





"""simple docstring"""




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 					_UpperCAmelCase    (  __snake_case						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = False   , snake_case_ = False   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(
            __UpperCamelCase   , split=__UpperCamelCase   , features=__UpperCamelCase   , cache_dir=__UpperCamelCase   , keep_in_memory=__UpperCamelCase   , streaming=__UpperCamelCase   , num_proc=__UpperCamelCase   , **__UpperCamelCase   , )
        A_    :			Optional[int]									=    path_or_paths if isinstance(__UpperCamelCase   , __UpperCamelCase    ) else {self.split: path_or_paths}
        A_    :			Any									=    Csv(
            cache_dir=__UpperCamelCase   , data_files=__UpperCamelCase   , features=__UpperCamelCase   , **__UpperCamelCase   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.streaming:
          A_    :			Dict									=    self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
          A_    :			Optional[Any]									=    None
          A_    :			Tuple									=    None
          A_    :			str									=    None
          A_    :			Optional[Any]									=    None

          self.builder.download_and_prepare(
              download_config=__UpperCamelCase   , download_mode=__UpperCamelCase   , verification_mode=__UpperCamelCase   , base_path=__UpperCamelCase   , num_proc=self.num_proc   , )
          A_    :			Optional[int]									=    self.builder.as_dataset(
              split=self.split   , verification_mode=__UpperCamelCase   , in_memory=self.keep_in_memory    )
        return dataset


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """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_    :			Any									=    dataset
        A_    :			List[str]									=    path_or_buf
        A_    :			List[Any]									=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        A_    :			Tuple									=    num_proc
        A_    :			Optional[Any]									=    'utf-8'
        A_    :			str									=    to_csv_kwargs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    self.to_csv_kwargs.pop('path_or_buf'   , __UpperCamelCase    )
        A_    :			Tuple									=    self.to_csv_kwargs.pop('header'   , __UpperCamelCase    )
        A_    :			Dict									=    self.to_csv_kwargs.pop('index'   , __UpperCamelCase    )

        if isinstance(self.path_or_buf   , (str, bytes, os.PathLike)    ):
          with open(self.path_or_buf   , 'wb+'    ) as buffer:
            A_    :			Any									=    self._write(file_obj=__UpperCamelCase   , header=__UpperCamelCase   , index=__UpperCamelCase   , **self.to_csv_kwargs    )
        else:
          A_    :			Dict									=    self._write(file_obj=self.path_or_buf   , header=__UpperCamelCase   , index=__UpperCamelCase   , **self.to_csv_kwargs    )
        return written

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_		,			A_		,			A_		,			A_    :			Dict									=    args

        A_    :			Dict									=    query_table(
            table=self.dataset.data   , key=slice(__UpperCamelCase   , offset + self.batch_size    )   , indices=self.dataset._indices   , )
        A_    :			Union[str, Any]									=    batch.to_pandas().to_csv(
            path_or_buf=__UpperCamelCase   , header=header if (offset == 0) else False   , index=__UpperCamelCase   , **__UpperCamelCase    )
        return csv_str.encode(self.encoding    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , **snake_case_    ):

        """simple docstring"""

        A_    :			List[Any]									=    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 CSV from Arrow format'   , ):
            A_    :			Union[str, Any]									=    self._batch_csv((offset, header, index, to_csv_kwargs)    )
            written += file_obj.write(__UpperCamelCase    )

        else:
          A_		,			A_    :			List[Any]									=    len(self.dataset    ), self.batch_size
          with multiprocessing.Pool(self.num_proc    ) as pool:
            for csv_str in logging.tqdm(
                pool.imap(
                    self._batch_csv   , [(offset, header, index, to_csv_kwargs) for offset in range(0   , __UpperCamelCase   , __UpperCamelCase    )]   , )   , 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 CSV from Arrow format'   , ):
              written += file_obj.write(__UpperCamelCase    )

        return written

import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def        lowerCAmelCase       (				snake_case__   :      int = 1				,					snake_case__   :      int = 1 )->			qiskit.result.counts.Counts:
    if isinstance(snake_case__				,					snake_case__ ) or isinstance(snake_case__				,					snake_case__ ):
        raise TypeError("inputs must be integers." )
    if (bit_a < 0) or (bit_a < 0):
        raise ValueError("inputs must be positive." )
    if (math.floor(snake_case__ ) != bit_a) or (math.floor(snake_case__ ) != bit_a):
        raise ValueError("inputs must be exact integers." )
    if (bit_a > 1) or (bit_a > 1):
        raise ValueError("inputs must be less or equal to 1." )

    # build registers
    A_								=					QuantumRegister(2				,					"qr" )
    A_								=					ClassicalRegister(2				,					"cr" )

    A_								=					QuantumCircuit(snake_case__				,					snake_case__ )

    # entanglement the qubits
    quantum_circuit.h(1 )
    quantum_circuit.cx(1				,					0 )

    # send the information
    A_								=					str(snake_case__ ) + str(snake_case__ )

    if c_information == "11":
        quantum_circuit.x(1 )
        quantum_circuit.z(1 )
    elif c_information == "10":
        quantum_circuit.z(1 )
    elif c_information == "01":
        quantum_circuit.x(1 )
    else:
        quantum_circuit.i(1 )

    # unentangled the circuit
    quantum_circuit.cx(1				,					0 )
    quantum_circuit.h(1 )

    # measure the circuit
    quantum_circuit.measure(snake_case__				,					snake_case__ )

    A_								=					Aer.get_backend("aer_simulator" )
    A_								=					execute(snake_case__				,					snake_case__				,					shots=1000 )

    return job.result().get_counts(snake_case__ )


if __name__ == "__main__":
     print(f"""Counts for classical state send: {superdense_coding(1,1)}""")






