




import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


snake_case_ : str    		=       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
    import pyspark







@dataclass
class __snake_case			(			datasets.BuilderConfig		):
   UpperCAmelCase__  :    Optional[Any]          =  None







def    A						(__A   :    "pyspark.sql.DataFrame"   ,				__A   :    List[int]   ,				)   ->      Optional[int]:







     """simple docstring"""


     import pyspark

     def generate_fn():
          UpperCAmelCase_          =							df.select('''*'''   ,				pyspark.sql.functions.spark_partition_id().alias('''part_id'''       )       )
          for partition_id in partition_order:
               UpperCAmelCase_          =							df_with_partition_id.select('''*'''       ).where(F"""part_id = {partition_id}"""       ).drop('''part_id'''       )
               UpperCAmelCase_          =							partition_df.collect()
               UpperCAmelCase_          =							0
               for row in rows:
                    yield F"""{partition_id}_{row_id}""", row.asDict()
                    row_id += 1

     return generate_fn







class __snake_case			(			_BaseExamplesIterable		):







   def __init__(      self		:						Optional[int]					,						_snake_case		:						"pyspark.sql.DataFrame"					,						_snake_case		:						List[Any]=None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							df
        UpperCAmelCase_          =							partition_order or range(self.df.rdd.getNumPartitions())
        UpperCAmelCase_          =							_generate_iterable_examples(self.df					,						self.partition_order)







   def __iter__(      self		:						Optional[int]):

        """simple docstring"""


        yield from self.generate_examples_fn()







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						np.random.Generator):

        """simple docstring"""


        UpperCAmelCase_          =							list(range(self.df.rdd.getNumPartitions()))
        generator.shuffle(_lowerCamelCase)
        return SparkExamplesIterable(self.df					,						partition_order=_lowerCamelCase)







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						int					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							self.split_shard_indices_by_worker(_lowerCamelCase					,						_lowerCamelCase)
        return SparkExamplesIterable(self.df					,						partition_order=_lowerCamelCase)







   @property
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        return len(self.partition_order)







class __snake_case			(			datasets.DatasetBuilder		):
   UpperCAmelCase__  :    Optional[Any]          =  SparkConfig







   def __init__(      self		:						str					,						_snake_case		:						"pyspark.sql.DataFrame"					,						_snake_case		:						str = None					,						_snake_case		:						str = None					,						**_snake_case		:						List[str]					,						):

        """simple docstring"""


        import pyspark

        UpperCAmelCase_          =							pyspark.sql.SparkSession.builder.getOrCreate()
        UpperCAmelCase_          =							df
        UpperCAmelCase_          =							working_dir

        super().__init__(
            cache_dir=_lowerCamelCase					,						config_name=str(self.df.semanticHash())					,						**_lowerCamelCase					,						)







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        def create_cache_and_write_probe(_snake_case		:						List[str]):
             # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
             # already exist.
             os.makedirs(self._cache_dir					,						exist_ok=_lowerCamelCase)
             UpperCAmelCase_          =							os.path.join(self._cache_dir					,						'''fs_test''' + uuid.uuida().hex)
             # Opening the file in append mode will create a new file unless it already exists, in which case it will not
             # change the file contents.
             open(_lowerCamelCase					,						'''a''')
             return [probe_file]

        if self._spark.conf.get('''spark.master'''					,						'''''').startswith('''local'''):
             return

        # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
        # accessible to the driver.
        # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
        if self._cache_dir:
             UpperCAmelCase_          =							(
                 self._spark.sparkContext.parallelize(range(1)					,						1).mapPartitions(_lowerCamelCase).collect()
             )
             if os.path.isfile(probe[0]):
                  return

        raise ValueError(
            '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''')







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        return datasets.DatasetInfo(features=self.config.features)







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						datasets.download.download_manager.DownloadManager):

        """simple docstring"""


        return [datasets.SplitGenerator(name=datasets.Split.TRAIN)]







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						List[Any]):

        """simple docstring"""


        import pyspark

        def get_arrow_batch_size(_snake_case		:						List[Any]):
             for batch in it:
                  yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]})

