql.Selectable"]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Features] = None     ,		__SCREAMING_SNAKE_CASE							:     str = None     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		**__SCREAMING_SNAKE_CASE							:     List[str]     ,		):


           '''simple docstring'''

           super().__init__(features=__SCREAMING_SNAKE_CASE     ,		cache_dir=__SCREAMING_SNAKE_CASE     ,		keep_in_memory=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				Sql(
               cache_dir=__SCREAMING_SNAKE_CASE     ,		features=__SCREAMING_SNAKE_CASE     ,		sql=__SCREAMING_SNAKE_CASE     ,		con=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				None
           __a						       =				None
           __a						       =				None
           __a						       =				None

           self.builder.download_and_prepare(
               download_config=__SCREAMING_SNAKE_CASE     ,		download_mode=__SCREAMING_SNAKE_CASE     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		base_path=__SCREAMING_SNAKE_CASE     ,		)

           # Build dataset for splits
           __a						       =				self.builder.as_dataset(
               split='''train'''     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		in_memory=self.keep_in_memory)
           return dataset


class     _A       :



       def __init__(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dataset     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		):


           '''simple docstring'''

           if num_proc is not None and num_proc <= 0:
               raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

           __a						       =				dataset
           __a						       =				name
           __a						       =				con
           __a						       =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           __a						       =				num_proc
           __a						       =				to_sql_kwargs



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				self.to_sql_kwargs.pop('''sql'''     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				self.to_sql_kwargs.pop('''con'''     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				self.to_sql_kwargs.pop('''index'''     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				self._write(index=__SCREAMING_SNAKE_CASE     ,		**self.to_sql_kwargs)
           return written



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a       ,   __a       ,   __a						       =				args
           __a						       =				{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
           __a						       =				query_table(
               table=self.dataset.data     ,		key=slice(__SCREAMING_SNAKE_CASE     ,		offset + self.batch_size)     ,		indices=self.dataset._indices     ,		)
           __a						       =				batch.to_pandas()
           __a						       =				df.to_sql(self.name     ,		self.con     ,		index=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           return num_rows or len(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           __a						       =				0

           if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0     ,		len(self.dataset)     ,		self.batch_size)     ,		unit='''ba'''     ,		disable=not logging.is_progress_bar_enabled()     ,		desc='''Creating SQL from Arrow format'''     ,		):
                   written += self._batch_sql((offset, index, to_sql_kwargs))
           else:
               __a       ,   __a						       =				len(self.dataset), self.batch_size
               with multiprocessing.Pool(self.num_proc) as pool:
                   for num_rows in logging.tqdm(
                       pool.imap(
                           self._batch_sql     ,		[(offset, index, to_sql_kwargs) for offset in range(0     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)]     ,		)     ,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,		unit='''ba'''     ,		disable=not logging.is_progress_bar_enabled()     ,		desc='''Creating SQL from Arrow format'''     ,		):
                       written += num_rows

           return written











import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def __UpperCamelCase			(       _A = 3  ):
						if isinstance(_A ,  _A  ):
												raise TypeError('''number of qubits must be a integer.'''  )
						if number_of_qubits <= 0:
												raise ValueError('''number of qubits must be > 0.'''  )
						if math.floor(_A  ) != number_of_qubits:
												raise ValueError('''number of qubits must be exact integer.'''  )
						if number_of_qubits > 10:
												raise ValueError('''number of qubits too large to simulate(>10).'''  )

						lowerCAmelCase_      			=  QuantumRegister(_A ,  '''qr'''  )
						lowerCAmelCase_      			=  ClassicalRegister(_A ,  '''cr'''  )

						lowerCAmelCase_      			=  QuantumCircuit(_A ,  _A  )

						lowerCAmelCase_      			=  number_of_qubits

						for i in range(_A  ):
												quantum_circuit.h(number_of_qubits - i - 1  )
												counter -= 1
												for j in range(_A  ):
																		quantum_circuit.cp(np.pi / 2 ** (counter - j) ,  _A ,  _A  )

						for k in range(number_of_qubits // 2  ):
												quantum_circuit.swap(_A ,  number_of_qubits - k - 1  )

						# measure all the qubits
						quantum_circuit.measure(_A ,  _A  )
						# simulate with 10000 shots
						lowerCAmelCase_      			=  Aer.get_backend('''qasm_simulator'''  )
						lowerCAmelCase_      			=  execute(_A ,  _A ,  shots=10000  )

						return job.result().get_counts(_A  )


if __name__ == "__main__":
						print(
						    f"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
						)