        UpperCAmelCase_          =							self.df.count()
        UpperCAmelCase_          =							df_num_rows if df_num_rows <= 100 else 100
        # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
        UpperCAmelCase_          =							(
            self.df.limit(_lowerCamelCase)
            .repartition(1)
            .mapInArrow(_lowerCamelCase					,						'''batch_bytes: long''')
            .agg(pyspark.sql.functions.sum('''batch_bytes''').alias('''sample_bytes'''))
            .collect()[0]
            .sample_bytes
            / sample_num_rows
        )
        UpperCAmelCase_          =							approx_bytes_per_row * df_num_rows
        if approx_total_size > max_shard_size:
             # Make sure there is at least one row per partition.
             UpperCAmelCase_          =							min(_lowerCamelCase					,						int(approx_total_size / max_shard_size))
             UpperCAmelCase_          =							self.df.repartition(_lowerCamelCase)







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						str					,						_snake_case		:						str					,						_snake_case		:						int					,						):

        """simple docstring"""


        import pyspark

        UpperCAmelCase_          =							ParquetWriter if file_format == '''parquet''' else ArrowWriter
        UpperCAmelCase_          =							os.path.join(self._working_dir					,						os.path.basename(_lowerCamelCase)) if self._working_dir else fpath
        UpperCAmelCase_          =							file_format == '''parquet'''

        # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
        # pickling the SparkContext.
        UpperCAmelCase_          =							self.config.features
        UpperCAmelCase_          =							self._writer_batch_size
        UpperCAmelCase_          =							self._fs.storage_options

        def write_arrow(_snake_case		:						Tuple):
             # Within the same SparkContext, no two task attempts will share the same attempt ID.
             UpperCAmelCase_          =							pyspark.TaskContext().taskAttemptId()
             UpperCAmelCase_          =							next(_lowerCamelCase					,						_lowerCamelCase)
             if first_batch is None:
                  # Some partitions might not receive any data.
                  return pa.RecordBatch.from_arrays(
                      [[task_id], [0], [0]]					,						names=['''task_id''', '''num_examples''', '''num_bytes''']					,						)
             UpperCAmelCase_          =							0
             UpperCAmelCase_          =							writer_class(
                 features=_lowerCamelCase					,						path=working_fpath.replace('''SSSSS'''					,						F"""{shard_id:05d}""").replace('''TTTTT'''					,						F"""{task_id:05d}""")					,						writer_batch_size=_lowerCamelCase					,						storage_options=_lowerCamelCase					,						embed_local_files=_lowerCamelCase					,						)
             UpperCAmelCase_          =							pa.Table.from_batches([first_batch])
             writer.write_table(_lowerCamelCase)
             for batch in it:
                  if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                       UpperCAmelCase_     ,       UpperCAmelCase_          =							writer.finalize()
                       writer.close()
                       yield pa.RecordBatch.from_arrays(
                           [[task_id], [num_examples], [num_bytes]]					,						names=['''task_id''', '''num_examples''', '''num_bytes''']					,						)
                       shard_id += 1
                       UpperCAmelCase_          =							writer_class(
                           features=writer._features					,						path=working_fpath.replace('''SSSSS'''					,						F"""{shard_id:05d}""").replace('''TTTTT'''					,						F"""{task_id:05d}""")					,						writer_batch_size=_lowerCamelCase					,						storage_options=_lowerCamelCase					,						embed_local_files=_lowerCamelCase					,						)
                  UpperCAmelCase_          =							pa.Table.from_batches([batch])
                  writer.write_table(_lowerCamelCase)

             if writer._num_bytes > 0:
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							writer.finalize()
                  writer.close()
                  yield pa.RecordBatch.from_arrays(
                      [[task_id], [num_examples], [num_bytes]]					,						names=['''task_id''', '''num_examples''', '''num_bytes''']					,						)

             if working_fpath != fpath:
                  for file in os.listdir(os.path.dirname(_lowerCamelCase)):
                       UpperCAmelCase_          =							os.path.join(os.path.dirname(_lowerCamelCase)					,						os.path.basename(_lowerCamelCase))
                       shutil.move(_lowerCamelCase					,						_lowerCamelCase)

        UpperCAmelCase_          =							(
            self.df.mapInArrow(_lowerCamelCase					,						'''task_id: long, num_examples: long, num_bytes: long''')
            .groupBy('''task_id''')
            .agg(
                pyspark.sql.functions.sum('''num_examples''').alias('''total_num_examples''')					,						pyspark.sql.functions.sum('''num_bytes''').alias('''total_num_bytes''')					,						pyspark.sql.functions.count('''num_bytes''').alias('''num_shards''')					,						pyspark.sql.functions.collect_list('''num_examples''').alias('''shard_lengths''')					,						)
            .collect()
        )
        for row in stats:
             yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						"datasets.SplitGenerator"					,						_snake_case		:						str = "arrow"					,						_snake_case		:						Optional[Union[str, int]] = None					,						_snake_case		:						Optional[int] = None					,						**_snake_case		:						List[Any]					,						):

        """simple docstring"""


        self._validate_cache_dir()

        UpperCAmelCase_          =							convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE)
        self._repartition_df_if_needed(_lowerCamelCase)
        UpperCAmelCase_          =							not is_remote_filesystem(self._fs)
        UpperCAmelCase_          =							os.path.join if is_local else posixpath.join

        UpperCAmelCase_          =							'''-TTTTT-SSSSS-of-NNNNN'''
        UpperCAmelCase_          =							F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
        UpperCAmelCase_          =							path_join(self._output_dir					,						_lowerCamelCase)

        UpperCAmelCase_          =							0
        UpperCAmelCase_          =							0
        UpperCAmelCase_          =							0
        UpperCAmelCase_          =							[]
        UpperCAmelCase_          =							[]

        for task_id, content in self._prepare_split_single(_lowerCamelCase					,						_lowerCamelCase					,						_lowerCamelCase):
             (
                 (
                 UpperCAmelCase_					
             )     ,       (
                 UpperCAmelCase_					
             )     ,       (
                 UpperCAmelCase_					
             )     ,       (
                 UpperCAmelCase_					
             )     ,       					
             )          =							content
             if num_bytes > 0:
                  total_num_examples += num_examples
                  total_num_bytes += num_bytes
                  total_shards += num_shards
                  task_id_and_num_shards.append((task_id, num_shards))
                  all_shard_lengths.extend(_lowerCamelCase)

        UpperCAmelCase_          =							total_num_examples
        UpperCAmelCase_          =							total_num_bytes

        # should rename everything at the end
        logger.debug(F"""Renaming {total_shards} shards.""")
        if total_shards > 1:
             UpperCAmelCase_          =							all_shard_lengths

             # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
             # pickling error due to pickling the SparkContext.
             UpperCAmelCase_          =							self._fs

             # use the -SSSSS-of-NNNNN pattern
             def _rename_shard(
                 _snake_case		:						int					,						_snake_case		:						int					,						_snake_case		:						int					,						):
                  rename(
                      _lowerCamelCase					,						fpath.replace('''SSSSS'''					,						F"""{shard_id:05d}""").replace('''TTTTT'''					,						F"""{task_id:05d}""")					,						fpath.replace('''TTTTT-SSSSS'''					,						F"""{global_shard_id:05d}""").replace('''NNNNN'''					,						F"""{total_shards:05d}""")					,						)

             UpperCAmelCase_          =							[]
             UpperCAmelCase_          =							0
             for i in range(len(_lowerCamelCase)):
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							task_id_and_num_shards[i]
                  for shard_id in range(_lowerCamelCase):
                       args.append([task_id, shard_id, global_shard_id])
                       global_shard_id += 1
             self._spark.sparkContext.parallelize(_lowerCamelCase					,						len(_lowerCamelCase)).map(lambda _snake_case: _rename_shard(*_lowerCamelCase)).collect()
        else:
             # don't use any pattern
             UpperCAmelCase_          =							0
             UpperCAmelCase_          =							task_id_and_num_shards[0][0]
             self._rename(
                 fpath.replace('''SSSSS'''					,						F"""{shard_id:05d}""").replace('''TTTTT'''					,						F"""{task_id:05d}""")					,						fpath.replace(_lowerCamelCase					,						'''''')					,						)







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						"datasets.SplitGenerator"					,						):

        """simple docstring"""


        return SparkExamplesIterable(self.df)








"""simple docstring"""



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 3		)     ->					qiskit.result.counts.Counts:
  if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
    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(__lowerCamelCase		) != 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).'''		)

  _snake_case					     =       QuantumRegister(__lowerCamelCase ,					'''qr'''		)
  _snake_case					     =       ClassicalRegister(__lowerCamelCase ,					'''cr'''		)

  _snake_case					     =       QuantumCircuit(__lowerCamelCase ,					__lowerCamelCase		)

  _snake_case					     =       number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(__lowerCamelCase ,					__lowerCamelCase		)
  # simulate with 10000 shots
  _snake_case					     =       Aer.get_backend('''qasm_simulator'''		)
  _snake_case					     =       execute(__lowerCamelCase ,					__lowerCamelCase ,					shots=1_00_00		)

  return job.result().get_counts(__lowerCamelCase		)


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



